141 results on '"Guo, Yi-Ke"'
Search Results
2. Brain metastasis, EGFR mutation subtype and generation of EGFR-TKI jointly influence the treatment outcome of patient with EGFR-mutant NSCLC
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Ju, Jia-Shiuan, Huang, Allen Chung-Cheng, Tung, Pi-Hung, Huang, Chi-Hsien, Chiu, Tzu-Hsuan, Wang, Chin-Chou, Ko, How-Wen, Chung, Fu-Tsai, Hsu, Ping-Chih, Fang, Yueh-Fu, Guo, Yi-Ke, Kuo, Chih-Hsi Scott, and Yang, Cheng-Ta
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- 2023
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3. A reduced order with data assimilation model: Theory and practice
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Arcucci, Rossella, Xiao, Dunhui, Fang, Fangxin, Navon, Ionel Michael, Wu, Pin, Pain, Christopher C., and Guo, Yi-Ke
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- 2023
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4. Data-driven surrogate model with latent data assimilation: Application to wildfire forecasting
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Cheng, Sibo, Prentice, I. Colin, Huang, Yuhan, Jin, Yufang, Guo, Yi-Ke, and Arcucci, Rossella
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- 2022
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5. Generalised Latent Assimilation in Heterogeneous Reduced Spaces with Machine Learning Surrogate Models
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Cheng, Sibo, Chen, Jianhua, Anastasiou, Charitos, Angeli, Panagiota, Matar, Omar K., Guo, Yi-Ke, Pain, Christopher C., and Arcucci, Rossella
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- 2023
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6. The different overall survival between single-agent EGFR-TKI treatment and with bevacizumab in non-small cell lung cancer patients with brain metastasis
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Chiu, Tzu-Hsuan, Tung, Pi-Hung, Huang, Chi-Hsien, Ju, Jia-Shiuan, Huang, Allen Chung-Cheng, Wang, Chin-Chou, Ko, Ho-Wen, Hsu, Ping-Chih, Fang, Yueh-Fu, Guo, Yi-Ke, Kuo, Chih-Hsi Scott, and Yang, Cheng-Ta
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- 2022
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7. Attention-based Convolutional Autoencoders for 3D-Variational Data Assimilation
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Mack, Julian, Arcucci, Rossella, Molina-Solana, Miguel, and Guo, Yi-Ke
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- 2020
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8. Variational Gaussian Process for Optimal Sensor Placement
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Tajnafoi, Gabor, Arcucci, Rossella, Mottet, Laetitia, Vouriot, Carolanne, Molina-Solana, Miguel, Pain, Christopher, and Guo, Yi-Ke
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- 2021
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9. A Reduced Order Deep Data Assimilation model
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Casas, César Quilodrán, Arcucci, Rossella, Wu, Pin, Pain, Christopher, and Guo, Yi-Ke
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- 2020
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10. A case study on understanding energy consumption through prediction and visualization (VIMOEN)
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Ruiz, L.G.B., Pegalajar, M.C., Molina-Solana, M., and Guo, Yi-Ke
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- 2020
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11. A retrospective study of alectinib versus ceritinib in patients with advanced non–small-cell lung cancer of anaplastic lymphoma kinase fusion in whom crizotinib treatment failed
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Kuo, Chih-Hsi Scott, Tung, Pi-Hung, Huang, Allen Chung-Cheng, Wang, Chin-Chou, Chang, John Wen-Cheng, Liu, Chien-Ying, Chung, Fu-Tsai, Fang, Yueh-Fu, Guo, Yi-Ke, and Yang, Cheng-Ta
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- 2021
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12. Enhancing CFD-LES air pollution prediction accuracy using data assimilation
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Aristodemou, Elsa, Arcucci, Rossella, Mottet, Laetitia, Robins, Alan, Pain, Christopher, and Guo, Yi-Ke
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- 2019
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13. Optimal reduced space for Variational Data Assimilation
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Arcucci, Rossella, Mottet, Laetitia, Pain, Christopher, and Guo, Yi-Ke
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- 2019
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14. Crowdsourcing with online quantitative design analysis
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Birch, David, Simondetti, Alvise, and Guo, Yi-ke
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- 2018
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15. Prior EGFR-TKI Treatment in EGFR-Mutated NSCLC Affects the Allele Frequency Fraction of Acquired T790M and the Subsequent Efficacy of Osimertinib
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Kuo, Chih-Hsi Scott, Huang, Chi-Hsien, Liu, Chien-Ying, Pavlidis, Stelios, Ko, Ho-Wen, Chung, Fu-Tsai, Lin, Tin-Yu, Wang, Chih-Liang, Guo, Yi-Ke, and Yang, Cheng-Ta
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- 2019
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16. Improving data exploration in graphs with fuzzy logic and large-scale visualisation
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Molina-Solana, Miguel, Birch, David, and Guo, Yi-ke
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- 2017
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17. Differential prognostic value of tumor and plasma T790M mutations in EGFR TKI-treated advanced NSCLC.
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Tung, Pi-Hung, Chiu, Tzu-Hsuan, Huang, Allen Chung-Cheng, Ju, Jia-Shiuan, Huang, Chi-Hsien, Wang, Chin-Chou, Ko, How-Wen, Chung, Fu-Tsai, Hsu, Ping-Chih, Fang, Yueh-Fu, Guo, Yi-Ke, Kuo, Chih-Hsi Scott, and Yang, Cheng-Ta
- Abstract
Background: Substitution of methionine for threonine at codon 790 (T790M) of epidermal growth factor receptor (EGFR) represents the major mechanism of resistance to EGFR tyrosine kinase inhibitors (TKIs) in EGFR -mutant non-small-cell lung cancer. We determined the prognostic impact and association of secondary T790M mutations with the outcomes of osimertinib and chemotherapy. Methods: Patients (n = 460) progressing from first-line EGFR-TKI treatment were assessed. Tissue and/or liquid biopsies were used to determine T790M status; post-progression overall survival (OS) was analyzed. Results: Overall, 143 (31.1%) patients were T790M positive, 95 (20.7%) were T790M negative, and 222 (48.2%) had unknown T790M status. T790M status [T790M positive versus T790M negative: hazard ratio (HR) 0.48 (95% confidence interval (CI), 0.32–0.70); p < 0.001, T790M unknown versus T790M negative: HR 1.97 (95% CI, 1.47–2.64); p < 0.001] was significantly associated with post-progression OS. T790M positivity rates were similar for tissue (90/168, 53.6%) and liquid (53/90, 58.9%) biopsies (Fisher's exact test, p = 0.433). Tumor T790M-positive patients had significantly longer post-progression OS than tumor T790M-negative patients (34.1 versus 17.1 months; log-rank test, p = 8 × 10
−5 ). Post-progression OS was similar between plasma T790M-positive and -negative patients (17.4 versus not reached; log-rank test, p = 0.600). In tumor T790M-positive patients, post-progression OS was similar after osimertinib and chemotherapy [34.1 versus 29.1 months; log-rank test, p = 0.900; HR 1.06 (95% CI, 0.44–2.57); p = 0.897]. Conclusion: T790M positivity predicts better post-progression OS than T790M negativity; tumor T790M positivity has a stronger prognostic impact than plasma T790M positivity. Osimertinib and chemotherapy provide similar OS benefits in patients with T790M-positive tumors. Plain language summary: Different prognostic meaning of tumor resistant gene detected from tumor or blood in patients with EGFR-mutant lung cancer The study demonstrates that patients with EGFR-mutant lung cancer who develop resistance due to a secondary T790M mutation, defined by tumor or blood T790M positivity, achieve better survival than patients without secondary T790M mutation; this association was mainly contributed by tumour T790M positivity. Oismertinib and chemotherapy led to similar survival in tumour T790M-positive patients. However, compared to osimertinib, chemotherapy was associated with longer survival in blood T790M-positive patients. [ABSTRACT FROM AUTHOR]- Published
- 2024
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18. Inferring functional connectivity in fMRI using minimum partial correlation
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Nie, Lei, Yang, Xian, Matthews, Paul M., Xu, Zhi-Wei, and Guo, Yi-Ke
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- 2017
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19. Macrophage migration inhibitory factor promotes glucocorticoid resistance of neutrophilic inflammation in a murine model of severe asthma.
