Interpretation of deep learning in genomics and epigenomics

Abstract

Machine learning methods have been widely applied to big data analysis in genomics and epigenomics research. Although accuracy and efficiency are common goals in many modeling tasks, model interpretability is especially important to these studies towards understanding the underlying molecular and cellular mechanisms. Deep neural networks (DNNs) have recently gained popularity in various types of genomic and epigenomic studies due to their capabilities in utilizing large-scale high-throughput bioinformatics data and achieving high accuracy in predictions and classifications. However, DNNs are often challenged by their potential to explain the predictions due to their black-box nature. In this review, we present current development in the model interpretation of DNNs, focusing on their applications in genomics and epigenomics. We first describe state-of-the-art DNN interpretation methods in representative machine learning fields. We then summarize the DNN interpretation methods in recent studies on genomics and epigenomics, focusing on current data- and computing-intensive topics such as sequence motif identification, genetic variations, gene expression, chromatin interactions and non-coding RNAs. We also present the biological discoveries that resulted from these interpretation methods. We finally discuss the advantages and limitations of current interpretation approaches in the context of genomic and epigenomic studies. Contact:xiaoman@mail.ucf.edu, haihu@cs.ucf.edu

Integration strategies of multi-omics data for machine learning analysis

“…The transparency of machine learning results may also lead to new biological discoveries. As several methods have been developed for interpreting neural networks models, we invite our reader to other reviews [71] , [72] . Most of those strategies focus on explaining the final decision of the algorithm and identifying biomarkers, but some DL models [73] , [74] can directly find relevant biological pathways during the learning process ( Fig.…”

Risk Prediction of Cardiovascular Events by Exploration of Molecular Data with Explainable Artificial Intelligence

“…In general, neural networks are flexible in terms of architecture and can therefore be tailored to specific tasks. CNNs have for instance been successfully applied to 1D genome sequence data for predicting the functional impact of non-coding SNP-variants on epigenetic features such as transcription binding sites and DNA accessibility [ 137 , 138 , 139 ]. Consequently, CNNs may be also well-suited for the task of risk prediction of disease or disease progression based on genotype data.…”

Interpretable machine learning for genomics

“…Holzinger et al ( 2019 ) frame their survey with a focus on medical applications, while Azodi et al ( 2020 ) specialize more narrowly on iML for genetics. For reviews of interpretable deep learning in genomics, see Talukder et al ( 2021 ) and Treppner et al ( 2021 ).…”