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3. Machine learning emulation of high resolution inundation maps.

Author

Briseid Storrøsten, Erlend, Ragu Ramalingam, Naveen, Lorito, Stefano, Volpe, Manuela, Sánchez-Linares, Carlos, Løvholt, Finn, and Gibbons, Steven J

Subjects
*MACHINE learning, *FLOODS, *TSUNAMI hazard zones, *TSUNAMI warning systems, *TSUNAMIS, *EARTHQUAKES, *EPISTEMIC uncertainty, *WATER depth
Abstract

Estimating coastal tsunami impact for early-warning or long-term hazard analysis requires the calculation of inundation metrics such as flow-depth or momentum flux. Both applications require the simulation of large numbers of scenarios to capture both the aleatory variability and the epistemic tsunami uncertainty. A computationally demanding step in simulating inundation is solving the non-linear shallow water (NLSW) equations on meshes with sufficiently high resolution to represent the local elevation accurately enough to capture the physics governing the flow. This computational expense is particularly challenging in the context of Tsunami Early Warning where strict time constraints apply. A machine learning (ML) model that predicts inundation maps from offshore simulation results with acceptable accuracy, trained on an acceptably small training set of full simulations, could replace the computationally expensive NLSW part of the simulations for vast numbers of scenarios and predict inundation rapidly and with reduced computational demands. We consider the application of an encoder–decoder based neural network to predict high-resolution inundation maps based only on more cheaply calculated simulated time-series at a limited number of offshore locations. The network needs to be trained using input offshore time-series and the corresponding inundation maps from previously calculated full simulations. We develop and evaluate the ML model on a comprehensive set of inundation simulations for the coast of eastern Sicily for tens of thousands of subduction earthquake sources in the Mediterranean Sea. We find good performance for this case study even using relatively small training sets (order of hundreds) provided that appropriate choices are made in the specification of model parameters, the specification of the loss function and the selection of training events. The uncertainty in the prediction for any given location decreases with the number of training events that inundate that location, with a good range of flow depths needed for accurate predictions. This means that care is needed to ensure that rarer high-inundation scenarios are well-represented in the training sets. The importance of applying regularization techniques increases as the size of the training sets decreases. The computational gain of the proposed methodology depends on the number of complete simulations needed to train the neural network, ranging between 164 and 4196 scenarios in this study. The cost of training the network is small in comparison with the cost of the numerical simulations and, for an ensemble of around 28 000 scenarios, this represents a 6- to 170-fold reduction in computing costs. [ABSTRACT FROM AUTHOR]

Published
2024
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4. Advancing nearshore and onshore tsunami hazard approximation with machine learning surrogates.

Author

Ragu Ramalingam, Naveen, Johnson, Kendra, Pagani, Marco, and Martina, Mario L. V.

Subjects
TSUNAMIS, TSUNAMI warning systems, THEORY of wave motion, DATABASES, RISK assessment, HAZARDS
Abstract

Probabilistic tsunami hazard and risk assessment (PTHA and PTRA) are vital methodologies for computing tsunami risk and prompt measures to mitigate impacts. At large regional scales, their use and scope are currently limited by the computational costs of numerically intensive simulations behind them, which may be feasible only with advanced computational resources like high-performance computing (HPC) and may still require reductions in resolution, number of scenarios modelled, or use of simpler approximation schemes. To conduct PTHA and PTRA for large proportions of the coast, we therefore need to develop concepts and algorithms for reducing the number of events simulated and for more efficiently approximating the needed simulation results. This case study for a coastal region of Tohoku, Japan, utilises a limited number of tsunami simulations from submarine earthquakes along the subduction interface to build a wave propagation and inundation database and fits these simulation results through a machine learning-based variational encoder-decoder model. This is used as a surrogate to predict the tsunami waveform at the coast and the maximum inundation depths onshore at the different test sites. The performance of the surrogate models was assessed using a 5-fold cross validation assessment across the simulation events. Further to understand its real world performance and test the generalisability of the model, we used 5 very different tsunami source models from literature for historic events to further benchmark the model and understand its current deficiencies. [ABSTRACT FROM AUTHOR]

Published
2024
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5. Blood RNA alternative splicing events as diagnostic biomarkers for infectious disease

Author

Zhang, Zijun, primary, Sauerwald, Natalie, additional, Cappuccio, Antonio, additional, Ramos, Irene, additional, Nair, Venugopalan D., additional, Nudelman, German, additional, Zaslavsky, Elena, additional, Ge, Yongchao, additional, Gaitas, Angelo, additional, Ren, Hui, additional, Brockman, Joel, additional, Geis, Jennifer, additional, Ramalingam, Naveen, additional, King, David, additional, McClain, Micah T., additional, Woods, Christopher W., additional, Henao, Ricardo, additional, Burke, Thomas W., additional, Tsalik, Ephraim L., additional, Goforth, Carl W., additional, Lizewski, Rhonda A., additional, Lizewski, Stephen E., additional, Weir, Dawn L., additional, Letizia, Andrew G., additional, Sealfon, Stuart C., additional, and Troyanskaya, Olga G., additional

