ECNet is an evolutionary context-integrated deep learning framework for protein engineering by Yunan Luo, Guangde Jiang, Tianhao Yu, Yang Liu, Lam Vo, Hantian Ding, Yufeng Su, Wesley Wei Qian, Huimin Zhao, Jian Peng

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Jumper enables discontinuous transcript assembly in coronaviruses by Palash Sashittal, Chuanyi Zhang, Jian Peng, Mohammed El-Kebir

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Unsupervised Word Embedding Learning by Incorporating Local and Global Contexts by Yu Meng, Jiaxin Huang, Guangyuan Wang, Zihan Wang, Chao Zhang, Jiawei Han

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Non-parametric correction of estimated gene trees using TRACTION by Sarah Christensen, Erin K. Molloy, Pranjal Vachaspati, Ananya Yammanuru, Tandy Warnow

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Correction to: The performance of coalescent-based species tree estimation methods under models of missing data by Michael Nute, Jed Chou, Erin K. Molloy, Tandy Warnow

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OCTAL: Optimal Completion of gene trees in polynomial time by Sarah Christensen, Erin K. Molloy, Pranjal Vachaspati, Tandy Warnow

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Typing tumors using pathways selected by somatic evolution by Sheng Wang, Jianzhu Ma, Wei Zhang, John Paul Shen, Justin Huang, Jian Peng, Trey Ideker

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BEDwARS: a robust Bayesian approach to bulk gene expression deconvolution with noisy reference signatures by Saba Ghaffari, Kelly J. Bouchonville, Ehsan Saleh, Remington E. Schmidt, Steven M. Offer, Saurabh Sinha

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SIESTA: enhancing searches for optimal supertrees and species trees by Pranjal Vachaspati, Tandy Warnow

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Unblended disjoint tree merging using GTM improves species tree estimation by Vladimir Smirnov, Tandy Warnow

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Dynamic transfer learning with progressive meta-task scheduler by Jun Wu, Jingrui He, Jingrui He

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Distinguishing linear and branched evolution given single-cell DNA sequencing data of tumors by Leah L. Weber, Mohammed El-Kebir

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Statistically consistent divide-and-conquer pipelines for phylogeny estimation using NJMerge by Erin K. Molloy, Tandy Warnow

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Implications of non-uniqueness in phylogenetic deconvolution of bulk DNA samples of tumors by Yuanyuan Qi, Dikshant Pradhan, Mohammed El-Kebir

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Next Decade’s AI-Based Drug Development Features Tight Integration of Data and Computation by Yunan Luo, Jian Peng, Jianzhu Ma

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Multi-modality machine learning predicting Parkinson’s disease by Mary B. Makarious, Hampton L. Leonard, Dan Vitale, Hirotaka Iwaki, Lana Sargent, Anant Dadu, Ivo Violich, Elizabeth Hutchins, David Saffo, Sara Bandres-Ciga, Jonggeol Jeff Kim, Yeajin Song, Melina Maleknia, Matt Bookman, Willy Nojopranoto, Roy H. Campbell, Sayed Hadi Hashemi, Juan A. Botia, John F. Carter, David W. Craig, Kendall Van Keuren-Jensen, Huw R. Morris, John A. Hardy, Cornelis Blauwendraat, Andrew B. Singleton, Faraz Faghri, Mike A. Nalls

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Moss enables high sensitivity single-nucleotide variant calling from multiple bulk DNA tumor samples by Chuanyi Zhang, Mohammed El-Kebir, Idoia Ochoa

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Identification and prediction of Parkinson’s disease subtypes and progression using machine learning in two cohorts by Anant Dadu, Vipul Satone, Rachneet Kaur, Sayed Hadi Hashemi, Hampton Leonard, Hirotaka Iwaki, Mary B. Makarious, Kimberley J. Billingsley, Sara Bandres‐Ciga, Lana J. Sargent, Alastair J. Noyce, Ali Daneshmand, Cornelis Blauwendraat, Ken Marek, Sonja W. Scholz, Andrew B. Singleton, Mike A. Nalls, Roy H. Campbell, Faraz Faghri

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Improving field boundary delineation in ResUNets via adversarial deep learning by Maxwell Jong, Kaiyu Guan, Sibo Wang, Yizhi Huang, Bin Peng

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A toolkit for constructing refactoring engines by Jeffrey Overbey

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