ArticleCell systems2015
Entropy-scaling search of massive biological data.
Article in Cell systems, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 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
29 citing papers in PubMed.
- Constrained Diffusion as a Paradigm for Evolution.bioRxiv : the preprint server for biology · 2026Article
- Efficient and robust search of microbial genomes via phylogenetic compression.Nature methods · 2025Article
- Scanorama: integrating large and diverse single-cell transcriptomic datasets.Nature protocols · 2024Review
- Efficient evolution of human antibodies from general protein language models.Nature biotechnology · 2024Article
- Fast and robust metagenomic sequence comparison through sparse chaining with skani.Nature methods · 2023Article
- Navigating bottlenecks and trade-offs in genomic data analysis.Nature reviews. Genetics · 2023Review
- Progress in and Opportunities for Applying Information Theory to Computational Biology and Bioinformatics.Entropy (Basel, Switzerland) · 2022Article
- Behavioral Neuroscience in the Era of Genomics: Tools and Lessons for Analyzing High-Dimensional Datasets.International journal of molecular sciences · 2022Review
- A Python-based programming language for high-performance computational genomics.Nature biotechnology · 2021Article
- Levenshtein Distance, Sequence Comparison and Biological Database Search.IEEE transactions on information theory · 2021Article
- Hopper: a mathematically optimal algorithm for sketching biological data.Bioinformatics (Oxford, England) · 2020Article
- Weighted minimizer sampling improves long read mapping.Bioinformatics (Oxford, England) · 2020Article
- Information Theory in Computational Biology: Where We Stand Today.Entropy (Basel, Switzerland) · 2020Article
- Efficient Construction of a Complete Index for Pan-Genomics Read Alignment.Journal of computational biology : a journal of computational molecular cell biology · 2020Article
- An Efficient, Scalable, and Exact Representation of High-Dimensional Color Information Enabled Using de Bruijn Graph Search.Journal of computational biology : a journal of computational molecular cell biology · 2020Article
- Carnelian uncovers hidden functional patterns across diverse study populations from whole metagenome sequencing reads.Genome biology · 2020Article
- Single Cell Genomics Reveals Viruses Consumed by Marine Protists.Frontiers in microbiology · 2020Article
- Metagenomic binning through low-density hashing.Bioinformatics (Oxford, England) · 2019Article
- Learning structural motif representations for efficient protein structure search.Bioinformatics (Oxford, England) · 2018Article
- Generalizable and Scalable Visualization of Single-Cell Data Using Neural Networks.Cell systems · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Many data sets exhibit well-defined structure that can be exploited to design faster search tools, but it is not always clear when such acceleration is possible. Here we introduce a framework for similarity search based on characterizing a data set's entropy and fractal dimension. We prove that searching scales in time with metric entropy (number of covering hyperspheres), if the fractal dimension of the data set is low, and scales in space with the sum of metric entropy and information-theoretic entropy (randomness of the data). Using these ideas, we present accelerated versions of standard tools, with no loss in specificity and little loss in sensitivity, for use in three domains-high-throughput drug screening (Ammolite, 150x speedup), metagenomics (MICA, 3.5x speedup of DIAMOND (3700x BLASTX)), and protein structure search (esFragBag, 10x speedup of FragBag). Our framework can be used to achieve 'compressive omics,' and the general theory can be readily applied to data science problems outside of biology. Source code: http://gems.csail.mit.edu.
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.