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Chen V, Yang M, Cui W, Kim JS, Talwalkar A, Ma J. Applying interpretable machine learning in computational biology-pitfalls, recommendations and opportunities for new developments. Nat Methods 2024; 21:1454-1461. [PMID: 39122941 DOI: 10.1038/s41592-024-02359-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 06/24/2024] [Indexed: 08/12/2024]
Abstract
Recent advances in machine learning have enabled the development of next-generation predictive models for complex computational biology problems, thereby spurring the use of interpretable machine learning (IML) to unveil biological insights. However, guidelines for using IML in computational biology are generally underdeveloped. We provide an overview of IML methods and evaluation techniques and discuss common pitfalls encountered when applying IML methods to computational biology problems. We also highlight open questions, especially in the era of large language models, and call for collaboration between IML and computational biology researchers.
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Affiliation(s)
- Valerie Chen
- Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Muyu Yang
- Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Wenbo Cui
- Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Joon Sik Kim
- Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Ameet Talwalkar
- Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
| | - Jian Ma
- Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
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Rennie S. Deep Learning for Elucidating Modifications to RNA-Status and Challenges Ahead. Genes (Basel) 2024; 15:629. [PMID: 38790258 PMCID: PMC11121098 DOI: 10.3390/genes15050629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Revised: 05/11/2024] [Accepted: 05/11/2024] [Indexed: 05/26/2024] Open
Abstract
RNA-binding proteins and chemical modifications to RNA play vital roles in the co- and post-transcriptional regulation of genes. In order to fully decipher their biological roles, it is an essential task to catalogue their precise target locations along with their preferred contexts and sequence-based determinants. Recently, deep learning approaches have significantly advanced in this field. These methods can predict the presence or absence of modification at specific genomic regions based on diverse features, particularly sequence and secondary structure, allowing us to decipher the highly non-linear sequence patterns and structures that underlie site preferences. This article provides an overview of how deep learning is being applied to this area, with a particular focus on the problem of mRNA-RBP binding, while also considering other types of chemical modification to RNA. It discusses how different types of model can handle sequence-based and/or secondary-structure-based inputs, the process of model training, including choice of negative regions and separating sets for testing and training, and offers recommendations for developing biologically relevant models. Finally, it highlights four key areas that are crucial for advancing the field.
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Affiliation(s)
- Sarah Rennie
- Section for Computational and RNA Biology, Department of Biology, University of Copenhagen, 2200 Copenhagen, Denmark
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Jiao S, Ye X, Sakurai T, Zou Q, Liu R. Integrated convolution and self-attention for improving peptide toxicity prediction. BIOINFORMATICS (OXFORD, ENGLAND) 2024; 40:btae297. [PMID: 38696758 DOI: 10.1093/bioinformatics/btae297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 04/02/2024] [Accepted: 04/30/2024] [Indexed: 05/04/2024]
Abstract
MOTIVATION Peptides are promising agents for the treatment of a variety of diseases due to their specificity and efficacy. However, the development of peptide-based drugs is often hindered by the potential toxicity of peptides, which poses a significant barrier to their clinical application. Traditional experimental methods for evaluating peptide toxicity are time-consuming and costly, making the development process inefficient. Therefore, there is an urgent need for computational tools specifically designed to predict peptide toxicity accurately and rapidly, facilitating the identification of safe peptide candidates for drug development. RESULTS We provide here a novel computational approach, CAPTP, which leverages the power of convolutional and self-attention to enhance the prediction of peptide toxicity from amino acid sequences. CAPTP demonstrates outstanding performance, achieving a Matthews correlation coefficient of approximately 0.82 in both cross-validation settings and on independent test datasets. This performance surpasses that of existing state-of-the-art peptide toxicity predictors. Importantly, CAPTP maintains its robustness and generalizability even when dealing with data imbalances. Further analysis by CAPTP reveals that certain sequential patterns, particularly in the head and central regions of peptides, are crucial in determining their toxicity. This insight can significantly inform and guide the design of safer peptide drugs. AVAILABILITY AND IMPLEMENTATION The source code for CAPTP is freely available at https://github.com/jiaoshihu/CAPTP.
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Affiliation(s)
- Shihu Jiao
- Department of Computer Science, University of Tsukuba, Tsukuba 3058577, Japan
| | - Xiucai Ye
- Department of Computer Science, University of Tsukuba, Tsukuba 3058577, Japan
| | - Tetsuya Sakurai
- Department of Computer Science, University of Tsukuba, Tsukuba 3058577, Japan
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China
| | - Ruijun Liu
- School of Software, Beihang University, Beijing 100191, China
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Novakovsky G, Fornes O, Saraswat M, Mostafavi S, Wasserman WW. ExplaiNN: interpretable and transparent neural networks for genomics. Genome Biol 2023; 24:154. [PMID: 37370113 DOI: 10.1186/s13059-023-02985-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Accepted: 06/12/2023] [Indexed: 06/29/2023] Open
Abstract
Deep learning models such as convolutional neural networks (CNNs) excel in genomic tasks but lack interpretability. We introduce ExplaiNN, which combines the expressiveness of CNNs with the interpretability of linear models. ExplaiNN can predict TF binding, chromatin accessibility, and de novo motifs, achieving performance comparable to state-of-the-art methods. Its predictions are transparent, providing global (cell state level) as well as local (individual sequence level) biological insights into the data. ExplaiNN can serve as a plug-and-play platform for pretrained models and annotated position weight matrices. ExplaiNN aims to accelerate the adoption of deep learning in genomic sequence analysis by domain experts.
