ReviewBriefings in bioinformatics2015
A primer to frequent itemset mining for bioinformatics.
Review in Briefings in bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Common auricular acupoint patterns for postoperative recovery across laparoscopic surgeries: a data mining-based systematic analysis.Frontiers in medicine · 2026Pooled it
- Association rule mining of the human gut microbiome.Science China. Life sciences · 2026Article
- Environmental factors influencing hormone receptor positive breast cancer incidence: integrating chemical signatures from dust wipes with self-reported sources of exposure.Journal of exposure science & environmental epidemiology · 2026Article
- scEVE: a single-cell RNA-seq ensemble clustering algorithm capitalizing on the differences of predictions between multiple clustering methods.NAR genomics and bioinformatics · 2025Article
- Establishing a Pharmacoinformatics Repository of Approved Medicines: A Database to Support Drug Product Development.Molecular pharmaceutics · 2025Article
- A Dataset for Constructing the Network Pharmacology of Overactive Bladder and Its Application to Reveal the Potential Therapeutic Targets of Rhynchophylline.Pharmaceuticals (Basel, Switzerland) · 2024Article
- Causes of death among United States decedents with ALS: An eye toward delaying mortality.Annals of clinical and translational neurology · 2023Article
- Key therapeutic targets implicated at the early stage of hepatocellular carcinoma identified through machine-learning approaches.Scientific reports · 2023Article
- Uncovering key molecular mechanisms in the early and late-stage of papillary thyroid carcinoma using association rule mining algorithm.PloS one · 2023Article
- A diagnostic miRNA panel to detect recurrence of ovarian cancer through artificial intelligence approaches.Journal of cancer research and clinical oncology · 2023Article
- Usefulness of Vaccine Adverse Event Reporting System for Machine-Learning Based Vaccine Research: A Case Study for COVID-19 Vaccines.International journal of molecular sciences · 2022Article
- Application of Pattern Mining Methods to Assess Exposures to Multiple Airborne Chemical Agents in Two Large Occupational Exposure Databases from France.International journal of environmental research and public health · 2022Article
- Pattern discovery and disentanglement on relational datasets.Scientific reports · 2021Article
- Explanation and prediction of clinical data with imbalanced class distribution based on pattern discovery and disentanglement.BMC medical informatics and decision making · 2021Article
- Learning the Regulatory Code of Gene Expression.Frontiers in molecular biosciences · 2021Review
- The relationship between non-motor features and weight-loss in the premanifest stage of Huntington's disease.PloS one · 2021Article
- Extending Association Rule Mining to Microbiome Pattern Analysis: Tools and Guidelines to Support Real Applications.Frontiers in bioinformatics · 2021Article
- Deep learning suggests that gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure.Nature communications · 2020Article
- Identification of novel activators of the metal responsive transcription factor (MTF-1) using a gene expression biomarker in a microarray compendium.Metallomics : integrated biometal science · 2020Article
- MESSAR: Automated recommendation of metabolite substructures from tandem mass spectra.PloS one · 2020Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Over the past two decades, pattern mining techniques have become an integral part of many bioinformatics solutions. Frequent itemset mining is a popular group of pattern mining techniques designed to identify elements that frequently co-occur. An archetypical example is the identification of products that often end up together in the same shopping basket in supermarket transactions. A number of algorithms have been developed to address variations of this computationally non-trivial problem. Frequent itemset mining techniques are able to efficiently capture the characteristics of (complex) data and succinctly summarize it. Owing to these and other interesting properties, these techniques have proven their value in biological data analysis. Nevertheless, information about the bioinformatics applications of these techniques remains scattered. In this primer, we introduce frequent itemset mining and their derived association rules for life scientists. We give an overview of various algorithms, and illustrate how they can be used in several real-life bioinformatics application domains. We end with a discussion of the future potential and open challenges for frequent itemset mining in the life sciences.
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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.