Machine learning and genome annotation: a match meant to be?
Machine learning and genome annotation: a match meant to be? is a research paper published in Genome Biology (2013). On theSindex it has a DataRank of 3.8. It has been cited 103 times, with 102 citing works in its 1-hop citation network.
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.
›Data sources & pipeline
FAIR Checklist
Context only (not used in score)- Has DOI
- Open Access
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
DataRank Breakdown
Base Score Contribution
0.697
From this paper's citation signal
Citation Network Contribution
3.2
From 86 citing papers with measurable signal
Top 5 citers driving the network score
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
- Random ForestsMachine Learning2001121,133 citationsDataRank 1.8
- Initial sequencing and analysis of the human genomeNature200124,542 citationsDataRank 17.1Top 10%
- An integrated encyclopedia of DNA elements in the human genomeNature201219,311 citationsDataRank 23.8Top 3%
- Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiationNature Biotechnology201016,393 citationsDataRank 1.5
- The Sequence of the Human GenomeScience200113,648 citationsDataRank 18.7Top 7%
Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 18% comes from its base citations and 82% from the citation network (86 citing papers contributed measurable signal).
- Base score B(p)
- log1p(citation_count) — grows sub-linearly, so a paper with 1,000 citations is not 10× a paper with 100.
- Network N(p)
- Σ over citers of log1p(Cq) ÷ max(outdegreeq, 1). Being cited by a highly-cited paper with few references counts most.
- Damping factor d = 0.85
- DataRank = (1−d)·B(p) + d·N(p) — the two cards above are each already multiplied by their share.
- Self-citations excluded
- Citers sharing any OpenAlex author ID with this paper are filtered out before the network sum.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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