Published
2023
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6. C1 CAGE detects transcription start sites and enhancer activity at single-cell resolution

Author

Kouno, Tsukasa, Moody, Jonathan, Kwon, Andrew Tae-Jun, Shibayama, Youtaro, Kato, Sachi, Huang, Yi, Böttcher, Michael, Motakis, Efthymios, Mendez, Mickaël, Severin, Jessica, Luginbühl, Joachim, Abugessaisa, Imad, Hasegawa, Akira, Takizawa, Satoshi, Arakawa, Takahiro, Furuno, Masaaki, Ramalingam, Naveen, West, Jay, Suzuki, Harukazu, Kasukawa, Takeya, Lassmann, Timo, Hon, Chung-Chau, Arner, Erik, Carninci, Piero, Plessy, Charles, and Shin, Jay W.

Published
2019
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7. Marker-free characterization of full-length transcriptomes of single live circulating tumor cells

Author

Poonia, Sarita, primary, Goel, Anurag, additional, Chawla, Smriti, additional, Bhattacharya, Namrata, additional, Rai, Priyadarshini, additional, Lee, Yi Fang, additional, Yap, Yoon Sim, additional, West, Jay, additional, Bhagat, Ali Asgar, additional, Tayal, Juhi, additional, Mehta, Anurag, additional, Ahuja, Gaurav, additional, Majumdar, Angshul, additional, Ramalingam, Naveen, additional, and Sengupta, Debarka, additional

Published
2022
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10. Marker-free characterization of full-length transcriptomes of single live circulating tumor cells

Author

Poonia, Sarita, Goel, Anurag, Chawla, Smriti, Bhattacharya, Namrata, Rai, Priyadarshini, Lee, Yi Fang, Yap, Yoon Sim, West, Jay, Bhagat, Ali Asgar, Tayal, Juhi, Mehta, Anurag, Ahuja, Gaurav, Majumdar, Angshul, Ramalingam, Naveen, and Sengupta, Debarka

Abstract

The identification and characterization of circulating tumor cells (CTCs) are important for gaining insights into the biology of metastatic cancers, monitoring disease progression, and medical management of the disease. The limiting factor in the enrichment of purified CTC populations is their sparse availability, heterogeneity, and altered phenotypes relative to the primary tumor. Intensive research both at the technical and molecular fronts led to the development of assays that ease CTC detection and identification from peripheral blood. Most CTC detection methods based on single-cell RNA sequencing (scRNA-seq) use a mix of size selection, marker-based white blood cell (WBC) depletion, and antibodies targeting tumor-associated antigens. However, the majority of these methods either miss out on atypical CTCs or suffer from WBC contamination. We present unCTC, an R package for unbiased identification and characterization of CTCs from single-cell transcriptomic data. unCTC features many standard and novel computational and statistical modules for various analyses. These include a novel method of scRNA-seq clustering, named deep dictionary learning using k-means clustering cost (DDLK), expression-based copy number variation (CNV) inference, and combinatorial, marker-based verification of the malignant phenotypes. DDLK enables robust segregation of CTCs and WBCs in the pathway space, as opposed to the gene expression space. We validated the utility of unCTC on scRNA-seq profiles of breast CTCs from six patients, captured and profiled using an integrated ClearCell FX and Polaris workflow that works by the principles of size-based separation of CTCs and marker-based WBC depletion.

Published
2023
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12. Modeling expression ranks for noise-tolerant differential expression analysis of scRNA-seq data

Author

Gupta, Krishan, primary, Lalit, Manan, additional, Biswas, Aditya, additional, Sanada, Chad D., additional, Greene, Cassandra, additional, Hukari, Kyle, additional, Maulik, Ujjwal, additional, Bandyopadhyay, Sanghamitra, additional, Ramalingam, Naveen, additional, Ahuja, Gaurav, additional, Ghosh, Abhik, additional, and Sengupta, Debarka, additional

Published
2021
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13. Erratum: Iyer, A., et al. Integrative Analysis and Machine Learning Based Characterization of Single Circulating Tumor Cells. J. Clin. Med. 2020, 9, 1206

Author

Iyer, Arvind, primary, Gupta, Krishan, additional, Sharma, Shreya, additional, Hari, Kishore, additional, Lee, Yi, additional, Ramalingam, Neevan, additional, Yap, Yoon, additional, West, Jay, additional, Bhagat, Ali, additional, Subramani, Balaram, additional, Sabuwala, Burhanuddin, additional, Zea Tan, Tuan, additional, Thiery, Jean, additional, Jolly, Mohit, additional, Ramalingam, Naveen, additional, and Sengupta, Debarka, additional

Published
2021
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14. Integrative Analysis and Machine Learning Based Characterization of Single Circulating Tumor Cells

