Regular papers

• Submodular sketches of single-cell RNA-seq measurements: Wei Yang, Jeff Bilmes, William Stafford Noble
• Unsupervised manifold alignment for single-cell multi-omics data: Ritambhara Singh, Pinar Demetci, Giancarlo Bonora, Vijay Ramani, Choli Lee, He Fang, Zhijun Duan, Xinxian Deng, Jay Shendure, Christine Disteche, William S. Noble
• Augmenting Signaling Pathway Reconstructions: Tobias Rubel, Anna Ritz
• Zero-shot imputations across species are enabled through joint modeling of human and mouse epigenomics: Jacob Schreiber, Deepthi Hegde, William Noble
• A Qualitative Evaluation of Language Models on Automatic Question-Answering for COVID-19: David Oniani, Yanshan Wang
• Integrative Deep Learning for PanCancer Molecular Subtype Classification Using Histopathological Images and RNAseq Data: Fatima Zare, Javad Noorbakhsh, Tianyu Wang, Jeffery Chuang, Sheida Nabavi
• Joint Grid Discretization for Biological Pattern Discovery: Jiandong Wang, Sajal Kumar, Joe Song
• Identifying Evolutionary Origins of Repeat Domains in Protein Families: Chaitanya Aluru, Mona Singh
• Ir-Man: An Information Retrieval Framework for Marine Animal Necropsy Analysis: Alexander Carmichael, Deepayan Bhowmik, Johanna Baily, Andrew Brownlow, George Gunn, Aaron Reeves
• Global Surveillance of COVID-19 by mining news media using a multi-source dynamic embedded topic model: Yue Li, Pratheeksha Nair, Zhi Wen, Imane Chafi, Anya Okhmatovskaia, Guido Powell, David Buckeridge
• Performance Evaluation of Viral Infection Diagnosis using T-Cell Receptor Sequence and Artificial Intelligence: Tim Kosfeld, Jonathan McMillan, Richard DiPaolo, Jie Hou, Tae-Hyuk Ahn
• Modularity Analysis of Bipartite Networks and Multivariate ANOVA for Identification of Differentially Expressed Proteins in a Mouse Model of Down Syndrome: Ali Jazayeri, Sara Pajouhanfar, Sadaf Saba, Christopher Yang
• TTSurv: Integration of Multi-Omics Data Using Multi-Stage Transfer Learning for Lung Cancer Prognosis Prediction: Yixing Jiang, Kristen Alford, Frank Ketchum, Li Tong, May D. Wang
• Efficiently mining rich subgraphs from vertex-attributed graphs: Riyad Hakim, Saeed Salem
• Predicting protein secondary structure by an ensemble through feature-based accuracy estimation: Spencer Krieger, John Kececioglu
• A Supervised Machine Learning Approach for Distinguishing Between Additive and Replacing Horizontal Gene Transfers: Abhijit Mondal, Misagh Kordi, Mukul S. Bansal
• Multi-Site Assessment of Pediatric Bone Age Using Deep Learning: Aly Valliani, John Schwartz, Varun Arvind, Jun Kim, Samuel Cho
• Automated Classification of Acute Rejection from Endomyocardial Biopsies: Felipe Giuste, Mythreye Venkatesan, Conan Zhao, Li Tong, Yuanda Zhu, Shriprasad Deshpande, May Wang
• ELMV: a Ensemble-Learning Approach for Analyzing Electrical Health Records with Significant Missing Values: Lucas Jing Liu, Hongwei Zhang, Jianzhong Di, Jin Chen
• Functional Enrichment Analysis of Deregulated Long Non-Coding RNAs in Cancer Based on their Genomic Neighbors: Gulden Olgun, Oznur Tastan