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Rama Raju Allam, Venkata Sita, Pavlidis, Stelios, Gang Liu, Kermani, Nazanin Zounemat, Simpson, Jennifer, To, Joyce, Donnelly, Sheila, Yi-Ke Guo, Hansbro, Philip M., Phipps, Simon, Morand, Eric F., Djukanovic, Ratko, Sterk, Peter, Kian Fan Chung, Adcock, Ian, Harris, James, Sukkar, Maria B., Allam, Venkata Sita Rama Raju, Liu, Gang, and Guo, Yi-Ke
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MACROPHAGE migration inhibitory factor ,ASTHMA ,WHEEZE ,ECZEMA ,GLUCOCORTICOIDS ,NLRP3 protein ,CHRONIC obstructive pulmonary disease - Abstract
Background: Severe neutrophilic asthma is resistant to treatment with glucocorticoids. The immunomodulatory protein macrophage migration inhibitory factor (MIF) promotes neutrophil recruitment to the lung and antagonises responses to glucocorticoids. We hypothesised that MIF promotes glucocorticoid resistance of neutrophilic inflammation in severe asthma.Methods: We examined whether sputum MIF protein correlated with clinical and molecular characteristics of severe neutrophilic asthma in the Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes (U-BIOPRED) cohort. We also investigated whether MIF regulates neutrophilic inflammation and glucocorticoid responsiveness in a murine model of severe asthma in vivo.Results: MIF protein levels positively correlated with the number of exacerbations in the previous year, sputum neutrophils and oral corticosteroid use across all U-BIOPRED subjects. Further analysis of MIF protein expression according to U-BIOPRED-defined transcriptomic-associated clusters (TACs) revealed increased MIF protein and a corresponding decrease in annexin-A1 protein in TAC2, which is most closely associated with airway neutrophilia and NLRP3 inflammasome activation. In a murine model of severe asthma, treatment with the MIF antagonist ISO-1 significantly inhibited neutrophilic inflammation and increased glucocorticoid responsiveness. Coimmunoprecipitation studies using lung tissue lysates demonstrated that MIF directly interacts with and cleaves annexin-A1, potentially reducing its biological activity.Conclusion: Our data suggest that MIF promotes glucocorticoid-resistance of neutrophilic inflammation by reducing the biological activity of annexin-A1, a potent glucocorticoid-regulated protein that inhibits neutrophil accumulation at sites of inflammation. This represents a previously unrecognised role for MIF in the regulation of inflammation and points to MIF as a potential therapeutic target for the management of severe neutrophilic asthma. [ABSTRACT FROM AUTHOR]- Published
- 2023
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20. Analyzing drop coalescence in microfluidic devices with a deep learning generative model.
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Zhu, Kewei, Cheng, Sibo, Kovalchuk, Nina, Simmons, Mark, Guo, Yi-Ke, Matar, Omar K., and Arcucci, Rossella
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Predicting drop coalescence based on process parameters is crucial for experimental design in chemical engineering. However, predictive models can suffer from the lack of training data and more importantly, the label imbalance problem. In this study, we propose the use of deep learning generative models to tackle this bottleneck by training the predictive models using generated synthetic data. A novel generative model, named double space conditional variational autoencoder (DSCVAE) is developed for labelled tabular data. By introducing label constraints in both the latent and the original space, DSCVAE is capable of generating consistent and realistic samples compared to the standard conditional variational autoencoder (CVAE). Two predictive models, namely random forest and gradient boosting classifiers, are enhanced on synthetic data and their performances are evaluated based on real experimental data. Numerical results show that a considerable improvement in prediction accuracy can be achieved by using synthetic data and the proposed DSCVAE clearly outperforms the standard CVAE. This research clearly provides more insights into handling imbalanced data for classification problems, especially in chemical engineering. [ABSTRACT FROM AUTHOR]
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- 2023
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21. A computational framework for complex disease stratification from multiple large-scale datasets
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De Meulder, Bertrand, Lefaudeux, Diane, Bansal, Aruna T., Mazein, Alexander, Chaiboonchoe, Amphun, Ahmed, Hassan, Balaur, Irina, Saqi, Mansoor, Pellet, Johann, Ballereau, Stéphane, Lemonnier, Nathanaël, Sun, Kai, Pandis, Ioannis, Yang, Xian, Batuwitage, Manohara, Kretsos, Kosmas, van Eyll, Jonathan, Bedding, Alun, Davison, Timothy, Dodson, Paul, Larminie, Christopher, Postle, Anthony, Corfield, Julie, Djukanovic, Ratko, Chung, Kian Fan, Adcock, Ian M., Guo, Yi-Ke, Sterk, Peter J., Manta, Alexander, Rowe, Anthony, Baribaud, Frédéric, Auffray, Charles, and the U-BIOPRED Study Group and the eTRIKS Consortium
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- 2018
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22. TKGQA Dataset: Using Question Answering to Guide and Validate the Evolution of Temporal Knowledge Graph.
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Ong, Ryan, Sun, Jiahao, Șerban, Ovidiu, and Guo, Yi-Ke
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KNOWLEDGE graphs ,MERGERS & acquisitions - Abstract
Temporal knowledge graphs can be used to represent the current state of the world and, as daily events happen, the need to update the temporal knowledge graph, in order to stay consistent with the state of the world, becomes very important. However, there is currently no reliable method to accurately validate the update and evolution of knowledge graphs. There has been a recent development in text summarisation, whereby question answering is used to both guide and fact-check summarisation quality. The exact process can be applied to the temporal knowledge graph update process. To the best of our knowledge, there is currently no dataset that connects temporal knowledge graphs with documents with question–answer pairs. In this paper, we proposed the TKGQA dataset, consisting of over 5000 financial news documents related to M&A. Each document has extracted facts, question–answer pairs, and before and after temporal knowledge graphs, to highlight the state of temporal knowledge and any changes caused by the facts extracted from the document. As we parse through each document, we use question–answering to check and guide the update process of the temporal knowledge graph. Dataset: https://doi.org/10.17605/OSF.IO/XQWA4 Dataset License: CC BY 4.0 [ABSTRACT FROM AUTHOR]
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- 2023
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23. Neural Assimilation
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Arcucci, Rossella, Moutiq, Lamya, and Guo, Yi-Ke
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Machine learning ,Data Assimilation ,Article ,Neural network - Abstract
We introduce a new neural network for Data Assimilation (DA). DA is the approximation of the true state of some physical system at a given time obtained combining time-distributed observations with a dynamic model in an optimal way. The typical assimilation scheme is made up of two major steps: a prediction and a correction of the prediction by including information provided by observed data. This is the so called prediction-correction cycle. Classical methods for DA include Kalman filter (KF). KF can provide a rich information structure about the solution but it is often complex and time-consuming. In operational forecasting there is insufficient time to restart a run from the beginning with new data. Therefore, data assimilation should enable real-time utilization of data to improve predictions. This mandates the choice of an efficient data assimilation algorithm. Due to this necessity, we introduce, in this paper, the Neural Assimilation (NA), a coupled neural network made of two Recurrent Neural Networks trained on forecasting data and observed data respectively. We prove that the solution of NA is the same of KF. As NA is trained on both forecasting and observed data, after the phase of training NA is used for the prediction without the necessity of a correction given by the observations. This allows to avoid the prediction-correction cycle making the whole process very fast. Experimental results are provided and NA is tested to improve the prediction of oxygen diffusion across the Blood-Brain Barrier (BBB).
