Machine Learning for Biomolecular Data Wrangling and Curation: A Review of Learned Extraction, Error Detection, Integration, and Curation Workflows

Abstract

The biomolecular sciences generate data faster than they can be made usable. Between instrument output and analyzable knowledge lies the unglamorous majority of bioinformatics labor: extracting assertions from literature, standardizing identifiers and vocabularies, detecting and repairing errors, resolving duplicate and conflicting records, imputing what is missing, correcting what batches distorted, and integrating what heterogeneous sources report differently. This wrangling and curation layer has historically been manual, expert, and chronically under-resourced relative to data growth, and machine learning has been moving into it from several directions at once. This review synthesizes the literature on machine learning for biomolecular data wrangling and curation. It organizes the field by curation function rather than by model family: learned extraction and annotation, where biomedical text mining and pre-trained language models convert literature into structured assertions and where computational function prediction annotates sequences at scale; learned error detection, from the foundational quality scores of sequencing through statistical validation of proteomics identifications to general anomaly detection over database entries; learned entity resolution and integration, where deduplication and record matching meet deep matching models; learned imputation and batch correction, where the missing-data and batch-effect literatures supply principled repair; and learning-directed curation workflows, where active learning, weak supervision, and human-in-the-loop systems allocate scarce expert attention. Cross-cutting infrastructure, ontologies and minimum-information standards, workflow managers, and the FAIR agenda, is reviewed as the substrate that makes learned curation deployable, and the evidence on pitfalls, leakage, label imperfection, and drift, is weighed. The review closes with open challenges, including curation of predicted rather than measured knowledge, and directions for the field.