Search

Protein-ligand binding affinity plays a pivotal role in drug development, particularly in identifying potential ligands for target disease-related proteins. Accurate affinity predictions can significantly reduce both the time and cost involved in drug development. However, highly precise affinity prediction remains a research challenge. A key to improve affinity prediction is to capture interactions between proteins and ligands effectively. Existing deep-learning-based computational approaches use 3D grids, 4D tensors, molecular graphs, or proximity-based adjacency matrices, which are either resource-intensive or do not directly represent potential interactions. In this paper, we propose atomic-level distance features and attention mechanisms to capture better specific protein-ligand interactions based on donor-acceptor relations, hydrophobicity, and π -stacking atoms. We argue that distances encompass both short-range direct and long-range indirect interaction effects while attention mechanisms capture levels of interaction effects. On the very well-known CASF-2016 dataset, our proposed method, named Distance plus Attention for Affinity Prediction (DAAP), significantly outperforms existing methods by achieving Correlation Coefficient (R) 0.909, Root Mean Squared Error (RMSE) 0.987, Mean Absolute Error (MAE) 0.745, Standard Deviation (SD) 0.988, and Concordance Index (CI) 0.876. The proposed method also shows substantial improvement, around 2% to 37%, on five other benchmark datasets. The program and data are publicly available on the website https://gitlab.com/mahnewton/daap. Scientific Contribution Statement This study innovatively introduces distance-based features to predict protein-ligand binding affinity, capitalizing on unique molecular interactions. Furthermore, the incorporation of protein sequence features of specific residues enhances the model's proficiency in capturing intricate binding patterns. The predictive capabilities are further strengthened through the use of a deep learning architecture with attention mechanisms, and an ensemble approach, averaging the outputs of five models, is implemented to ensure robust and reliable predictions. [ABSTRACT FROM AUTHOR]

Social media platforms like Twitter have been recognized as a reliable real-time information dissemination and collection medium, especially during disasters when traditional communication media fail. Information access improves situational awareness and is essential for successful disaster management. The response team primarily requires information about the people in danger and the need for and availability of resources, such as food, shelter, and medical supplies. These details can only be actionable with the location information. People's tweets during disasters will be informal and not adhere to standard linguistic rules, causing traditional NLP methods to fail. This study focuses on location reference recognition, in which the system must identify any locations mentioned in tweets. Most existing solutions focus on rule-based systems and gazetteers, which depend on the completeness of the gazetteers and manually defined rules. Since people's writing styles differ significantly, manually defining rules will be complex. This paper introduces a neural network architecture based on BiLSTM, CRF, and attention mechanisms. It exploits statistical linguistic properties also. Compared to state-of-the-art methods, the model demonstrated superior results in both in- and cross-domain scenarios on tweet datasets representing diverse disaster types from different regions and times. Empirical results demonstrate that supervised systems can replace gazetteer-based solutions. BiLSTM and CRF, in conjunction with attention mechanism, improve the sequential modelling in informal text. Our system excels in non-English tweets also. The observations have applications in location-based services like tracking news events, traffic management, and event localization. [ABSTRACT FROM AUTHOR]

The accurate and automated segmentation of neurons in electron microscopy (EM) images presents a challenging problem in neuroscience. In this paper, we propose a deep learning (DL)-based neuron segmentation approach using a relatively shallow residual deconvolutional neural network (DNN) as the core. Our proposed segmentation framework differs from most EM image segmentation networks that use original images as input, as we suggest using modified morphological features with attention characteristics as input instead. We calculate Bhattacharyya distances in morphology feature space to provide attention features, which are then concatenated with the original image and used as input to the segmentation network. By attending to membrane pixels, our network efficiently highlights the discrimination between membrane boundaries and background pixels. To remove noise pixels in the segmentation map while preserving membrane boundaries in the final result, the dilated version of the output is then processed using a guided filtering scenario. Our proposed model outperforms several competing DL methods such as DeepLabv3 as well as different versions of deconvolutional neural networks on the ISBI 2012 dataset in terms of various image segmentation metrics. The proposed attention-based morphological guided framework can help advance research in neuroscience by providing an automated and accurate segmentation of neurons in EM images. [ABSTRACT FROM AUTHOR]

