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Cited by 82Single-cell and spatial transcriptomicsMachine Learning in BioinformaticsComputational Drug Discovery Methods

Enhancing scientific discoveries in molecular biology with deep generative models

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Lopez, Romain, Gayoso, Adam, Yosef, Nir·2020·Molecular Systems Biology
DOI 10.15252/msb.20199198PMC PMC7517326
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Abstract

Generative models provide a well‐established statistical framework for evaluating uncertainty and deriving conclusions from large data sets especially in the presence of noise, sparsity, and bias. Initially developed for computer vision and natural language processing, these models have been shown to effectively summarize the complexity that underlies many types of data and enable a range of applications including supervised learning tasks, such as assigning labels to images; unsupervised learning tasks, such as dimensionality reduction; and out‐of‐sample generation, such as de novo image synthesis. With this early success, the power of generative models is now being increasingly leveraged in molecular biology, with applications ranging from designing new molecules with properties of interest to identifying deleterious mutations in our genomes and to dissecting transcriptional variability between single cells. In this review, we provide a brief overview of the technical notions behind generative models and their implementation with deep learning techniques. We then describe several different ways in which these models can be utilized in practice, using several recent applications in molecular biology as examples.

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  • Early approaches could not be generalized to unseen perturbations.

    “However, these methods could not be generalized to unseen perturbations.”

    From A systematic comparison of single-cell perturbation response prediction models (2026) · cites this paper as [12]

    The 'In silico generation of biomedical data' section describes scGen predicting a perturbation’s effect in a cell type lacking perturbation data. The 'Disentangling factors of variation' section questions whether its assumptions hold for cell-type-specific responses. Neither passage says that early methods could not generalize to unseen perturbations; searches for unseen and novel perturbations found no such statement. Page numbers were not provided in our copy. (LLM, conf=0.70)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Lopez, R., Gayoso, A., & Yosef, N.
Affiliations
  • University of California, Berkeley (US)
  • Ragon Institute of MGH, MIT and Harvard (US)
Corresponding author
Yosef, Nir
ORCID
Venue
Molecular Systems Biology
Publisher
Springer Science and Business Media LLC
Published
2020-09-01
Volume
16
Issue
9
Pages
e9198-e9198
ISSN
1744-4292 (ISSN-L)
DOI
10.15252/msb.20199198
PMC
PMC7517326
OpenAlex
W3088354555
Cited by
82
References
14
Citations by year
2026: 14 · 2025: 14 · 2024: 15 · 2023: 12 · 2022: 14 · 2021: 10 · 2020: 3
FWCI
0.93
Type
journal article
Language
en
Open access
Licence
Locations
  • Molecular Systems Biology (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
HHS | NIH | National Institute of Allergy and Infectious Diseases (U19 AI090023)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
Keywords
deep generative models, molecular biology, generative adversarial networks, variational autoencoder, molecular design, de novo molecule generation, deleterious mutation prediction, deep learning
MeSH terms
Deep Learning, Decision Making, Molecular Biology, Models, Statistical, Neural Networks, Computer, Biomedical Research

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