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Method: Matrix sketching framework for linear mixed models in association studies
- Myson Burch,
- Aritra Bose,
- Gregory Dexter,
- Laxmi Parida,
- and Petros Drineas
Genome Res. September 2024 34: 1304-1311; Published in Advance September 4, 2024, doi:10.1101/gr.279230.124
...++ with Intel's OpenMPI supporting libraries.MethodsMixed-model associationLMMs are formed using the following simple linear model: (1) where is the measured phenotype (response); is the matrix of the k covariates (e.g., principal components, age, sex, etc.) with the corresponding vector of fixed effects β ∈ ℝk...
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Method: Automated interpretable artificial intelligence genomic prediction with AlGP
- Lei Wei,
- Ziqin Jiang,
- Baoliang Fan,
- Yidan Yan,
- Zhenqiang Xu,
- Xiaoxiang Hu,
- and Yuzhe Wang
Genome Res. March 5, 2026 ; Published in Advance March 5, 2026, doi:10.1101/gr.281006.125
...(predictive phenotype) because of their “black-box” nature (Novakovsky et al. 2023). For genomic researchers, interpretative information, which is often lacking, can bemore valuable than predictions themselves because it can provide new insights into genetic processes. Traditional linear models, such as -wide...
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Method: Interpretable phenotype decoding from multicondition sequencing data with ALPINE
- Wei-Hao Lee,
- Lechuan Li,
- Ruth Dannenfelser,
- and Vicky Yao
Genome Res. December 2025 35: 2756-2769; Published in Advance October 22, 2025, doi:10.1101/gr.280566.125
...to link genes with conditions, requiring the user to essentially reconstitute predicted counts and perform differential gene expression. In contrast, scParser (Zhao et al. 2024) adopts a matrix factorization framework combined with sparse representation learning to model multiple conditions, but because...
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Method: Incorporating valuable prior knowledge to improve deep learning prediction of genetic perturbation responses
- Xiuhao Fu,
- Chao Yang,
- Chunyan Cui,
- Aoyun Geng,
- Yidi Sun,
- Chao Zha,
- Feifei Cui,
- Leyi Wei,
- Quan Zou,
- Xin Gao,
- and Zilong Zhang
Genome Res. March 26, 2026 gr.281523.125; Published in Advance March 26, 2026, doi:10.1101/gr.281523.125
...demonstrates a consistent performance advantage across most datasets and metrics, 190 indicating that deep learning models can still provide additional predictive power in 191 this context. A direct comparison between PRIM and the linear model is provided in 192 11 11 Supplemental Fig S7A. Furthermore, we...
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Method: Modeling gene interactions in polygenic prediction via geometric deep learning
- Han Li,
- Jianyang Zeng,
- Michael P. Snyder,
- and Sai Zhang
Genome Res. January 2025 35: 178-187; Published in Advance November 19, 2024, doi:10.1101/gr.279694.124
..., these methods only take a limited number of variants as their input and also lack the integration of prior knowledge regarding biological complexity. Indeed, existing nonlinear PRSs have demonstrated either comparable or, in many cases, inferior performance in predicting phenotypes compared with linear models...
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Method: Early feature extraction drives model performance in high-resolution chromatin accessibility prediction
- Aayush Grover,
- Till Muser,
- Liine Kasak,
- Lin Zhang,
- Ekaterina Krymova,
- and Valentina Boeva
Genome Res. March 2026 36: 619-629; Published in Advance January 12, 2026, doi:10.1101/gr.281042.125
..., or attention mechanisms, drive performance in these high-resolution predictions. To address these knowledge gaps, we systematically evaluate classic architectural choices and introduce ConvNeXt V2 blocks, originally developed for computer vision, as high-resolution feature extractors in deep learning models...
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Resource: Pangenome-genotyped structural variation improves molecular phenotype mapping in cattle
- Alexander S. Leonard,
- Xena M. Mapel,
- and Hubert Pausch
Genome Res. February 2024 34: 300-309; Published in Advance February 14, 2024, doi:10.1101/gr.278267.123
...Pan-genotyped structural variation improves molecular phenotype mapping in cattle Alexander S. Leonard, Xena M. Mapel and Hubert Pausch Animal Genomics, ETH Zurich, 8092 Zurich, Switzerland Corresponding authors: alleonard@ethz.ch, hubert.pausch@usys.ethz.chAbstractExpression and splicing...
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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
...) accessibility is shown by a line and colored by direction of difference. Predicted expression was scaled to WT using a linear regression fitted to all payload examples in Figure 1B.Finally, to explore performance of deep learning models on sequences even further diverged from the reference , we investigated...
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Method: Parameter-efficient fine-tuning on large protein language models improves signal peptide prediction
- Shuai Zeng,
- Duolin Wang,
- Lei Jiang,
- and Dong Xu
Genome Res. September 2024 34: 1445-1454; Published in Advance July 26, 2024, doi:10.1101/gr.279132.124
...in protein translocation in cells. The development of large protein language models (PLMs) and prompt-based learning provide a new opportunity for SP prediction, especially for the categories with limited annotated data. We present a parameter-efficient fine-tuning (PEFT) framework for SP prediction, PEFT...
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Method: Interactive visualization and interpretation of pangenome graphs by linear reference–based coordinate projection and annotation integration
- Zepu Miao and
- Jia-Xing Yue
Genome Res. February 2025 35: 296-310; Published in Advance January 13, 2025, doi:10.1101/gr.279461.124
...Interactive visualization and interpretation of pan graphs by linear reference–based coordinate projection and annotation integration Zepu Miao and Jia-Xing Yue State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong...
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