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- 2020
24. A Scalable Inference Method For Large Dynamic Economic Systems
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Khandelwal, Pratha, Nadler, Philip, Arcucci, Rossella, Knottenbelt, William, and Guo, Yi-Ke
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FOS: Economics and business ,FOS: Computer and information sciences ,Computer Science - Machine Learning ,Artificial Intelligence (cs.AI) ,Computer Science - Artificial Intelligence ,Statistics - Machine Learning ,Econometrics (econ.EM) ,Machine Learning (stat.ML) ,Machine Learning (cs.LG) ,Economics - Econometrics - Abstract
The nature of available economic data has changed fundamentally in the last decade due to the economy's digitisation. With the prevalence of often black box data-driven machine learning methods, there is a necessity to develop interpretable machine learning methods that can conduct econometric inference, helping policymakers leverage the new nature of economic data. We therefore present a novel Variational Bayesian Inference approach to incorporate a time-varying parameter auto-regressive model which is scalable for big data. Our model is applied to a large blockchain dataset containing prices, transactions of individual actors, analyzing transactional flows and price movements on a very granular level. The model is extendable to any dataset which can be modelled as a dynamical system. We further improve the simple state-space modelling by introducing non-linearities in the forward model with the help of machine learning architectures.
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- 2021
25. Correcting public opinion trends through Bayesian data assimilation
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Hendrickx, Robin, Arcucci, Rossella, Lopez, Julio Amador D��az, Guo, Yi-Ke, and Kennedy, Mark
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FOS: Computer and information sciences ,Computer Science - Computers and Society ,Computer Science - Machine Learning ,Artificial Intelligence (cs.AI) ,Computer Science - Computation and Language ,Computer Science - Artificial Intelligence ,Computers and Society (cs.CY) ,Computation and Language (cs.CL) ,Machine Learning (cs.LG) - Abstract
Measuring public opinion is a key focus during democratic elections, enabling candidates to gauge their popularity and alter their campaign strategies accordingly. Traditional survey polling remains the most popular estimation technique, despite its cost and time intensity, measurement errors, lack of real-time capabilities and lagged representation of public opinion. In recent years, Twitter opinion mining has attempted to combat these issues. Despite achieving promising results, it experiences its own set of shortcomings such as an unrepresentative sample population and a lack of long term stability. This paper aims to merge data from both these techniques using Bayesian data assimilation to arrive at a more accurate estimate of true public opinion for the Brexit referendum. This paper demonstrates the effectiveness of the proposed approach using Twitter opinion data and survey data from trusted pollsters. Firstly, the possible existence of a time gap of 16 days between the two data sets is identified. This gap is subsequently incorporated into a proposed assimilation architecture. This method was found to adequately incorporate information from both sources and measure a strong upward trend in Leave support leading up to the Brexit referendum. The proposed technique provides useful estimates of true opinion, which is essential to future opinion measurement and forecasting research.
- Published
- 2021
26. Ensemble latent assimilation with deep learning surrogate model: application to drop interaction in a microfluidics device.
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Zhuang, Yilin, Cheng, Sibo, Kovalchuk, Nina, Simmons, Mark, Matar, Omar K., Guo, Yi-Ke, and Arcucci, Rossella
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MICROFLUIDICS ,DEEP learning ,RECURRENT neural networks ,REDUCED-order models ,DYNAMICAL systems - Abstract
A major challenge in the field of microfluidics is to predict and control drop interactions. This work develops an image-based data-driven model to forecast drop dynamics based on experiments performed on a microfluidics device. Reduced-order modelling techniques are applied to compress the recorded images into low-dimensional spaces and alleviate the computational cost. Recurrent neural networks are then employed to build a surrogate model of drop interactions by learning the dynamics of compressed variables in the reduced-order space. The surrogate model is integrated with real-time observations using data assimilation. In this paper we developed an ensemble-based latent assimilation algorithm scheme which shows an improvement in terms of accuracy with respect to the previous approaches. This work demonstrates the possibility to create a reliable data-driven model enabling a high fidelity prediction of drop interactions in microfluidics device. The performance of the developed system is evaluated against experimental data (i.e., recorded videos), which are excluded from the training of the surrogate model. The developed scheme is general and can be applied to other dynamical systems. [ABSTRACT FROM AUTHOR]
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- 2022
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27. Data Assimilation in the Latent Space of a Neural Network
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Amendola, Maddalena, Arcucci, Rossella, Mottet, Laetitia, Casas, Cesar Quilodran, Fan, Shiwei, Pain, Christopher, Linden, Paul, and Guo, Yi-Ke
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FOS: Computer and information sciences ,Computer Science - Machine Learning ,Statistics - Machine Learning ,Machine Learning (stat.ML) ,Machine Learning (cs.LG) - Abstract
There is an urgent need to build models to tackle Indoor Air Quality issue. Since the model should be accurate and fast, Reduced Order Modelling technique is used to reduce the dimensionality of the problem. The accuracy of the model, that represent a dynamic system, is improved integrating real data coming from sensors using Data Assimilation techniques. In this paper, we formulate a new methodology called Latent Assimilation that combines Data Assimilation and Machine Learning. We use a Convolutional neural network to reduce the dimensionality of the problem, a Long-Short-Term-Memory to build a surrogate model of the dynamic system and an Optimal Interpolated Kalman Filter to incorporate real data. Experimental results are provided for CO2 concentration within an indoor space. This methodology can be used for example to predict in real-time the load of virus, such as the SARS-COV-2, in the air by linking it to the concentration of CO2.
- Published
- 2020
28. IC cloud: Enabling compositional cloud
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Guo, Yi-Ke and Guo, Li
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- 2011
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29. Parameter Flexible Wildfire Prediction Using Machine Learning Techniques: Forward and Inverse Modelling.
