ReviewGenome biology2013
Machine learning and genome annotation: a match meant to be?
Review in Genome biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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
37 citing papers in PubMed.
- A Prognostic Model of Surgical Site Infection Using Daily Clinical Wound Assessment.Journal of the American College of Surgeons · 2016Trial
- AI-driven parametrization of Michaelis-Menten maximal velocity: Advancing in silico new approach methodologies (NAMs).NAM journal · 2025Article
- The GIAB genomic stratifications resource for human reference genomes.Nature communications · 2024Article
- Few-shot meta-learning applied to whole brain activity maps improves systems neuropharmacology and drug discovery.iScience · 2024Article
- Current understanding of functional peptides encoded by lncRNA in cancer.Cancer cell international · 2024Review
- From beer to breadboards: yeast as a force for biological innovation.Genome biology · 2024Review
- Predicting rice diseases using advanced technologies at different scales: present status and future perspectives.aBIOTECH · 2023Review
- Identification of a Novel Oxidative Stress- and Anoikis-Related Prognostic Signature and Its Immune Landscape Analysis in Non-Small Cell Lung Cancer.International journal of molecular sciences · 2023Article
- Tailored machine learning models for functional RNA detection in genome-wide screens.NAR genomics and bioinformatics · 2023Article
- Review
- Deep learning in bioinformatics.Turkish journal of biology = Turk biyoloji dergisi · 2023Article
- Metabolomics and modelling approaches for systems metabolic engineering.Metabolic engineering communications · 2022Review
- Artificial intelligence and machine learning in precision and genomic medicine.Medical oncology (Northwood, London, England) · 2022Review
- Artificial Intelligence and Machine Learning: What You Always Wanted to Know but Were Afraid to Ask.Gastro hep advances · 2022Review
- IoMT-Based Mitochondrial and Multifactorial Genetic Inheritance Disorder Prediction Using Machine Learning.Computational intelligence and neuroscience · 2022Article
- Predicting Tissue-Specific mRNA and Protein Abundance in Maize: A Machine Learning Approach.Frontiers in artificial intelligence · 2022Article
- Crop breeding for a changing climate: integrating phenomics and genomics with bioinformatics.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2021Review
- Review
- Machine learning in plant science and plant breeding.iScience · 2021Review
- SIMON: Open-Source Knowledge Discovery Platform.Patterns (New York, N.Y.) · 2021Article
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
No grant is acknowledged in the PubMed record.
Abstract
By its very nature, genomics produces large, high-dimensional datasets that are well suited to analysis by machine learning approaches. Here, we explain some key aspects of machine learning that make it useful for genome annotation, with illustrative examples from ENCODE.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.