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Affiliation(s)
- Gherman Novakovsky
- Department of Medical Genetics, Centre for Molecular Medicine and Therapeutics, BC Children's Hospital Research Institute, University of British Columbia, Vancouver, BC, Canada
| | - Oriol Fornes
- Department of Medical Genetics, Centre for Molecular Medicine and Therapeutics, BC Children's Hospital Research Institute, University of British Columbia, Vancouver, BC, Canada
| | - Manu Saraswat
- Department of Medical Genetics, Centre for Molecular Medicine and Therapeutics, BC Children's Hospital Research Institute, University of British Columbia, Vancouver, BC, Canada
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany
| | - Sara Mostafavi
- Paul G. Allen School of Computer Science and Engineering, University of Washington (UW), Seattle, USA
| | - Wyeth W Wasserman
- Department of Medical Genetics, Centre for Molecular Medicine and Therapeutics, BC Children's Hospital Research Institute, University of British Columbia, Vancouver, BC, Canada.
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Malina S, Cizin D, Knowles DA. Deep mendelian randomization: Investigating the causal knowledge of genomic deep learning models. PLoS Comput Biol 2022; 18:e1009880. [PMID: 36265006 PMCID: PMC9624391 DOI: 10.1371/journal.pcbi.1009880] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 11/01/2022] [Accepted: 09/19/2022] [Indexed: 11/06/2022] Open
Abstract
Multi-task deep learning (DL) models can accurately predict diverse genomic marks from sequence, but whether these models learn the causal relationships between genomic marks is unknown. Here, we describe Deep Mendelian Randomization (DeepMR), a method for estimating causal relationships between genomic marks learned by genomic DL models. By combining Mendelian randomization with in silico mutagenesis, DeepMR obtains local (locus specific) and global estimates of (an assumed) linear causal relationship between marks. In a simulation designed to test recovery of pairwise causal relations between transcription factors (TFs), DeepMR gives accurate and unbiased estimates of the 'true' global causal effect, but its coverage decays in the presence of sequence-dependent confounding. We then apply DeepMR to examine the global relationships learned by a state-of-the-art DL model, BPNet, between TFs involved in reprogramming. DeepMR's causal effect estimates validate previously hypothesized relationships between TFs and suggest new relationships for future investigation.
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Affiliation(s)
- Stephen Malina
- Department of Computer Science, Columbia University, New York, New York, United States of America
- Dyno Therapeutics, Watertown, Massachusetts, United States of America
- * E-mail: ,
| | - Daniel Cizin
- Department of Computer Science, Columbia University, New York, New York, United States of America
- Tri-Institutional Ph.D. Program in Computational Biology and Medicine, Weill Cornell Medicine, New York, New York, United States of America
| | - David A. Knowles
- Department of Computer Science, Columbia University, New York, New York, United States of America
- New York Genome Center, New York, New York, United States of America
- Department of Systems Biology, Columbia University, New York, New York, United States of America
- Data Science Institute, Columbia University, New York, New York, United States of America
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Yang M, Ma J. Machine Learning Methods for Exploring Sequence Determinants of 3D Genome Organization. J Mol Biol 2022; 434:167666. [PMID: 35659533 DOI: 10.1016/j.jmb.2022.167666] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 05/23/2022] [Accepted: 05/27/2022] [Indexed: 01/25/2023]
Abstract
In higher eukaryotic cells, chromosomes are folded inside the nucleus. Recent advances in whole-genome mapping technologies have revealed the multiscale features of 3D genome organization that are intertwined with fundamental genome functions. However, DNA sequence determinants that modulate the formation of 3D genome organization remain poorly characterized. In the past few years, predicting 3D genome organization based on DNA sequence features has become an active area of research. Here, we review the recent progress in computational approaches to unraveling important sequence elements for 3D genome organization. In particular, we discuss the rapid development of machine learning-based methods that facilitate the connections between DNA sequence features and 3D genome architectures at different scales. While much progress has been made in developing predictive models for revealing important sequence features for 3D genome organization, new research is urgently needed to incorporate multi-omic data and enhance model interpretability, further advancing our understanding of gene regulation mechanisms through the lens of 3D genome organization.
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Affiliation(s)
- Muyu Yang
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, United States. https://twitter.com/muyu_wendy_yang
| | - Jian Ma
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, United States.
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