Author

Iyer, Arvind, primary, Gupta, Krishan, additional, Sharma, Shreya, additional, Hari, Kishore, additional, Lee, Yi, additional, Ramalingam, Neevan, additional, Yap, Yoon, additional, West, Jay, additional, Bhagat, Ali, additional, Subramani, Balaram, additional, Sabuwala, Burhanuddin, additional, Tan, Tuan, additional, Thiery, Jean, additional, Jolly, Mohit, additional, Ramalingam, Naveen, additional, and Sengupta, Debarka, additional

Published
2020
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15. Abstract 2923: Label-free enrichment and integrated full-length mRNA transcriptome analysis of single live circulating tumor cells from breast cancer patients

Author

Ramalingam, Naveen, primary, Lee, Yi Fang, additional, Szpankowski, Lukasz, additional, Leyrat, Anne, additional, Fowler, Brian, additional, Tan, Jovina, additional, Lu, Chong Tracy, additional, Angeles, Ninez Delos, additional, Sanada, Chad, additional, Greene, Cassandra, additional, Hukari, Kyle, additional, Wu, Andrew, additional, Yap, Yoon-Sim, additional, West, Jay, additional, and Bhagat, Ali Asgar, additional

Published
2017
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17. Corrigendum: Fluidic Logic Used in a Systems Approach to Enable Integrated Single-Cell Functional Analysis

Author

Ramalingam, Naveen, primary, Fowler, Brian, additional, Szpankowski, Lukasz, additional, Leyrat, Anne A., additional, Hukari, Kyle, additional, Maung, Myo Thu, additional, Yorza, Wiganda, additional, Norris, Michael, additional, Cesar, Chris, additional, Shuga, Joe, additional, Gonzales, Michael L., additional, Sanada, Chad D., additional, Wang, Xiaohui, additional, Yeung, Rudy, additional, Hwang, Win, additional, Axsom, Justin, additional, Devaraju, Naga Sai Gopi Krishna, additional, Angeles, Ninez Delos, additional, Greene, Cassandra, additional, Zhou, Ming-Fang, additional, Ong, Eng-Seng, additional, Poh, Chang-Chee, additional, Lam, Marcos, additional, Choi, Henry, additional, Htoo, Zaw, additional, Lee, Leo, additional, Chin, Chee-Sing, additional, Shen, Zhong-Wei, additional, Lu, Chong T., additional, Holcomb, Ilona, additional, Ooi, Aik, additional, Stolarczyk, Craig, additional, Shuga, Tony, additional, Livak, Kenneth J., additional, Larsen, Cate, additional, Unger, Marc, additional, and West, Jay A. A., additional

Published
2017
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18. Fluidic Logic Used in a Systems Approach to Enable Integrated Single-Cell Functional Analysis

Author

Ramalingam, Naveen, primary, Fowler, Brian, additional, Szpankowski, Lukasz, additional, Leyrat, Anne A., additional, Hukari, Kyle, additional, Maung, Myo Thu, additional, Yorza, Wiganda, additional, Norris, Michael, additional, Cesar, Chris, additional, Shuga, Joe, additional, Gonzales, Michael L., additional, Sanada, Chad D., additional, Wang, Xiaohui, additional, Yeung, Rudy, additional, Hwang, Win, additional, Axsom, Justin, additional, Devaraju, Naga Sai Gopi Krishna, additional, Angeles, Ninez Delos, additional, Greene, Cassandra, additional, Zhou, Ming-Fang, additional, Ong, Eng-Seng, additional, Poh, Chang-Chee, additional, Lam, Marcos, additional, Choi, Henry, additional, Htoo, Zaw, additional, Lee, Leo, additional, Chin, Chee-Sing, additional, Shen, Zhong-Wei, additional, Lu, Chong T., additional, Holcomb, Ilona, additional, Ooi, Aik, additional, Stolarczyk, Craig, additional, Shuga, Tony, additional, Livak, Kenneth J., additional, Unger, Marc, additional, and West, Jay A. A., additional

Published
2016
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19. Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex

Author

Pollen, Alex A, primary, Nowakowski, Tomasz J, additional, Shuga, Joe, additional, Wang, Xiaohui, additional, Leyrat, Anne A, additional, Lui, Jan H, additional, Li, Nianzhen, additional, Szpankowski, Lukasz, additional, Fowler, Brian, additional, Chen, Peilin, additional, Ramalingam, Naveen, additional, Sun, Gang, additional, Thu, Myo, additional, Norris, Michael, additional, Lebofsky, Ronald, additional, Toppani, Dominique, additional, Kemp, Darnell W, additional, Wong, Michael, additional, Clerkson, Barry, additional, Jones, Brittnee N, additional, Wu, Shiquan, additional, Knutsson, Lawrence, additional, Alvarado, Beatriz, additional, Wang, Jing, additional, Weaver, Lesley S, additional, May, Andrew P, additional, Jones, Robert C, additional, Unger, Marc A, additional, Kriegstein, Arnold R, additional, and West, Jay A A, additional

Published
2014
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