ReviewFEBS letters2016
Protein function in precision medicine: deep understanding with machine learning.
Review in FEBS letters, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predicting Functional Effects of Synonymous Variants: A Systematic Review and Perspectives.Frontiers in genetics · 2019Pooled it
- Structure-Based Network Analysis of AlphaFold Structure Predictions Identifies Putative Causative Variants of Inherited Retinal Disease.Investigative ophthalmology & visual science · 2026Article
- On the state of protein function prediction: a report on the fourth CAFA challenge.bioRxiv : the preprint server for biology · 2026Article
- Advances in Protein Function Prediction from the Fifth CAFA Challenge.bioRxiv : the preprint server for biology · 2026Article
- Literature-driven extraction and computational prediction of causal statements linking genetic variants to biological processes, pathways and phenotypes.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2026Article
- SPACE: STRING proteins as complementary embeddings.Bioinformatics (Oxford, England) · 2025Article
- Critical assessment of missense variant effect predictors on disease-relevant variant data.Human genetics · 2025Article
- Evaluation of enzyme activity predictions for variants of unknown significance in Arylsulfatase A.Human genetics · 2025Article
- Evaluating predictors of kinase activity of STK11 variants identified in primary human non-small cell lung cancers.Human genetics · 2025Article
- Structural analysis of genomic and proteomic signatures reveal dynamic expression of intrinsically disordered regions in breast cancer.iScience · 2024Article
- Evaluating predictors of kinase activity of STK11 variants identified in primary human non-small cell lung cancers.Research square · 2024Article
- Evaluation of enzyme activity predictions for variants of unknown significance in Arylsulfatase A.bioRxiv : the preprint server for biology · 2024Article
- Structure-based network analysis predicts pathogenic variants in human proteins associated with inherited retinal disease.NPJ genomic medicine · 2024Article
- CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods.Genome biology · 2024Article
- Evaluating the relevance of sequence conservation in the prediction of pathogenic missense variants.Human genetics · 2022Article
- Prioritizing de novo autism risk variants with calibrated gene- and variant-scoring models.Human genetics · 2022Article
- Towards a robust out-of-the-box neural network model for genomic data.BMC bioinformatics · 2022Article
- KEAP1 Cancer Mutants: A Large-Scale Molecular Dynamics Study of Protein Stability.International journal of molecular sciences · 2021Article
- Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions.Medicinal research reviews · 2021Review
- Inferring the molecular and phenotypic impact of amino acid variants with MutPred2.Nature communications · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Precision medicine and personalized health efforts propose leveraging complex molecular, medical and family history, along with other types of personal data toward better life. We argue that this ambitious objective will require advanced and specialized machine learning solutions. Simply skimming some low-hanging results off the data wealth might have limited potential. Instead, we need to better understand all parts of the system to define medically relevant causes and effects: how do particular sequence variants affect particular proteins and pathways? How do these effects, in turn, cause the health or disease-related phenotype? Toward this end, deeper understanding will not simply diffuse from deeper machine learning, but from more explicit focus on understanding protein function, context-specific protein interaction networks, and impact of variation on both.
Indexed as
Identifiers
What Socratic 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 Socratic graph.