Lab Publications

Pre-prints

Michal Golovanevsky, Carsten Eickhoff, and R. Singh. “Multimodal Attention-based Deep Learning for Alzheimer’s Disease Diagnosis.” [Code]

Quang Huy Tran, Hicham Janati, Nicolas Courty, Rémi Flamary, Ievgen Redko, Pinar Demetci, and R. Singh (ordering based on contribution). “Unbalanced CO-Optimal Transport.”

Suchen Zheng*, Nitya Thakkar*, Hannah L Harris, Megan Zhang, Susanna Liu, Mark Gerstein, Erez Lieberman Aiden, Jordan Rowley, William S Noble, Gamze Gursoy, and R. Singh. “Predicting A/B compartments from histone modifications using deep learning.” [Code]

Ghulam Murtaza, Atishay Jain, Madeline Hughes, Thulasi Varatharajan, and R. Singh. “Investigating the performance of deep learning methods for Hi-C resolution improvement.” [Code]

Charlotte Guetta-Terrier, David Karambizi, Bedia Akosman, Jia-Shu Chen, Suchitra Kamle, J Eduardo Fajardo, Andras Fiser, R. Singh, Steven A Toms, Chun Geun Lee, Jack A Elias, Nikos Tapinos. “Chi3l1 is a modulator of glioma stem cell states and a therapeutic vulnerability for glioblastoma.”


2022

Camillo Saueressig, Adam Berkley, Reshma Munbodh, and R. Singh. “A Joint Graph and Image Convolution Network for Automatic Brain Tumor Segmentation“, Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Lecture Notes in Computer Science (LNCS, Springer). [Code]

Pinar Demetci, Rebecca Santorella, Björn Sandstede, and R. Singh. “Unsupervised integration of single-cell multi-omics datasets with disparities in cell-type representation“, International Conference on Research in Computational Molecular Biology (RECOMB). [Website][Code]

Lucas Paulo de Lima Camillo, Louis R Lapierre, and R. Singh. “AltumAge: A Pan-Tissue DNA-Methylation Epigenetic Clock Based on Deep Learning“, npj Aging. [Code]

Jeremy Bigness, Xavi Loinaz, Shalin Patel, Erica Larschan, and R. Singh. “Integrating long-range regulatory interactions to predict gene expression using graph convolutional networks“, Journal of Computational Biology. [Talk][Code]

Chris K. Kim, Ji Whae Choi, and others. “An automated pipeline for rapid triage of COVID-19 patients using artificial
intelligence based on chest radiographs and clinical data
“, npj Digital Medicine.

2021

Giancarlo Bonora, Vijay Ramani, R. Singh, He Fang, Dana Jackson, Sanjay Srivatsan, Ruolan Qiu, Choli Lee, Cole Trapnell, Jay Shendure, Zhijun Duan, Xinxian Deng, William S. Noble, and Christine M. Disteche. “Single-cell landscape of nuclear configuration and gene expression during stem cell differentiation and X inactivation“, Genome Biology.

Pinar Demetci*, Rebecca Santorella*, Björn Sandstede, William S. Noble, and R. Singh. Gromov-Wasserstein optimal transport to align single-cell multi-omics data.”, International Conference on Research in Computational Molecular Biology (RECOMB), Journal of Computational Biology (Special RECOMB Issue).
[Website][Code]

Ashley Mae Conard, Nathaniel Goodman, Yanhui Hu, Norbert Perrimon, R. Singh, Charles Lawrence, and Erica Larschan. “TIMEOR: a web-based tool to uncover temporal regulatory mechanisms from multi-omics data“, Nucleic Acids Research. [Website]

Jacob Schreiber and R.Singh. “Machine learning for profile prediction in genomics“, Current Opinion in Chemical Biology.

Camillo Saueressig, Adam Berkley, Ellliot Kang, Reshma Munbodh, and R. Singh. “Exploring graph-based neural networks for automatic brain tumor segmentation.”, From Data to Models and Back, Lecture Notes in Computer Science (LNCS, Springer).

2020

R. Singh, Pinar Demetci, Giancarlo Bonora, Vijay Ramani, Choli Lee, He Fang, Zhijun Duan, Xinxian Deng, Jay Shendure, Christine Disteche, and William S. Noble. “Unsupervised manifold alignment for single-cell multi-omics data“, ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM BCB)
[Talk][Code]

Jacob Schreiber, R.Singh, Jeffrey Bilmes and William S. Noble. “A pitfall for machine learning methods aiming to predict across cell types“, Genome Biology

Derek Blakely, Eamon Collins, R. Singh, Andrew Norton, Jack Lanchantin, and Yanjun Qi. “FastSK: Fast Sequence Analysis with Gapped String Kernels“, Bioinformatics (presented at the European Conference on Computational Biology (ECCB)) [Code]

Legend:
* Equal Contribution
† Co-corresponding Authors
Underlined names are undergraduate student members of our lab.