ArticleEntropy (Basel, Switzerland)2010
Data Compression Concepts and Algorithms and their Applications to Bioinformatics.
Article in Entropy (Basel, Switzerland), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Vertical lossless genomic data compression tools for assembled genomes: A systematic literature review.PloS one · 2020Pooled it
- Autoencoders for genomic variation analysis.Genome research · 2026Article
- The Resolved Mutual Information Function as a Structural Fingerprint of Biomolecular Sequences for Interpretable Machine Learning Classifiers.Entropy (Basel, Switzerland) · 2021Article
- AC2: An Efficient Protein Sequence Compression Tool Using Artificial Neural Networks and Cache-Hash Models.Entropy (Basel, Switzerland) · 2021Article
- Storage Space Allocation Strategy for Digital Data with Message Importance.Entropy (Basel, Switzerland) · 2020Article
- Algorithms designed for compressed-gene-data transformation among gene banks with different references.BMC bioinformatics · 2018Article
- Sequence Factorization with Multiple References.PloS one · 2015Article
- Conditional entropy in variation-adjusted windows detects selection signatures associated with expression quantitative trait loci (eQTLs).BMC genomics · 2015Article
- Information theory applications for biological sequence analysis.Briefings in bioinformatics · 2014Review
- Using weighted entropy to rank chemicals in quantitative high-throughput screening experiments.Journal of biomolecular screening · 2014Article
- Adaptive efficient compression of genomes.Algorithms for molecular biology : AMB · 2012Article
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
Data compression at its base is concerned with how information is organized in data. Understanding this organization can lead to efficient ways of representing the information and hence data compression. In this paper we review the ways in which ideas and approaches fundamental to the theory and practice of data compression have been used in the area of bioinformatics. We look at how basic theoretical ideas from data compression, such as the notions of entropy, mutual information, and complexity have been used for analyzing biological sequences in order to discover hidden patterns, infer phylogenetic relationships between organisms and study viral populations. Finally, we look at how inferred grammars for biological sequences have been used to uncover structure in biological sequences.
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.