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Cheng, Sibo, Jin, Yufang, Harrison, Sandy P., Quilodrán-Casas, César, Prentice, Iain Colin, Guo, Yi-Ke, and Arcucci, Rossella
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MACHINE learning ,PARAMETER identification ,WILDFIRES ,PARAMETER estimation ,CALIFORNIA wildfires ,INTRUSION detection systems (Computer security) ,FORECASTING - Abstract
Parameter identification for wildfire forecasting models often relies on case-by-case tuning or posterior diagnosis/analysis, which can be computationally expensive due to the complexity of the forward prediction model. In this paper, we introduce an efficient parameter flexible fire prediction algorithm based on machine learning and reduced order modelling techniques. Using a training dataset generated by physics-based fire simulations, the method forecasts burned area at different time steps with a low computational cost. We then address the bottleneck of efficient parameter estimation by developing a novel inverse approach relying on data assimilation techniques (latent assimilation) in the reduced order space. The forward and the inverse modellings are tested on two recent large wildfire events in California. Satellite observations are used to validate the forward prediction approach and identify the model parameters. By combining these forward and inverse approaches, the system manages to integrate real-time observations for parameter adjustment, leading to more accurate future predictions. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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30. Impact of prolonged and early bevacizumab treatment on the overall survival of EGFR‐mutant and EGFR‐wild type nonsquamous non‐small cell lung cancer
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Huang, Yu‐Chen, Shen, Shih‐Min, Liu, Chien‐Ying, Pavlidis, Stelios, Wang, Chih‐Liang, Ko, How‐Wen, Chung, Fu‐Tsai, Lin, Tin‐Yu, Feng, Po‐Hao, Lee, Kang‐Yun, Guo, Yi‐Ke, Yang, Cheng‐Ta, and Kuo, Chih‐Hsi Scott
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Adult ,Aged, 80 and over ,Male ,Lung Neoplasms ,EGFR ,Original Articles ,Kaplan-Meier Estimate ,Middle Aged ,NSCLC ,VEGF ,Time-to-Treatment ,Bevacizumab ,ErbB Receptors ,Antineoplastic Agents, Immunological ,Treatment Outcome ,Carcinoma, Non-Small-Cell Lung ,Antineoplastic Combined Chemotherapy Protocols ,Mutation ,Humans ,Original Article ,Female ,Aged ,Neoplasm Staging ,Proportional Hazards Models ,Retrospective Studies - Abstract
Background VEGF plays a key role in tumor angiogenesis and immunosuppression. VEGF‐blocking has proven beneficial for EGFR mutant and wild‐type nonsquamous non‐small cell lung cancer (nonsq‐NSCLC); however, the number of cycles and treatment line yielding the optimal benefit are unknown. Methods We retrospectively analyzed the data of 115 patients with advanced/metastatic nonsq‐NSCLC administered at least one cycle of bevacizumab. The number of bevacizumab cycles was treated as a time‐dependent covariate. Predictors of overall survival (OS) were investigated. Results Bevacizumab was used as first‐line treatment in 47 (40.9%) patients, with a median of five cycles (range: 1–31). Eastern Cooperative Oncology Group performance status ≥ 2 (hazard ratio [HR] 4.78, 95% confidence interval [CI] 2.68–8.51; P < 0.001), wild‐type EGFR (HR 2.61, 95% CI 1.45–4.70; P = 0.001), and bleeding during bevacizumab treatment (HR 3.63, 95% CI 1.77–7.45; P < 0.001) were predictive of poor OS; the number of bevacizumab cycles and first‐line administration were not. In the wild‐type EGFR subgroup, the number of bevacizumab cycles (≥ 5 vs. 1–4) was associated with a significant OS benefit (HR 0.28, 95% CI 0.08–0.98; P = 0.044); first‐line administration also showed an OS benefit (HR 0.48, 95% CI 0.20–1.17; P = 0.105). A significant association between the number of cycles and EGFR status was identified (P = 0.046). Conclusion OS benefit is negatively affected by bleeding events in bevacizumab‐treated patients. Prolonged and early introduction of bevacizumab may provide an OS benefit for patients with wild‐type EGFR nonsq‐NSCLC.
- Published
- 2018
31. Minimised Geometric Buchberger Algorithm for Integer Programming
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Li, Qiang, Guo, Yi-ke, Darlington, John, and Ida, Tetsuo
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- 2001
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32. Relationship between type 2 cytokine and inflammasome responses in obesity-associated asthma.
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Pinkerton, James W., Kim, Richard Y., Brown, Alexandra C., Rae, Brittany E., Donovan, Chantal, Mayall, Jemma R., Carroll, Olivia R., Khadem Ali, Md., Scott, Hayley A., Berthon, Bronwyn S., Baines, Katherine J., Starkey, Malcolm R., Kermani, Nazanin Z., Guo, Yi-Ke, Robertson, Avril A.B., O'Neill, Luke A.J., Adcock, Ian M., Cooper, Matthew A., Gibson, Peter G., and Wood, Lisa G.
- Abstract
Obesity is a risk factor for asthma, and obese asthmatic individuals are more likely to have severe, steroid-insensitive disease. How obesity affects the pathogenesis and severity of asthma is poorly understood. Roles for increased inflammasome-mediated neutrophilic responses, type 2 immunity, and eosinophilic inflammation have been described. We investigated how obesity affects the pathogenesis and severity of asthma and identified effective therapies for obesity-associated disease. We assessed associations between body mass index and inflammasome responses with type 2 (T2) immune responses in the sputum of 25 subjects with asthma. Functional roles for NLR family, pyrin domain–containing (NLRP) 3 inflammasome and T2 cytokine responses in driving key features of disease were examined in experimental high-fat diet–induced obesity and asthma. Body mass index and inflammasome responses positively correlated with increased IL-5 and IL-13 expression as well as C-C chemokine receptor type 3 expression in the sputum of subjects with asthma. High-fat diet–induced obesity resulted in steroid-insensitive airway hyperresponsiveness in both the presence and absence of experimental asthma. High-fat diet–induced obesity was also associated with increased NLRP3 inflammasome responses and eosinophilic inflammation in airway tissue, but not lumen, in experimental asthma. Inhibition of NLRP3 inflammasome responses reduced steroid-insensitive airway hyperresponsiveness but had no effect on IL-5 or IL-13 responses in experimental asthma. Depletion of IL-5 and IL-13 reduced obesity-induced NLRP3 inflammasome responses and steroid-insensitive airway hyperresponsiveness in experimental asthma. We found a relationship between T2 cytokine and NLRP3 inflammasome responses in obesity-associated asthma, highlighting the potential utility of T2 cytokine–targeted biologics and inflammasome inhibitors. [ABSTRACT FROM AUTHOR]
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- 2022
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33. KDE Bioscience: Platform for bioinformatics analysis workflows
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Lu, Qiang, Hao, Pei, Curcin, Vasa, He, Weizhong, Li, Yuan-Yuan, Luo, Qing-Ming, Guo, Yi-Ke, and Li, Yi-Xue
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- 2006
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34. Afatinib treatment in a large real‐world cohort of nonsmall cell lung cancer patients with common and uncommon epidermal growth factor receptor mutation.
