ReviewSpringerPlus2016
An overview of topic modeling and its current applications in bioinformatics.
Review in SpringerPlus, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 84 papers.
What it found
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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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Who cites it
84 citing papers in PubMed.
- A Mechanism-Centered Text-Mining Landscape of Mitochondrial Bioenergetics and Signaling in Disease Research.International journal of molecular sciences · 2026Review
- Unsupervised characterization of 100,272 EHR patients identifies high-risk groups and comorbidities linked to premature aging.NPJ digital medicine · 2026Article
- Evolution of research on global amphibian declines.Conservation biology : the journal of the Society for Conservation Biology · 2026Review
- Unveiling patterns: an exploration of machine learning techniques for unsupervised feature selection in single-cell data.Briefings in bioinformatics · 2026Review
- AI-driven antimicrobial peptide characterization unveils novel motifs for drug design.Scientific reports · 2025Article
- SEEK-VEC: Robust Latent Structure Discovery via Ensemble Topic Modeling.bioRxiv : the preprint server for biology · 2025Article
- Estimating Genome-Wide Phylogenies Using Probabilistic Topic Modeling.Systematic biology · 2025Article
- Detecting signatures underlying the composition of biological data.Nucleic acids research · 2025Article
- Latent Dirichlet Allocation reveals tomato root-associated bacterial interactions responding to hairy root disease.Environmental microbiome · 2025Article
- Finding Case Report Nuggets: A Web-Based Tool for Mining and Enhancing the Value of Clinical Case Reports.medRxiv : the preprint server for health sciences · 2025Article
- Exploring the latent space of transcriptomic data with topic modeling.NAR genomics and bioinformatics · 2025Article
- Food Access in New York City During the COVID-19 Pandemic: Social Media Monitoring Study.JMIR formative research · 2025Article
- Demixer: a probabilistic generative model to delineate different strains of a microbial species in a mixed infection sample.Bioinformatics (Oxford, England) · 2025Article
- What Do We Gain When Tolerating Loss? The Information Bottleneck Wrings Out Recombination.Molecular biology and evolution · 2025Article
- Topic modeling analysis of the Allen Human Brain Atlas.Scientific reports · 2025Article
- A neuro-immune axis of transcriptomic dysregulation within the subgenual anterior cingulate cortex in schizophrenia.bioRxiv : the preprint server for biology · 2025Article
- Identifying Yalom's group therapeutic factors in anonymous mental health discussions on Reddit: a mixed-methods analysis using large language models, topic modeling and human supervision.Frontiers in psychiatry · 2025Article
- Enabling near real time use of wildlife necropsy data: Text-mining approaches to derive interactive dashboard displays.PloS one · 2025Article
- AI-powered topic modeling: comparing LDA and BERTopic in analyzing opioid-related cardiovascular risks in women.Experimental biology and medicine (Maywood, N.J.) · 2025Article
- Topic modeling and content analysis of people's anxiety-related concerns raised on a computer-mediated health platform.Scientific reports · 2024Article
24 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWith the rapid accumulation of biological datasets, machine learning methods designed to automate data analysis are urgently needed. In recent years, so-called topic models that originated from the field of natural language processing have been receiving much attention in bioinformatics because of their interpretability. Our aim was to review the application and development of topic models for bioinformatics. DESCRIPTION: This paper starts with the description of a topic model, with a focus on the understanding of topic modeling. A general outline is provided on how to build an application in a topic model and how to develop a topic model. Meanwhile, the literature on application of topic models to biological data was searched and analyzed in depth. According to the types of models and the analogy between the concept of document-topic-word and a biological object (as well as the tasks of a topic model), we categorized the related studies and provided an outlook on the use of topic models for the development of bioinformatics applications.
conclusionTopic modeling is a useful method (in contrast to the traditional means of data reduction in bioinformatics) and enhances researchers' ability to interpret biological information. Nevertheless, due to the lack of topic models optimized for specific biological data, the studies on topic modeling in biological data still have a long and challenging road ahead. We believe that topic models are a promising method for various applications in bioinformatics research.
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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.