ReviewMolecular cell2022
The use of machine learning to discover regulatory networks controlling biological systems.
Review in Molecular cell, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 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
28 citing papers in PubMed.
- Opportunities for artificial intelligence and synthetic biology in designing living drug delivery systems.Advanced drug delivery reviews · 2026Review
- MarkerScout: A Disease-Agnostic Machine Learning Framework for Biomarker Prediction from Multi-Scale Mechanistic Models.bioRxiv : the preprint server for biology · 2026Article
- Chondrolectin regulates the sublaminar localization and regenerative function of muscle satellite cells in mice.iScience · 2026Article
- Deep learning-based high-information-content graph representation of early stage bacterial biofilms.NPJ biofilms and microbiomes · 2026Article
- Graph Learning in Bioinformatics: A Survey of Graph Neural Network Architectures, Biological Graph Construction and Bioinformatics Applications.Biomolecules · 2026Review
- Mechanisms linking cytoplasmic decay of translation-defective mRNA to transcriptional adaptation.Science (New York, N.Y.) · 2026Article
- Gene regulatory networks involved in activation of Notch signaling by AGEs in the pathogenesis of diabetic kidney disease.PloS one · 2026Article
- A copula-infused graph neural network for cell type classification in single-cell RNA sequencing data.Computational and structural biotechnology journal · 2026Article
- Indirect 3D printing in tissue engineering: expanding materials used for improved scaffold functionality.Biomedical engineering online · 2025Review
- Bladder cancer diagnostic and prognostic models from DNA methylation by multi algorithm machine learning.NPJ precision oncology · 2025Article
- Chondrolectin regulates the sublaminar localization and regenerative function of muscle satellite cells in mice.bioRxiv : the preprint server for biology · 2025Article
- Chromosomal Instability and Clonal Heterogeneity in Breast Cancer: From Mechanisms to Clinical Applications.Cancers · 2025Review
- Non-canonical plant metabolism.Nature plants · 2025Review
- Machine learning in infectious diseases: potential applications and limitations.Annals of medicine · 2024Review
- Ten challenges and opportunities in computational immuno-oncology.Journal for immunotherapy of cancer · 2024Review
- Network modeling links kidney developmental programs and the cancer type-specificity of VHL mutations.NPJ systems biology and applications · 2024Article
- Recent advances in artificial intelligent strategies for tissue engineering and regenerative medicine.Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2024Review
- A Causal Regulation Modeling Algorithm for Temporal Events with Application toInternational journal of molecular sciences · 2024Article
- Advancements and applications of single-cell multi-omics techniques in cancer research: Unveiling heterogeneity and paving the way for precision therapeutics.Biochemistry and biophysics reports · 2024Review
- Evaluation of single-sample network inference methods for precision oncology.NPJ systems biology and applications · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Biological systems are composed of a vast web of multiscale molecular interactors and interactions. High-throughput technologies, both bulk and single cell, now allow for investigation of the properties and quantities of these interactors. Computational algorithms and machine learning methods then provide the tools to derive meaningful insights from the resulting data sets. One such approach is graphical network modeling, which provides a computational framework to explicitly model the molecular interactions within and between the cells comprising biological systems. These graphical networks aim to describe a putative chain of cause and effect between interacting molecules. This feature allows for determination of key molecules in a biological process, accelerated generation of mechanistic hypotheses, and simulation of experimental outcomes. We review the computational concepts and applications of graphical network models across molecular scales for both intracellular and intercellular regulatory biology, examples of successful applications, and the future directions needed to overcome current limitations.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.