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Huang, Chi‐Hsien, Ju, Jia‐Shiuan, Chiu, Tzu‐Hsuan, Huang, Allen Chung‐Cheng, Tung, Pi‐Hung, Wang, Chin‐Chou, Liu, Chien‐Ying, Chung, Fu‐Tsai, Fang, Yueh‐Fu, Guo, Yi‐Ke, Kuo, Chih‐Hsi Scott, and Yang, Cheng‐Ta
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EPIDERMAL growth factor receptors ,NON-small-cell lung carcinoma ,GENETIC mutation ,PROTEIN-tyrosine kinase inhibitors ,AFATINIB ,EPIDERMAL growth factor - Abstract
The epidermal growth factor receptor tyrosine kinase inhibitor (EGFR‐TKI) afatinib improves survival in nonsmall cell lung cancer (NSCLC) patients with EGFR mutation. We analysed the outcome between EGFR mutation subtypes in a large afatinib‐treated cohort in which 516 EGFR‐mutated NSCLC patients receiving afatinib as front‐line treatment. EGFR uncommon mutations include exon 20 insertion, de novo T790M of high or low allele frequency (dT790MHAF/dT790MLAF), non‐T790M compound mutation and others, where EGFR exon 20 insertion and dT790MHAF were defined as type‐I and the rest as type‐II uncommon mutation. Four hundred and sixty‐one (89.3%) and 55 (10.7%) patients were common and uncommon mutation, respectively. Exon 20 insertion and dT790MHAF patients demonstrated a significantly shortened progression‐free survival (PFS) (2.6 and 4.1 months) compared to EGFR common mutation, dT790MLAF and other uncommon mutation patients (15.1, 27.0 and 18.4 months; P = 3 × 10−8). Type‐I uncommon mutation was an independent predictor of PFS (HR 4.46 [95% CI, 2.60‐7.64]; P <.001) and OS (HR 2.56 [95% CI, 1.37‐4.75]; P =.003). EGFR L858R patients demonstrated a significantly higher CNS progression (cause‐specific HR, 3.16; 95% CI 1.24‐8.08; P =.016), and type‐I uncommon mutation patients exhibited a significantly higher systemic progression (cause‐specific HR, 4.95; 95% CI 2.30‐10.60; P = 4.3 × 10−5). Tendencies of higher CNS and lower systemic progression were observed in type‐II uncommon mutation patients. A PFS ≥ 12 months (OR 2.38 [95% CI, 1.18‐4.89]; P =.016) and uncommon EGFR mutation (OR 0.08 [95% CI, 0.01‐0.48]; P =.021) were independent predictors of secondary T790M. Afatinib‐treated NSCLC patients presented an EGFR genotype‐specific pattern of disease progression and outcome. What's new? For patients with nonsmall cell lung cancer (NSCLC), epidermal growth factor tyrosine kinase inhibitors (EGFR‐TKIs) can significantly improve survival. EGFG‐TKI effectiveness, however, is compromised by acquired EGFR mutations, especially de novo T790M mutations. Here, the impact of EGFR genotypes on the efficacy of afatinib, a second‐generation EGFR‐TKI, was investigated in a cohort of EGFR‐mutated NSCLC patients. Afatinib efficacy was associated with T790M allele quantity in patients with de novo T790M mutation. In particular, front‐line afatinib therapy was associated with favourable survival in EGFR‐mutated patients, whereas resistance was marked by a genotype‐specific pattern of disease progression, with secondary T790M development. [ABSTRACT FROM AUTHOR]
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- 2022
- Full Text
- View/download PDF
35. InfoGrid: providing information integration for knowledge discovery
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Giannadakis, Nikolaos, Rowe, Anthony, Ghanem, Moustafa, and Guo, Yi-ke
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- 2003
- Full Text
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36. First- or second-generation epidermal growth factor receptor tyrosine kinase inhibitors in a large, real-world cohort of patients with non-small cell lung cancer.
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Huang, Allen Chung-Cheng, Huang, Chi-Hsien, Ju, Jia-Shiuan, Chiu, Tzu-Hsuan, Tung, Pi-Hung, Wang, Chin-Chou, Liu, Chien-Ying, Chung, Fu-Tsai, Fang, Yueh-Fu, Guo, Yi-Ke, Kuo, Chih-Hsi Scott, and Yang, Cheng-Ta
- Abstract
Background: There are limited comparisons of first- and second-generation EGFR tyrosine kinase inhibitors (TKIs) in large, real-world cohorts of non-small cell lung cancer (NSCLC) patients with epidermal growth factor receptor (EGFR) mutations. Methods: Patients with advanced NSCLC (N = 612) with common EGFR mutations receiving first-line gefitinib/erlotinib and afatinib were grouped and propensity-score matched. Progression-free survival (PFS), overall survival (OS) and secondary T790M mutations were analyzed. Results: The gefitinib/erlotinib and afatinib groups each contained 206 patients after matching. Compared with gefitinib/erlotinib, patients receiving afatinib achieved longer median PFS (16.3 versus 14.2 months; log-rank test p = 0.020) and had a lower risk of progression [hazard ratio (HR) 0.73 (95% confidence interval (CI), 0.57–0.94); p = 0.017]. Median OS (37.3 versus 34.2 months; log-rank test p = 0.500) and reduction in risk of death [HR 0.89 (95% CI, 0.65–1.23); p = 0.476] did not differ significantly between groups. T790M positivity was significantly higher in the gefitinib/erlotinib than afatinib group (70.9% versus 44.6%, p < 0.001). Multivariate analysis demonstrated that afatinib was independently associated with lower T790M positivity [odds ratio (OR) 0.27 (95% CI, 0.14–0.53); p < 0.001], whereas ⩾12 months PFS after EGFR-TKI treatment [OR 3.00 (95% CI, 1.56–5.98); p = 0.001] and brain metastasis [OR 2.12 (95% CI, 1.08–4.26); p = 0.030] were associated with higher T790M positivity. Sequential third-generation EGFR-TKI treatment was administered to 63 patients, in whom median OS after the second–third-generation and first–third-generation EGFR-TKI sequences were 38.8 and 29.1 months, respectively. Conclusion: Compared with gefitinib/erlotinib, afatinib had a higher treatment efficacy and a lower secondary T790M positivity in a large, real-world cohort of Asian patients with EGFR -mutated NSCLC. [ABSTRACT FROM AUTHOR]
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- 2021
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37. Network‐based visualisation reveals new insights into transposable element diversity.
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Schneider, Lisa, Guo, Yi‐Ke, Birch, David, and Sarkies, Peter
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HORIZONTAL gene transfer , *EUKARYOTIC genomes , *VISUALIZATION , *BIPARTITE graphs , *GENE silencing , *GENE regulatory networks - Abstract
Transposable elements (TEs) are widespread across eukaryotic genomes, yet their content varies widely between different species. Factors shaping the diversity of TEs are poorly understood. Understanding the evolution of TEs is difficult because their sequences diversify rapidly and TEs are often transferred through non‐conventional means such as horizontal gene transfer. We developed a method to track TE evolution using network analysis to visualise TE sequence and TE content across different genomes. We illustrate our method by first using a monopartite network to study the sequence evolution of Tc1/mariner elements across focal species. We identify a connection between two subfamilies associated with convergent acquisition of a domain from a protein‐coding gene. Second, we use a bipartite network to study how TE content across species is shaped by epigenetic silencing mechanisms. We show that the presence of Piwi‐interacting RNAs is associated with differences in network topology after controlling for phylogenetic effects. Together, our method demonstrates how a network‐based approach can identify hitherto unknown properties of TE evolution across species. SYNOPSIS: A novel network‐based method is used to study transposable element (TE) evolution, revealing new insights into how epigenetic silencing mechanisms affect TE content across species. Network approaches can be used to reconstruct the evolution of transposable elements.Networks based on sequence similarity between transposable elements reveal unexpected connections between TE families due to convergent evolution.Bipartite networks based on TE content show differences in network properties associated with the different epigenetic TE silencing mechanisms possessed by different species. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
38. A computational framework for complex disease stratification from multiple large-scale datasets
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Meulder, Bertrand de, Lefaudeux, Diane, Bansal, Aruna T., Mazein, Alexander, Chaiboonchoe, Amphun, Ahmed, Hassan, Balaur, Irina, Saqi, Mansoor, Pellet, Johann, Ballereau, Stephane, Lemonnier, Nathanaël, Sun, Kai, Pandis, Ioannis, Yang, Xian, Batuwitage, Manohara, Kretsos, Kosmas, Eyll, Jonathan van, Bedding, Alun, Davison, Timothy, Dodson, Paul, Larminie, Christopher, Postle, Anthony, Corfield, Julie, Djukanovic, Ratko, Chung, Kian Fan, Adcock, Ian M., Guo, Yi-Ke, Sterk, Peter J., Manta, Alexander, Rowe, Anthony, Baribaud, Frédéric, Auffray, Charles, Badorrek, Philipp, Faulenbach, Cornelia, Braun, Armin, Hohlfeld, Jens, Krug, Norbert, and Publica
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stratification ,Omics data ,systems medicine ,molecular signature - Abstract
Background: Multilevel data integration is becoming a major area of research in systems biology. Within this area, multi-'omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-'omics signatures of disease states. Methods: The framework is divided into four major steps: dataset subsetting, feature filtering, 'omics-based clustering and biomarker identification. Results: We illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-'omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes. Conclusions: This framework will help health researchers plan and perform multi-'omics big data analyses to generate hypotheses and make sense of their rich, diverse and ever growing datasets, to enable implementation of translational P4 medicine.