The human visual system has limited capacity in simultaneously processing multiple visual inputs. Consequently, humans rely on shifting their attention from one location to another. When viewing an image of complex scenes, psychology studies and behavioural observations show that humans prioritise and sequentially shift attention among multiple visual stimuli. In this paper, we propose to predict the saliency rank of multiple objects by inferring human attention shift. We first construct a new large-scale salient object ranking dataset, with the saliency rank of objects defined by the order that an observer attends to these objects via attention shift. We then propose a new deep learning-based model to leverage both bottom-up and top-down attention mechanisms for saliency rank prediction. Our model includes three novel modules: Spatial Mask Module (SMM), Selective Attention Module (SAM) and Salient Instance Edge Module (SIEM). SMM integrates bottom-up and semantic object properties to enhance contextual object features, from which SAM learns the dependencies between object features and image features for saliency reasoning. SIEM is designed to improve segmentation of salient objects, which helps further improve their rank predictions. Experimental results show that our proposed network achieves state-of-the-art performances on the salient object ranking task across multiple datasets. Code and data are available at https://github.com/SirisAvishek/Attention_Shift_Ranks. [ABSTRACT FROM AUTHOR]

Air pollution poses a significant threat to the health of the environment and human well-being. The air quality index (AQI) is an important measure of air pollution that describes the degree of air pollution and its impact on health. Therefore, accurate and reliable prediction of the AQI is critical but challenging due to the non-linearity and stochastic nature of air particles. This research aims to propose an AQI prediction hybrid deep learning model based on the Attention Convolutional Neural Networks (ACNN), Autoregressive Integrated Moving Average (ARIMA), Quantum Particle Swarm Optimization (QPSO)-enhanced-Long Short-Term Memory (LSTM) and XGBoost modelling techniques. Daily air quality data were collected from the official Seoul Air registry for the period 2021 to 2022. The data were first preprocessed through the ARIMA model to capture and fit the linear part of the data and followed by a hybrid deep learning architecture developed in the pretraining–finetuning framework for the non-linear part of the data. This hybrid model first used convolution to extract the deep features of the original air quality data, and then used the QPSO to optimize the hyperparameter for LSTM network for mining the long-terms time series features, and the XGBoost model was adopted to fine-tune the final AQI prediction model. The robustness and reliability of the resulting model were assessed and compared with other widely used models and across meteorological stations. Our proposed model achieves up to 31.13% reduction in MSE, 19.03% reduction in MAE and 2% improvement in R-squared compared to the best appropriate conventional model, indicating a much stronger magnitude of relationships between predicted and actual values. The overall results show that the attentive hybrid deep Quantum inspired Particle Swarm Optimization model is more feasible and efficient in predicting air quality index at both city-wide and station-specific levels. [ABSTRACT FROM AUTHOR]

The benefits of incorporating process information in a large-scale assessment with the complex micro-level evidence from the examinees (i.e., process log data) are well documented in the research across large-scale assessments and learning analytics. This study introduces a deep-learning-based approach to predictive modeling of the examinee's performance in sequential, interactive problem-solving tasks from a large-scale assessment of adults' educational competencies. The current methods disambiguate problem-solving behaviors using network analysis to inform the examinee's performance in a series of problem-solving tasks. The unique contribution of this framework lies in the introduction of an "effort-aware" system. The system considers the information regarding the examinee's task-engagement level to accurately predict their task performance. The study demonstrates the potential to introduce a high-performing deep learning model to learning analytics and examinee performance modeling in a large-scale problem-solving task environment collected from the OECD Programme for the International Assessment of Adult Competencies (PIAAC 2012) test in multiple countries, including the United States, South Korea, and the United Kingdom. Our findings indicated a close relationship between the examinee's engagement level and their problem-solving skills as well as the importance of modeling them together to have a better measure of students' problem-solving performance. [ABSTRACT FROM AUTHOR]