ArticleFrontiers in bioinformatics2021
Machine Learning for Causal Inference in Biological Networks: Perspectives of This Challenge.
Article in Frontiers in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The use of machine learning on administrative and survey data to predict suicidal thoughts and behaviors: a systematic review.Frontiers in psychiatry · 2024Pooled it
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- SHISMA: SHape-driven inference of significant cell type-specific subnetworks from tiMe series single-cell trAnscriptomics.Bioinformatics advances · 2026Article
- Multi-omic and computational approaches for biomarker discovery and clinical translation in pediatric sepsis.Frontiers in pharmacology · 2026Review
- Associations among physical activity, diet, non-lifestyle characteristics and the gut microbiome of cancer patients: A scoping review and network analysis.Oncoscience · 2026Review
- Machine Learning-Enhanced Nanoparticle Design for Precision Cancer Drug Delivery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- The dawn of a new era: can machine learning and large language models reshape QSP modeling?Journal of pharmacokinetics and pharmacodynamics · 2025Review
- Causal machine learning models for predicting low birth weight in midwife-led continuity care intervention in North Shoa Zone, Ethiopia.BMC medical informatics and decision making · 2025Article
- Artificial Intelligence (AI)-driven approach to climate action and sustainable development.Nature communications · 2025Article
- Identification of diagnostic candidate genes in COVID-19 patients with sepsis.Immunity, inflammation and disease · 2024Article
- Gene regulatory networks in disease and ageing.Nature reviews. Nephrology · 2024Review
- A molecular index for biological age identified from the metabolome and senescence-associated secretome in humans.Aging cell · 2024Article
- Machine-learning algorithms in screening for type 2 diabetes mellitus: Data from Fasa Adults Cohort Study.Endocrinology, diabetes & metabolism · 2024Article
- Context-aware knowledge selection and reliable model recommendation with ACCORDION.Frontiers in systems biology · 2024Article
- Artificial intelligence for dementia prevention.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Review
- What is circulating factor disease and how is it currently explained?Pediatric nephrology (Berlin, Germany) · 2023Review
- Unified tumor growth mechanisms from multimodel inference and dataset integration.PLoS computational biology · 2023Article
- Tumor heterogeneity: preclinical models, emerging technologies, and future applications.Frontiers in oncology · 2023Review
- An introduction to causal inference for pharmacometricians.CPT: pharmacometrics & systems pharmacology · 2023Article
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Abstract
Most machine learning-based methods predict outcomes rather than understanding causality. Machine learning methods have been proved to be efficient in finding correlations in data, but unskilful to determine causation. This issue severely limits the applicability of machine learning methods to infer the causal relationships between the entities of a biological network, and more in general of any dynamical system, such as medical intervention strategies and clinical outcomes system, that is representable as a network. From the perspective of those who want to use the results of network inference not only to understand the mechanisms underlying the dynamics, but also to understand how the network reacts to external stimuli (e. g. environmental factors, therapeutic treatments), tools that can understand the causal relationships between data are highly demanded. Given the increasing popularity of machine learning techniques in computational biology and the recent literature proposing the use of machine learning techniques for the inference of biological networks, we would like to present the challenges that mathematics and computer science research faces in generalising machine learning to an approach capable of understanding causal relationships, and the prospects that achieving this will open up for the medical application domains of systems biology, the main paradigm of which is precisely network biology at any physical scale.
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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.