ReviewPatterns (New York, N.Y.)2021
Machine learning applications for therapeutic tasks with genomics data.
Review in Patterns (New York, N.Y.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- Determining a role for Patient and Public Involvement and Engagement (PPIE) in genomic data governance for cancer care.European journal of human genetics : EJHG · 2026Article
- Hybrid deep learning framework for accurate classification of high dimensional genomic data.Scientific reports · 2026Article
- Ensemble Machine Learning on Bulk RNA-Seq Identifies 17-Gene Signature Predicting Neoadjuvant Chemotherapy Response in Breast Cancer.Current issues in molecular biology · 2026Article
- Toward Personalized Medicine in Parkinson's Disease: A Scoping Review of Biomarkers, Genetics, and Treatment Stratification.Journal of clinical practice and research · 2025Review
- Enhanced prediction of breast cancer patient response to chemotherapy by integrating deconvolved expression patterns of immune, stromal and tumor cells.bioRxiv : the preprint server for biology · 2025Article
- Advancing genome-based precision medicine: a review on machine learning applications for rare genetic disorders.Briefings in bioinformatics · 2025Review
- Harnessing machine learning in contemporary tobacco research.Toxicology reports · 2025Review
- Pathways to chronic disease detection and prediction: Mapping the potential of machine learning to the pathophysiological processes while navigating ethical challenges.Chronic diseases and translational medicine · 2025Review
- Enhancing genomic disorder prediction through Feynman Concordance and Interpolated Nearest Centroid techniques.Scientific reports · 2024Article
- PERCEPTION predicts patient response and resistance to treatment using single-cell transcriptomics of their tumors.Nature cancer · 2024Article
- Enabling technology and core theory of synthetic biology.Science China. Life sciences · 2023Review
- Diagnostic markers and potential therapeutic agents for Sjögren's syndrome screened through multiple machine learning and molecular docking.Clinical and experimental immunology · 2023Article
- Biological data studies, scale-up the potential with machine learning.European journal of human genetics : EJHG · 2023Article
- Cryptic mutations of PLC family members in brain disorders: recent discoveries and a deep-learning-based approach.Brain : a journal of neurology · 2023Review
- Article
- Nanomedicine Ex Machina: Between Model-Informed Development and Artificial Intelligence.Frontiers in digital health · 2022Review
Corrections and comments
- Commented on by
Authors and funding
6 authors.
Funding
Abstract
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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.