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- 2018
39. Sputum macrophage diversity and activation in asthma: Role of severity and inflammatory phenotype.
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Tiotiu, Angelica, Zounemat Kermani, Nazanin, Badi, Yusef, Pavlidis, Stelios, Hansbro, Philip M., Guo, Yi‐Ke, Chung, Kian Fan, and Adcock, Ian M.
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MACROPHAGE activation ,PHENOTYPES ,SPUTUM ,ASTHMA ,TUMOR necrosis factor receptors ,MACROPHAGE activation syndrome - Abstract
Background: Macrophages control innate and acquired immunity, but their role in severe asthma remains ill‐defined. We investigated gene signatures of macrophage subtypes in the sputum of 104 asthmatics and 16 healthy volunteers from the U‐BIOPRED cohort. Methods: Forty‐nine gene signatures (modules) for differentially stimulated macrophages, one to assess lung tissue‐resident cells (TR‐Mφ) and two for their polarization (classically and alternatively activated macrophages: M1 and M2, respectively) were studied using gene set variation analysis. We calculated enrichment scores (ES) across severity and previously identified asthma transcriptome‐associated clusters (TACs). Results: Macrophage numbers were significantly decreased in severe asthma compared to mild‐moderate asthma and healthy volunteers. The ES for most modules were also significantly reduced in severe asthma except for 3 associated with inflammatory responses driven by TNF and Toll‐like receptors via NF‐κB, eicosanoid biosynthesis via the lipoxygenase pathway and IL‐2 biosynthesis (all P <.01). Sputum macrophage number and the ES for most macrophage signatures were higher in the TAC3 group compared to TAC1 and TAC2 asthmatics. However, a high enrichment was found in TAC1 for 3 modules showing inflammatory pathways linked to Toll‐like and TNF receptor activation and arachidonic acid metabolism (P <.001) and in TAC2 for the inflammasome and interferon signalling pathways (P <.001). Data were validated in the ADEPT cohort. Module analysis provides additional information compared to conventional M1 and M2 classification. TR‐Mφ were enriched in TAC3 and associated with mitochondrial function. Conclusions: Macrophage activation is attenuated in severe granulocytic asthma highlighting defective innate immunity except for specific subsets characterized by distinct inflammatory pathways. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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40. Deep Data Assimilation: Integrating Deep Learning with Data Assimilation.
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Arcucci, Rossella, Zhu, Jiangcheng, Hu, Shuang, Guo, Yi-Ke, and Rodrigues, João M. F.
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DEEP learning ,RECURRENT neural networks ,MACHINE learning ,DYNAMIC models ,DYNAMICAL systems - Abstract
In this paper, we propose Deep Data Assimilation (DDA), an integration of Data Assimilation (DA) with Machine Learning (ML). DA is the Bayesian approximation of the true state of some physical system at a given time by combining time-distributed observations with a dynamic model in an optimal way. We use a ML model in order to learn the assimilation process. In particular, a recurrent neural network, trained with the state of the dynamical system and the results of the DA process, is applied for this purpose. At each iteration, we learn a function that accumulates the misfit between the results of the forecasting model and the results of the DA. Subsequently, we compose this function with the dynamic model. This resulting composition is a dynamic model that includes the features of the DA process and that can be used for future prediction without the necessity of the DA. In fact, we prove that the DDA approach implies a reduction of the model error, which decreases at each iteration; this is achieved thanks to the use of DA in the training process. DDA is very useful in that cases when observations are not available for some time steps and DA cannot be applied to reduce the model error. The effectiveness of this method is validated by examples and a sensitivity study. In this paper, the DDA technology is applied to two different applications: the Double integral mass dot system and the Lorenz system. However, the algorithm and numerical methods that are proposed in this work can be applied to other physics problems that involve other equations and/or state variables. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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- View/download PDF
41. A data driven methodology for social science research with left-behind children as a case study.
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Wu, Chao, Wang, Guolong, Hu, Simon, Liu, Yue, Mi, Hong, Zhou, Ye, Guo, Yi-ke, and Song, Tongtong
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SOCIAL science research ,SOCIAL science methodology ,FEATURE selection ,MACHINE learning ,REGRESSION analysis - Abstract
For decades, traditional correlation analysis and regression models have been used in social science research. However, the development of machine learning algorithms makes it possible to apply machine learning techniques for social science research and social issues, which may outperform standard regression methods in some cases. Under the circumstances, this article proposes a methodological workflow for data analysis by machine learning techniques that have the possibility to be widely applied in social issues. Specifically, the workflow tries to uncover the natural mechanisms behind the social issues through a data-driven perspective from feature selection to model building. The advantage of data-driven techniques in feature selection is that the workflow can be built without so much restriction of related knowledge and theory in social science. The advantage of using machine learning techniques in modelling is to uncover non-linear and complex relationships behind social issues. The main purpose of our methodological workflow is to find important fields relevant to the target and provide appropriate predictions. However, to explain the result still needs theory and knowledge from social science. In this paper, we trained a methodological workflow with left-behind children as the social issue case, and all steps and full results are included. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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42. Consolidation treatment of durvalumab after chemoradiation in real‐world patients with stage III unresectable non‐small cell lung cancer.
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Chu, Chia‐Hsun, Chiu, Tzu‐Hsuan, Wang, Chin‐Chou, Chang, Wen‐Chen, Huang, Allen Chung‐Cheng, Liu, Chien‐Ying, Wang, Chih‐Liang, Ko, Ho‐Wen, Chung, Fu‐Tsai, Hsu, Ping‐Chih, Guo, Yi‐Ke, Kuo, Chih‐Hsi S., and Yang, Cheng‐Ta
- Subjects
LUNG cancer prognosis ,COMBINED modality therapy ,CONFIDENCE intervals ,LUNG cancer ,LYMPHOCYTES ,METASTASIS ,MONOCLONAL antibodies ,NEUTROPHILS ,SURVIVAL ,TIME ,TUMOR classification ,TREATMENT effectiveness ,RETROSPECTIVE studies ,DISEASE progression ,ODDS ratio ,CHEMORADIOTHERAPY - Abstract
Background: Treatment for stage III non‐small cell lung cancer (NSCLC) of unresectable disease mainly involves concurrent chemoradiation (CRT). Post‐CRT consolidation treatment with durvalumab is a major therapeutic advance that provides survival benefit in this group of patients. However, the performance of this treatment strategy remains to be studied in a real‐world setting. Methods: A total of 31 patients who had disease control post‐CRT were included in the durvalumab early access program (EAP) as an intent‐to‐treat cohort and retrospectively reviewed for post‐CRT progression‐free survival (PFS) and time to metastatic disease or death (TMDD). The neutrophil‐to‐lymphocyte ratio (NLR) at the initiation of durvalumab was analyzed in 29 patients. Results: The median time from the completion of concurrent CRT to the initiation of durvalumb was 2.8 months. The objective response was 25.8% and the 12 month PFS and TMDD‐free rate were 56.4% and 66.9%, respectively. The low NLR patients showed a significantly longer post‐CRT PFS (not reach vs. 12.0 months [95% CI: 5.5–not estimable]; P = 0.040; the hazard ratio for disease progression or death, 0.23 [95% CI: 0.05–1.00]; P = 0.048) and the 12 month post‐CRT PFS rate (82.5 vs. 42.6%). The post‐CRT TMDD (not reach vs. 12.6 months, [95% CI: 10.8–not estimable]; P = 0.010; the hazard ratio for distant metastasis or death, 0.11 [95% CI: 0.01–0.88]; P = 0.037) and 12 month post‐CRT TMDD‐free rate (90.9 vs. 57.1%) were also significantly higher in the low NLR patients. Conclusions: Durvalumab consolidation treatment in real‐world patients showed substantial efficacy and the correlation with the NLR level warrants further investigation. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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43. Front‐line treatment of ceritinib improves efficacy over crizotinib for Asian patients with anaplastic lymphoma kinase fusion NSCLC: The role of systemic progression control.