• CTDPathSim: Cell line-tumor deconvoluted pathway- based similarity in the context of precision medicine in cancer: Banabithi Bose, Serdar Bozdag
• Cross-Global Attention Graph Kernel Network Prediction of Drug Prescription: Hao-Ren Yao, Der-Chen Chang, Ophir Frieder, Wendy Huang, I-Chia Liang, Chi-Feng Hung
• Collaborative Cloud Computing Framework for Health Data with Open Source Technologies: Fatemeh Rouzbeh, Ananth Grama, Paul Griffin, Mohammad Adibuzzaman
• MeSH Indexing Using the Biomedical Citation Network: William Gasper, Dario Ghersi, Parvathi Chundi
• Deep Ranking in Template-free Protein Structure Prediction: Xiao Chen, Nasrin Akhter, Zhiye Guo, Tianqi Wu, Jie Hou, Amarda Shehu, Jianlin Cheng
• Variational Autoencoders for Protein Structure Prediction: Fardina Alam, Amarda Shehu
• GPU-Accelerated Drug Discovery with Docking on the Summit Supercomputer: Porting, Optimization, and Application to COVID-19 Research: Scott Le Grand, Aaron Scheinberg, Andreas Tillack, Mathialakan Thavappiragasam, Josh Vermaas, Rupesh Agarwal, Jeff Larkin, Duncan Poole, Diogo Santos-Martins, Leonardo Solis-Vasquez, Andreas Koch, Stefano Forli, Oscar Hernandez, Jeremy Smith, Ada Sedova
• Protein evolution is structure dependent and non-homogeneous across the tree of life: Akanksha Pandey, Edward Braun
• An Integer Linear Programming Solution for the Most Parsimonious Reconciliation Problem under the Duplication-Loss-Coalescence Model: Morgan Carothers, Joseph Gardi, Gianluca Gross, Tatsuki Kuze, Nuo Liu, Fiona Plunkett, Julia Qian, Yi-Chieh Wu
• Abstract Mining: Ellie Small, Javier Cabrera, John Kostis
• Bio-JOIE: Joint Representation Learning of Biological Knowledge Bases: Junheng Hao, Chelsea J.-T. Ju, Muhao Chen, Yizhou Sun, Carlo Zaniolo, Wei Wang
• A deep learning fusion model for brain disorder classification: Application to distinguishing schizophrenia and autism spectrum disorder: Yuhui Du, Bang Li, Yuliang Hou, Vince D Calhoun
• A Generalized Robinson-Foulds Distance for Clonal Trees, Mutation Trees, and Phylogenetic Trees and Networks: Mercè Llabrés, Francesc Rosselló, Gabriel Valiente
• Staging Epileptogenesis with Deep Neural Networks: Diyuan Lu, Sebastian Bauer, Valentin Neubert, Lara Sophie Costard, Felix Rosenow, Jochen Triesch
• The impact of sample size and tissue type on the reproducibility of gene co-expression networks: Katie Ovens, Brian Eames, Ian McQuillan
• A Preliminary Investigation in the Molecular Basis of Host Shutoff Mechanism in SARS-CoV: Niharika Pandala, Casey A. Cole, Devaun McFarland, Anita Nag, Homayoun Valafar
• Linearization of Ancestral Genomes with Duplicated Genes: Pavel Avdeyev, Max Alekseyev
• Three Co-expression Pattern Types across Microbial Transcriptional Networks of Plankton in Two Oceanic Waters: Ruby Sharma, Xuye Luo, Sajal Kumar, Joe Song
• Rhabdomyosarcoma Histology Classification using Ensemble of Deep Learning Networks: Saloni Agarwal, Mohamedelfatih Eltigani Osman Abaker, Xinyi Zhang, Ovidiu Daescu, Donald A. Barkauskas, Erin R Rudzinski, Patrick Leavey
• GANDALF: Peptide Generation for Drug Design using Sequential and Structural Generative Adversarial Networks: Allison Rossetto, Wenjin Zhou

