ArticleBMC bioinformatics2019
Exploring the druggable space around the Fanconi anemia pathway using machine learning and mechanistic models.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 50 citations in OpenAlex.
- Systematic Review: Drug Repositioning for Congenital Disorders of Glycosylation (CDG).International journal of molecular sciences · 2022Pooled it
- Toward Early Diagnosis and Therapeutic Discovery in CLN3 Disease: A Computational Biomarker Discovery Framework.medRxiv : the preprint server for health sciences · 2026Article
- Metabolomics and bioinformatics study of baicalin in treating pulmonary fibrosis.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Strategy for drug repurposing in fibroadipogenic replacement during muscle wasting: application to duchenne muscular dystrophy.Frontiers in cell and developmental biology · 2025Article
- drexml: A command line tool and Python package for drug repurposing.Computational and structural biotechnology journal · 2024Article
- A comprehensive review of computational cell cycle models in guiding cancer treatment strategies.NPJ systems biology and applications · 2024Review
- The mechanistic functional landscape of retinitis pigmentosa: a machine learning-driven approach to therapeutic target discovery.Journal of translational medicine · 2024Article
- Functional Profiling of Soft Tissue Sarcoma Using Mechanistic Models.International journal of molecular sciences · 2023Article
- The Impact of Artificial Intelligence in the Odyssey of Rare Diseases.Biomedicines · 2023Review
- Discovering potential interactions between rare diseases and COVID-19 by combining mechanistic models of viral infection with statistical modeling.Human molecular genetics · 2022Article
- Using predictive machine learning models for drug response simulation by calibrating patient-specific pathway signatures.NPJ systems biology and applications · 2021Article
- A comprehensive database for integrated analysis of omics data in autoimmune diseases.BMC bioinformatics · 2021Article
- Incorporating Machine Learning into Established Bioinformatics Frameworks.International journal of molecular sciences · 2021Review
- A versatile workflow to integrate RNA-seq genomic and transcriptomic data into mechanistic models of signaling pathways.PLoS computational biology · 2021Article
- Mechanistic modeling of the SARS-CoV-2 disease map.BioData mining · 2021Article
- Genome-scale mechanistic modeling of signaling pathways made easy: A bioconductor/cytoscape/web server framework for the analysis of omic data.Computational and structural biotechnology journal · 2021Article
- Artificial Intelligence in Epigenetic Studies: Shedding Light on Rare Diseases.Frontiers in molecular biosciences · 2021Review
- Drug repurposing for COVID-19 using machine learning and mechanistic models of signal transduction circuits related to SARS-CoV-2 infection.Signal transduction and targeted therapy · 2020Article
- Contributions from the 2019 Literature on Bioinformatics and Translational Informatics.Yearbook of medical informatics · 2020Review
- Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
backgroundIn spite of the abundance of genomic data, predictive models that describe phenotypes as a function of gene expression or mutations are difficult to obtain because they are affected by the curse of dimensionality, given the disbalance between samples and candidate genes. And this is especially dramatic in scenarios in which the availability of samples is difficult, such as the case of rare diseases.
resultsThe application of multi-output regression machine learning methodologies to predict the potential effect of external proteins over the signaling circuits that trigger Fanconi anemia related cell functionalities, inferred with a mechanistic model, allowed us to detect over 20 potential therapeutic targets.
conclusionsThe use of artificial intelligence methods for the prediction of potentially causal relationships between proteins of interest and cell activities related with disease-related phenotypes opens promising avenues for the systematic search of new targets in rare diseases.
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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.