Artificial Intelligence-Driven Self-Healing Bioinformatics Pipelines: A Systematic Review of Automated Failure Detection and Remediation in Omics and Computational Biology Workflows

Artificial Intelligence-Driven Self-Healing Bioinformatics Pipelines: A Systematic Review of Automated Failure Detection and Remediation in Omics and Computational Biology Workflows | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Artificial Intelligence-Driven Self-Healing Bioinformatics Pipelines: A Systematic Review of Automated Failure Detection and Remediation in Omics and Computational Biology Workflows Bipul Bhattarai, Sulav Dahal, Naina Maharjan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9619074/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : Bioinformatics pipelines spanning genomics, transcriptomics, proteomics, and metagenomics face a pervasive reproducibility crisis driven by software dependency drift, resource exhaustion, and non-deterministic tool behaviour. Despite substantial investment in workflow management systems such as Nextflow, Snakemake, and Galaxy, pipeline failure responses remain predominantly manual. Advances in large language models (LLMs), retrieval-augmented generation (RAG), and multi-agent orchestration offer technically credible pathways to automated pipeline failure detection and autonomous remediation, yet their systematic application to bioinformatics-specific infrastructure has not been reviewed. Results : This PRISMA 2020-compliant systematic review (PROSPERO: CRD420261361756) synthesised 26 studies from six databases covering January 2019 to April 2026, addressing five research questions spanning LLM-based log parsing, multi-agent workflow orchestration, human-in-the-loop governance, fault-tolerant infrastructure patterns, and benchmarking gaps. LLM-RAG frameworks achieved up to 80% workflow step recall and reduced manual curation time by over 90%. Multi-agent systems including BioMaster and MARWA demonstrated superior error recovery across 18 omics modalities and 102 bioinformatics tools, consistently outperforming single-agent baselines. Infrastructure foundations are technically mature, but no included study demonstrated an end-to-end integrated self-healing pipeline combining monitoring, anomaly detection, remediation, and governance-compliant audit trail generation. Evidence quality was moderate (mean 6.2/10; range 5–9). Conclusions : Governance frameworks, bioinformatics-specific benchmarks, and regulatory alignment for clinical contexts remain critically absent and represent the field’s primary bottleneck. We propose a four-layer conceptual model (Infrastructure, Observability, Orchestration, Governance) to organise current evidence and identify research priorities. Three directions warrant priority investment: standardised failure-injection benchmarks, end-to-end integrated pipeline validation in production bioinformatics environments, and LLM fine-tuning on bioinformatics-specific log formats. Bioinformatics Computational Biology Artificial Intelligence and Machine Learning Epigenetics & Genomics bioinformatics pipelines self-healing systems large language models multiagent orchestration anomaly detection workflow reproducibility AIOps systematic review omics computing retrieval-augmented generation Full Text Additional Declarations The authors declare no competing interests. Supplementary Files DataExtractionWorkbookfinal.xlsx Data Extration Workbook SystematicReviewScreeningWorkbookfinal.xlsx Review Screening Workbook PRISMAChecklistBhattarai2026.pdf PRISMACHECKLIST Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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