Short papers

• A multi-context feature learning approach to identify disease-specific gene neighborhoods: Sudhir Ghandikota, Anil Jegga
• MinIsoClust: Isoform clustering using minhash and locality sensitive hashing: Sairam Behera, Jitender S. Deogun, Etsuko N. Moriyama
• Smart Computational Approaches with Advanced Feature Selection Algorithms for Optimizing the Classification of Mobility Data in Health Informatics: Elham Rastegari, Donovan Orn, Hesham Ali
• A Novel Pupillometric-Based Application for the Automated Diagnosis of ADHD Using Machine Learning: William Das, Shubh Khanna
• EXAM: An Explainable Attention-based Model for COVID-19 Automatic Diagnosis: Wenqi Shi, Li Tong, Yuchen Zhuang, Yuanda Zhu, May Wang
• Translocator: local realignment and global remapping enabling accurate translocation detection using single-molecule sequencing long reads: Ye Wu, Ruibang Luo, Tak-Wah Lam, Hing-Fung Ting, Junwen Wang
• Re-balancing Variational Autoencoder Loss for Molecule Sequence Generation: Chaochao Yan, Sheng Wang, Jinyu Yang, Tingyang Xu, Junzhou Huang
• Fusion Lasso and Its Applications to Cancer Subtype and Stage Prediction: Zhong Chen, Andrea Edwards, Kun Zhang
• Correlation Imputation in Single cell RNA-seq using Auxiliary Information and Ensemble Learning: Luqin Gan, Giuseppe Vinci, Genevera Allen
• Predicting Criticality in COVID-19 Patients: Roger Hallman, Anjali Chikkula, Temiloluwa Prioleau
• A Dynamics-based Approach for the Target Control of Boolean Networks: Cui Su, Jun Pang
• Characterization of S. cerevisiae Protein Complexes by Representative DDI Graph Planarity: William Gasper, Kate Cooper, Nathan Cornelius, Sanjukta Bhowmick, Hesham Ali
• Transforming the Language of Life: Transformer Neural Networks for Protein Prediction Tasks: Ananthan Nambiar, Maeve Heflin, Simon Liu, Sergei Maslov, Mark Hopkins, Anna Ritz
• HMSC: a Hybrid Metagenomic Sequence Classification Algorithm: Subrata Saha, Zigeng Wang, Sanguthevar Rajasekaran
• CNN Based Segmentation of Infarcted Regions in Acute Cerebral Stroke Patients From Computed Tomography Perfusion Imaging: Luca Tomasetti, Kjersti Engan, Mahdieh Khanmohammadi, Kathinka Dæhli Kurz
• Population-scale Genomic Data Augmentation Based on Conditional Generative Adversarial Networks: Junjie Chen, Mohammad Mowlaei, Xinghua Shi
• Structural representations of DNA regulatory substrates can enhance sequence-based algorithms by associating functional sequence variants: Jan Zrimec

Highlight papers

• Pairwise Versus Multiple Global Network Alignment: Vipin Vijayan, Shawn Gu, Eric Krebs, Lei Meng, Tijana Milenkovic
• iSOM-GSN: An Integrative Approach for Transforming Multi-omic Data into Gene Similarity Networks via Self-organizing Maps: Luis Rueda, Nazia Fatima
• NeTFactor, a framework for identifying transcriptional regulators of gene expression-based biomarkers: Mehmet Eren Ahsen, Yoojin Chun, Alexander Grishin, Galina Grishina, Gustavo Stolovitzky, Gaurav Pandey, Supinda Bunyavanich
• Objective risk stratification of prostate cancer using machine learning and radiomics applied to multiparametric magnetic resonance images: Bino Varghese, Frank Chen, Darryl Hwang, Suzanne Palmer, Andre Luis De Castro Abreu, Osamu Ukimura, Monish Aron, Manju Aron, Inderbir Gill, Vinay Duddalwar, Gaurav Pandey
• Benchmarking of computational error-correction methods for next-generation sequencing data: Jaqueline J. Brito, Serghei Mangul
• SMART: SuperMaximal Approximate Repeats Tool: Lorraine Ayad, Panagiotis Charalampopoulos, Solon Pissis
• DeCoDe: degenerate codon design for complete protein-coding DNA libraries: Tyler Shimko, Polly Fordyce, Yaron Orenstein
• Boosting the accuracy of protein secondary structure prediction through nearest neighbor search and method hybridization: Spencer Krieger, John Kececioglu
• How to build regulatory networks from single-cell gene expression data?: Aditya Pratapa, Amogh Jalihal, Jeffrey Law, Aditya Bharadwaj, T. M. Murali
• Processing millions of single cells by SHARP: Shibiao Wan, Junil Kim, Kyoung Jae Won
• Avocado: Deep tensor factorization characterizes the human epigenome via imputation of tens of thousands of functional experiments: Jacob Schreiber, Timothy Durham, Jeffrey Bilmes, William Noble