Machine Learning Applications for Therapeutic Tasks with Genomics Data
| Autor: | Greg Gibson, Cathy W. Critchlow, Lucas M. Glass, Jimeng Sun, Kexin Huang, Cao Xiao |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer science General Decision Sciences Genomics Review Health records Machine learning computer.software_genre Quantitative Biology - Quantitative Methods Field (computer science) Machine Learning (cs.LG) therapeutics discovery and development Biomedical data genomics Quantitative Biology - Genomics Quantitative Methods (q-bio.QM) Genomics (q-bio.GN) business.industry Pipeline (software) ComputingMethodologies_PATTERNRECOGNITION machine learning FOS: Biological sciences Key (cryptography) Artificial intelligence business computer |
| Zdroj: | Patterns |
| Popis: | Summary Thanks to the increasing availability of genomics and other biomedical data, many machine learning algorithms have been proposed for a wide range of therapeutic discovery and development tasks. In this survey, we review the literature on machine learning applications for genomics through the lens of therapeutic development. We investigate the interplay among genomics, compounds, proteins, electronic health records, cellular images, and clinical texts. We identify 22 machine learning in genomics applications that span the whole therapeutics pipeline, from discovering novel targets, personalizing medicine, developing gene-editing tools, all the way to facilitating clinical trials and post-market studies. We also pinpoint seven key challenges in this field with potentials for expansion and impact. This survey examines recent research at the intersection of machine learning, genomics, and therapeutic development. The bigger picture The genome contains instructions for building the function and structure of organisms. Recent high-throughput techniques have made it possible to generate massive amounts of genomics data. However, there are numerous roadblocks on the way to turning genomic data into tangible therapeutics. We observe that genomics data alone are insufficient for therapeutic development. We need to investigate how genomics data interact with other types of data such as compounds, proteins, electronic health records, images, and texts. Machine learning techniques can be used to identify patterns and extract insights from these complex data. In this review, we survey a wide range of genomics applications of machine learning that can enable faster and more efficacious therapeutic development. Challenges remain, including technical problems such as learning under different contexts given low-resource constraints, and practical issues such as mistrust of models, privacy, and fairness. Recent high-throughput techniques have made it possible to generate massive amounts of genomics data. However, there are numerous roadblocks on the way to turning genomic data into tangible therapeutics. We need to investigate how genomics data interact with other types of data such as compounds, proteins, electronic health records, images, and texts. In this review, we survey a wide range of genomics applications of machine learning that can enable faster and more efficacious therapeutic development. |
| Databáze: | OpenAIRE |
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