ArticleJournal of computational biology : a journal of computational molecular cell biology2021
Metric Labeling and Semimetric Embedding for Protein Annotation Prediction.
Article in Journal of computational biology : a journal of computational molecular cell biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- A unified benchmark of supervised and retrieval-based methods for viral genomic sequence classification.Scientific reports · 2026Article
- Enhancing tumor T cell antigen prediction by integrating deep protein representations.BMC bioinformatics · 2026Article
- MAGC-DTI: modality-shared space and adaptive gated interactive cross-attention for drug-target interaction prediction.Scientific reports · 2026Article
- A comparative analysis of topological domain callers over RNA-associated interactome.BMC bioinformatics · 2026Article
- Analysis of chromatin structure reveals the connection between sQTLs and the splicing of distant genes.Scientific reports · 2025Article
- Joint analysis of sQTL and Hi-C reveals spatial proximity between sQTLs and target genes in cancer tissues.Scientific reports · 2025Article
- Diffusion archeology for diffusion progression history reconstruction.Knowledge and information systems · 2016Article
- Exploiting ontology graph for predicting sparsely annotated gene function.Bioinformatics (Oxford, England) · 2015Article
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Abstract
Computational techniques have been successful at predicting protein function from relational data (functional or physical interactions). These techniques have been used to generate hypotheses and to direct experimental validation. With few exceptions, the task is modeled as multilabel classification problems where the labels (functions) are treated independently or semi-independently. However, databases such as the Gene Ontology provide information about the similarities between functions. We explore the use of the Metric Labeling combinatorial optimization problem to make use of heuristically computed distances between functions to make more accurate predictions of protein function in networks derived from both physical interactions and a combination of other data types. To do this, we give a new technique (based on convex optimization) for converting heuristic semimetric distances into a metric with minimum least-squared distortion (LSD). The Metric Labeling approach is shown to outperform five existing techniques for inferring function from networks. These results suggest that Metric Labeling is useful for protein function prediction, and that LSD minimization can help solve the problem of converting heuristic distances to a metric.
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