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Research: Analysis of a cell-free DNA–based cancer screening cohort links fragmentomic profiles, nuclease levels, and plasma DNA concentrations
- Yasine Malki,
- Guannan Kang,
- W.K. Jacky Lam,
- Qing Zhou,
- Suk Hang Cheng,
- Peter P.H. Cheung,
- Jinyue Bai,
- Ming Lok Chan,
- Chui Ting Lee,
- Wenlei Peng,
- Yiqiong Zhang,
- Wanxia Gai,
- Winsome W.S. Wong,
- Mary-Jane L. Ma,
- Wenshuo Li,
- Xinzhou Xu,
- Zhuoran Gao,
- Irene O.L. Tse,
- Huimin Shang,
- L.Y. Lois Choy,
- Peiyong Jiang,
- K.C. Allen Chan,
- and Y.M. Dennis Lo
Genome Res. January 2025 35: 31-42; Published in Advance November 27, 2024, doi:10.1101/gr.279667.124
...Analysis of a cell-free DNA–based cancer screening cohort links fragmentomic profiles, nuclease levels, and plasma DNA concentrations Yasine Malki1,2,3,5, Guannan Kang1,2,3,5, W.K. Jacky Lam1,2,3,4,5, Qing Zhou1,2,3, Suk Hang Cheng1,2,3, Peter P.H. Cheung2,3, Jinyue Bai1,2,3, Ming Lok Chan2,3, Chui...
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Method: Automated interpretable artificial intelligence genomic prediction with AIGP
- Lei Wei,
- Ziqin Jiang,
- Baoliang Fan,
- Yidan Yan,
- Zhenqiang Xu,
- Xiaoxiang Hu,
- and Yuzhe Wang
Genome Res. March 5, 2026 gr.281006.125; Published in Advance March 5, 2026, doi:10.1101/gr.281006.125
...remains a major genetic challenge. 21 Traditional statistical methods (such as GBLUP and BayesR) have limitations, including 22 reliance on artificial prior assumptions, and hard to capture epistatic effects. Machine learning 23 (ML) has emerged as a powerful alternative for genomic prediction; however...
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Method: Graph-based deep reinforcement learning for haplotype assembly with Ralphi
- Enzo Battistella,
- Anant Maheshwari,
- Barış Ekim,
- Bonnie Berger,
- and Victoria Popic
Genome Res. December 2025 35: 2617-2625; Published in Advance November 14, 2025, doi:10.1101/gr.280569.125
...-encoder for haplotype assembly and viral quasispecies reconstruction. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, New York, Vol. 34, pp. 719–726. AAAI Press, Palo Alto, CA. ↵Ke Z, Vikalo H. 2022. Deep learning for assembly of haplotypes and viral quasispecies from short and long sequencing reads...
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Corrigendum: Corrigendum: Machine learning identifies activation of RUNX/AP-1 as drivers of mesenchymal and fibrotic regulatory programs in gastric cancer
- Milad Razavi-Mohseni,
- Weitai Huang,
- Yu A. Guo,
- Dustin Shigaki,
- Shamaine Wei Ting Ho,
- Patrick Tan,
- Anders J. Skanderup,
- and Michael A. Beer
Genome Res. February 2026 36: 432; doi:10.1101/gr.281294.125
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Method: A somatic hypermutation–based machine learning model stratifies individuals with Crohn's disease and controls
- Modi Safra,
- Lael Werner,
- Ayelet Peres,
- Pazit Polak,
- Naomi Salamon,
- Michael Schvimer,
- Batia Weiss,
- Iris Barshack,
- Dror S. Shouval,
- and Gur Yaari
Genome Res. January 2023 33: 71-79; Published in Advance December 16, 2022, doi:10.1101/gr.276683.122
...sequencing experiments generate immense amounts of data, raising the need for new approaches to analyze and make data-driven recommendations, decisions, and predictions. Artificial intelligence (AI) and, specifically, machine learning (ML) approaches have been developed to integrate large amounts of data...
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Method: Spatial domain detection using contrastive self-supervised learning for spatial multi-omics technologies
- Jianing Yao,
- Jinglun Yu,
- Brian Caffo,
- Stephanie C. Page,
- Keri Martinowich,
- and Stephanie C. Hicks
Genome Res. July 2025 35: 1621-1632; Published in Advance May 20, 2025, doi:10.1101/gr.279380.124
..., Auerbach BJ, Li M. 2021b. Statistical and machine learning methods for spatially resolved transcriptomics with histology. Comput Struct Biotechnol J 19: 3829–3841. doi:10.1016/j.csbj.2021.06.052 ↵Huuki-Myers LA, Spangler A, Eagles NJ, Montgomery KD, Kwon SH, Guo B, Grant-Peters M, Divecha HR, Tippani M...
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Method: Inferring disease progression stages in single-cell transcriptomics using a weakly supervised deep learning approach
- Fabien Wehbe,
- Levi Adams,
- Jordan Babadoudou,
- Samantha Yuen,
- Yoon-Seong Kim,
- and Yoshiaki Tanaka
Genome Res. January 2025 35: 135-146; Published in Advance December 2, 2024, doi:10.1101/gr.278812.123
...and healthy donors and hinders identification of bona fide disease-associated gene expression patterns (Trapnell 2015).Deep learning is a type of artificial intelligence (AI) method that automatically recognizes feature trends and patterns from data sets and solves a complex classification and regression...
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Research: Machine learning identifies activation of RUNX/AP-1 as drivers of mesenchymal and fibrotic regulatory programs in gastric cancer
- Milad Razavi-Mohseni,
- Weitai Huang,
- Yu A. Guo,
- Dustin Shigaki,
- Shamaine Wei Ting Ho,
- Patrick Tan,
- Anders J. Skanderup,
- and Michael A. Beer
Genome Res. May 2024 34: 680-695; Published in Advance May 22, 2024, doi:10.1101/gr.278565.123
.... Using the ATAC-seq data, we developed a machine learning approach to determine the transcription factors (TFs) regulating the subtypes of GC. We identified TFs driving the mesenchymal (RUNX2, ZEB1, SNAI2, AP-1 dimer) and the epithelial (GATA4, GATA6, KLF5, HNF4A, FOXA2, GRHL2) states in GC. We...
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Method: Iterative improvement of deep learning models using synthetic regulatory genomics
- André M. Ribeiro-dos-Santos and
- Matthew T. Maurano
Genome Res. November 2025 35: 2539-2549; Published in Advance October 22, 2025, doi:10.1101/gr.280540.125
...), but direct biochemical assessment of each variant in every relevant cell type and state remains impractical.An alternative approach is to train machine learning models on reference genomic sequence and then apply them to genetic variation data. Recent machine learning models including gkmSVM (Lee et al. 2015...
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Method: Nematode gene annotation by machine-learning-assisted proteotranscriptomics enables proteome-wide evolutionary analysis
- Alejandro Ceron-Noriega,
- Miguel V. Almeida,
- Michal Levin,
- and Falk Butter
Genome Res. January 2023 33: 112-128; Published in Advance January 18, 2023, doi:10.1101/gr.277070.122
...of nematodes is believed to date back to ∼650–750 million years, generating a large and phylogenetically diverse group to be explored. However, for most species high-quality gene annotations are incomprehensive or missing. Combining short-read RNA sequencing with mass spectrometry–based proteomics and machine-learning...

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