- Author
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Huang, Shih‐Hao, Huang, Allen Chung‐Cheng, Wang, Chin‐Chou, Chang, Wen‐Chen, Liu, Chien‐Ying, Pavlidis, Stelios, Ko, Ho‐Wen, Chung, Fu‐Tsai, Hsu, Ping‐Chih, Guo, Yi‐Ke, Kuo, Chih‐Hsi Scott, and Yang, Cheng‐Ta
- Subjects
DIARRHEA ,LUNG cancer prognosis ,PREVENTION of disease progression ,CANCER chemotherapy ,COMPARATIVE studies ,CONFIDENCE intervals ,LUNG cancer ,PIPERIDINE ,PLATINUM compounds ,TUMOR classification ,TREATMENT effectiveness ,RETROSPECTIVE studies ,DISEASE progression ,ANAPLASTIC lymphoma kinase ,LOG-rank test ,ODDS ratio ,CHEMICAL inhibitors - Abstract
Background: Approximately 3%–5% of lung adenocarcinoma is driven by anaplastic lymphoma kinase (ALK) fusion oncogene, whose activity can be suppressed by multiple ALK inhibitors. Crizotinib and ceritinib have demonstrated superior efficacy to platinum‐based chemotherapy as front‐line treatment for patients with ALK‐positive advanced non‐small cell lung cancer (NSCLC). However, the direct comparison between them in the front‐line setting remains lacking. Methods: A total of 48 patients with ALK‐positive, previously untreated advanced NSCLC, who received crizotinib and ceritinib as front‐line treatment were retrospectively investigated. The efficacy and pattern of disease progression were analyzed. Results: Patients receiving ceritinib treatment were significantly younger than those receiving crizotinib treatment (52.0 vs. 63.0, P = 0.016). The median progression‐free survival (PFS) was significantly longer with ceritinib than with crizotinib treatment (32.3 vs. 12.9 months; log‐rank P = 0.020); the hazard ratio for disease progression or death, 0.27 (95% CI, 0.08–0.90; P = 0.033). An objective response was noted in all patients in the ceritinib group and in 23 patients in the crizotinib group (74.2%; 95% CI, 59.0 to 88.5). The rate of systemic progression was significantly lower over time with ceritinib treatment compared to crizotinib treatment (cause‐specific hazard ratio, 0.21; 95% CI 0.06–0.73; P = 0.014). Serious adverse events were noted in one (2.9%) patient showing elevated liver function in the crizotinib group and three (23.1%) patients showing diarrhea in the ceritinib group. Dose reduction was needed in five out of 13 (38.5%) patients receiving ceritinib treatment. Conclusion: Ceritinib showed higher efficacy associated with a better control of systemic progression compared to crizotinib for the front‐line treatment of ALK‐positive advanced NSCLCs. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
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44. Erratum to: Making sense of big data in health research: towards an EU action plan
- Author
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Auffray, Charles, Balling, Rudi, Barroso, Inês, Bencze, László, Benson, Mikael, Bergeron, Jay, Bernal-Delgado, Enrique, Blomberg, Niklas, Bock, Christoph, Conesa, Ana, Del Signore, Susanna, Delogne, Christophe, Devilee, Peter, Di Meglio, Alberto, Eijkemans, Marinus, Flicek, Paul, Graf, Norbert, Grimm, Vera, Guchelaar, Henk-Jan, Guo, Yi-Ke, Gut, Ivo Glynne, Hanbury, Allan, Hanif, Shahid, Hilgers, Ralf-Dieter, Honrado, Ángel, Hose, D Rod, Houwing-Duistermaat, Jeanine, Hubbard, Tim, Janacek, Sophie Helen, Karanikas, Haralampos, Kievits, Tim, Kohler, Manfred, Kremer, Andreas, Lanfear, Jerry, Lengauer, Thomas, Maes, Edith, Meert, Theo, Müller, Werner, Nickel, Dörthe, Oledzki, Peter, Pedersen, Bertrand, Petkovic, Milan, Pliakos, Konstantinos, Rattray, Magnus, I Màs, Josep Redón, Schneider, Reinhard, Sengstag, Thierry, Serra-Picamal, Xavier, Spek, Wouter, Vaas, Lea A I, van Batenburg, Okker, Vandelaer, Marc, Varnai, Peter, Villoslada, Pablo, Vizcaíno, Juan Antonio, Wubbe, John Peter Mary, and Zanetti, Gianluigi
- Subjects
Journal Article - Published
- 2016
45. Correction: Making sense of big data in health research: towards an EU action plan (vol 8, pg 71, 2016)
- Author
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Auffray, Charles, Balling, Rudi, Barroso, Ines, Bencze, Laszlo, Benson, Mikael, Bergeron, Jay, Bernal-Delgado, Enrique, Blomberg, Niklas, Bock, Christoph, Conesa, Ana, Del Signore, Susanna, Delogne, Christophe, Devilee, Peter, Di Meglio, Alberto, Eijkemans, Marinus, Flicek, Paul, Graf, Norbert, Grimm, Vera, Guchelaar, Henk-Jan, Guo, Yi-Ke, Glynne Gut, Ivo, Hanbury, Allan, Hanif, Shahid, Hilgers, Ralf-Dieter, Honrado, Angel, Rod Hose, D., Houwing-Duistermaat, Jeanine, Hubbard, Tim, Helen Janacek, Sophie, Karanikas, Haralampos, Kievits, Tim, Kohler, Manfred, Kremer, Andreas, Lanfear, Jerry, Lengauer, Thomas, Maes, Edith, Meert, Theo, Muller, Werner, Nickel, Dothe, Oledzki, Peter, Pedersen, Bertrand, Petkovic, Milan, Pliakos, Konstantinos, Rattray, Magnus, Redon i Mas, Josep, Schneider, Reinhard, Sengstag, Thierry, Serra-Picamal, Xavier, Spek, Wouter, Vaas, Lea A. I., van Batenburg, Okker, Vandelaer, Marc, Varnai, Peter, Villoslada, Pablo, Antonio Vizcaino, Juan, Peter Mary Wubbe, John, and Zanetti, Gianluigi
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Klinisk medicin ,Clinical Medicine - Abstract
n/a
- Published
- 2016
46. Clinical and inflammatory characteristics of the European U-BIOPRED adult severe asthma cohort
- Author
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Shaw, Dominick E., Sousa, Ana R., Fowler, Stephen J., Fleming, Louise J., Roberts, Graham, Corfield, Julie, Pandis, Ioannis, Bansal, Aruna T., Bel, Elisabeth H., Auffray, Charles, Compton, Chris H., Bisgaard, Hans, Bucchioni, Enrica, Caruso, Massimo, Chanez, Pascal, Dahlen, Barbro, Dahlen, Sven-Erik, Dyson, Kerry, Frey, Urs, Geiser, Thomas, Gerhardsson de Verdier, Maria, Gibeon, David, Guo, Yi-ke, Hashimoto, Simone, Hedlin, Gunilla, Jeyasingham, Elizabeth, Hekking, Pieter-Paul W., Higenbottam, Tim, Knox, Alan J., Krug, Norbert, Erpenbeck, Veit J., Larsson, Lars X., Lazarinis, Nikos, Matthews, John G., Middelveld, Roelinde, Montuschi, Paolo, Musial, Jacek, Myles, David, Pahus, Laurie, Seibold, Wolfgang, Singer, Florian, Strandberg, Karin, Vestbo, Jorgen, Vissing, Nadja, von Garnier, Christophe, Adcock, Ian M., Wagers, Scott, Rowe, Anthony, Howarth, Peter, Wagener, Ariane H., Djukanovic, Ratko, Sterk, Peter J., and Chung, Kian Fan
- Subjects
Pulmonary and Respiratory Medicine ,respiratory tract diseases - Abstract
U-BIOPRED is a European Union consortium of 20 academic institutions, 11 pharmaceutical companies and six patient organisations with the objective of improving the understanding of asthma disease mechanisms using a systems biology approach.This cross-sectional assessment of adults with severe asthma, mild/moderate asthma and healthy controls from 11 European countries consisted of analyses of patient-reported outcomes, lung function, blood and airway inflammatory measurements.Patients with severe asthma (nonsmokers, n=311; smokers/ex-smokers, n=110) had more symptoms and exacerbations compared to patients with mild/moderate disease (n=88) (2.5 exacerbations versus 0.4 in the preceding 12?months; p
- Published
- 2015
47. Comparison of a combination of chemotherapy and immune checkpoint inhibitors and immune checkpoint inhibitors alone for the treatment of advanced and metastatic non‐small cell lung cancer.
