ArticlePLoS computational biology2022
Constructing benchmark test sets for biological sequence analysis using independent set algorithms.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 13 papers.
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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.
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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.
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Who cites it
13 citing papers in PubMed.
- Architectural good practices for reproducible benchmarking in protein machine learning.Frontiers in bioinformatics · 2026Article
- Evaluating computational tools for protein-coding sequence detection: Are they up to the task?RNA (New York, N.Y.) · 2025Article
- Design and structure of overlapping regions in PCA via deep learning.Synthetic and systems biotechnology · 2025Article
- Simulation of adaptive immune receptors and repertoires with complex immune information to guide the development and benchmarking of AIRR machine learning.Nucleic acids research · 2025Article
- VirTAXA: enhancing RNA virus taxonomic classification with remote homology search and tree-based validation.Bioinformatics (Oxford, England) · 2024Article
- SpanSeq: similarity-based sequence data splitting method for improved development and assessment of deep learning projects.NAR genomics and bioinformatics · 2024Article
- Time series-based hybrid ensemble learning model with multivariate multidimensional feature coding for DNA methylation prediction.BMC genomics · 2023Article
- GraphPart: homology partitioning for biological sequence analysis.NAR genomics and bioinformatics · 2023Article
- PhaVIP: Phage VIrion Protein classification based on chaos game representation and Vision Transformer.Bioinformatics (Oxford, England) · 2023Article
- Deep self-supervised learning for biosynthetic gene cluster detection and product classification.PLoS computational biology · 2023Article
- Correction: Constructing Benchmark Test Sets for Biological Sequence Analysis Using Independent Set Algorithms.PLoS computational biology · 2023Article
- From sequence to function through structure: Deep learning for protein design.Computational and structural biotechnology journal · 2023Review
- Phage family classification underFrontiers in microbiology · 2022Review
Corrections and comments
- Erratum issued
Authors and funding
2 authors.
Funding
Abstract
Biological sequence families contain many sequences that are very similar to each other because they are related by evolution, so the strategy for splitting data into separate training and test sets is a nontrivial choice in benchmarking sequence analysis methods. A random split is insufficient because it will yield test sequences that are closely related or even identical to training sequences. Adapting ideas from independent set graph algorithms, we describe two new methods for splitting sequence data into dissimilar training and test sets. These algorithms input a sequence family and produce a split in which each test sequence is less than p% identical to any individual training sequence. These algorithms successfully split more families than a previous approach, enabling construction of more diverse benchmark datasets.
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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.