- Author
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Kuo, Chih‐Hsi Scott, Wang, Chin‐Chou, Huang, Yu‐Chen, Pavlidis, Stelios, Liu, Chien‐Ying, Ko, How‐Wen, Chung, Fu‐Tsai, Lin, Tin‐Yu, Wang, Chih‐Liang, Guo, Yi‐Ke, and Yang, Cheng‐Ta
- Subjects
CANCER chemotherapy ,CANCER patients ,COMBINED modality therapy ,CONFIDENCE intervals ,IMMUNOTHERAPY ,LUNG cancer ,METASTASIS ,SURVIVAL ,TREATMENT effectiveness ,RETROSPECTIVE studies ,DESCRIPTIVE statistics - Abstract
Background: Single agent immune checkpoint inhibitors (ICIs) improve survival outcomes compared to chemotherapy for advanced non‐small cell lung cancer (NSCLC), but treatment efficacy widely varies. The combination of ICIs with chemotherapy has shown promising efficacy over chemotherapy alone; however, whether this strategy is superior to single agent ICIs for the treatment of advanced NSCLC remains unknown. Methods: The records of 109 patients with advanced NSCLC who were administered at least one cycle of ICIs were retrospectively reviewed. Patients were grouped based on the presence or absence of a chemotherapy treatment combination. Efficacy and survival outcomes were analyzed. Result: Sixty‐nine (58.0%) patients received single agent ICIs (ICI group) and 50 (42.0%) received ICIs and chemotherapy (ICC group). The median (3.2 vs. 3.0 months; P = 0.025) and one‐year (34.5 vs. 9.6%; P = 0.026) progression‐free survival (PFS) rates were significantly better in the ICC than in the ICI group. The superior efficacy of ICC remained in the propensity score matched pairs (median PFS 3.2 vs. 2.6 months, P = 0.032; 1‐year PFS 35.2 vs. 7.6%; P = 0.035). Eastern Cooperative Oncology Group performance status 0–1 (HR 0.37, 95% CI 0.22–0.62; P < 0.001) and the ICC group (HR 0.56, 95% CI 0.34–0.94; P = 0.028) were predictive of PFS. Subgroup‐to‐chemotherapy interaction revealed improved risk reduction for adenocarcinoma and EGFR mutation. Conclusion: Combing chemotherapy with ICIs improved treatment efficacy over ICIs alone. The additional efficacy of chemotherapy may differ between histological subtypes and EGFR mutation status. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
48. Navigating the disease landscape: knowledge representations for contextualizing molecular signatures.
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Saqi, Mansoor, Lysenko, Artem, Guo, Yi-Ke, Tsunoda, Tatsuhiko, and Auffray, Charles
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KNOWLEDGE representation (Information theory) ,DISEASES ,INDIVIDUALIZED medicine - Abstract
Large amounts of data emerging from experiments in molecular medicine are leading to the identification of molecular signatures associated with disease subtypes. The contextualization of these patterns is important for obtaining mechanistic insight into the aberrant processes associated with a disease, and this typically involves the integration of multiple heterogeneous types of data. In this review, we discuss knowledge representations that can be useful to explore the biological context of molecular signatures, in particular three main approaches, namely, pathway mapping approaches, molecular network centric approaches and approaches that represent biological statements as knowledge graphs. We discuss the utility of each of these paradigms, illustrate how they can be leveraged with selected practical examples and identify ongoing challenges for this field of research. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
49. Making sense of big data in health research: Towards an EU action plan.
- Author
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Auffray, Charles, Balling, Rudi, Barroso, Inês, Bencze, László, Benson, Mikael, Bergeron, Jay, Bernal-Delgado, Enrique, Blomberg, Niklas, Bock, Christoph, Conesa, Ana, Del Signore, Susanna, Delogne, Christophe, Devilee, Peter, Di Meglio, Alberto, Eijkemans, Marinus, Flicek, Paul, Graf, Norbert, Grimm, Vera, Guchelaar, Henk-Jan, and Guo, Yi-Ke
- Subjects
MEDICAL care ,GENOMES ,ELECTRONIC data processing - Abstract
Medicine and healthcare are undergoing profound changes. Whole-genome sequencing and high-resolution imaging technologies are key drivers of this rapid and crucial transformation. Technological innovation combined with automation and miniaturization has triggered an explosion in data production that will soon reach exabyte proportions. How are we going to deal with this exponential increase in data production? The potential of "big data" for improving health is enormous but, at the same time, we face a wide range of challenges to overcome urgently. Europe is very proud of its cultural diversity; however, exploitation of the data made available through advances in genomic medicine, imaging, and a wide range of mobile health applications or connected devices is hampered by numerous historical, technical, legal, and political barriers. European health systems and databases are diverse and fragmented. There is a lack of harmonization of data formats, processing, analysis, and data transfer, which leads to incompatibilities and lost opportunities. Legal frameworks for data sharing are evolving. Clinicians, researchers, and citizens need improved methods, tools, and training to generate, analyze, and query data effectively. Addressing these barriers will contribute to creating the European Single Market for health, which will improve health and healthcare for all Europeans. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
- View/download PDF
50. Computational Infrastructures for Data and Knowledge Management in Systems Biology.
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Georgatos, Fotis, Ballereau, Stéphane, Pellet, Johann, Ghanem, Moustafa, Price, Nathan, Hood, Leroy, Guo, Yi-Ke, Boutigny, Dominique, Auffray, Charles, Balling, Rudi, and Schneider, Reinhard
- Published
- 2013
- Full Text
- View/download PDF
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