ReviewBriefings in bioinformatics2021
Deep learning-based clustering approaches for bioinformatics.
Review in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 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.
The trial behind it
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Who cites it
70 citing papers in PubMed.
- Integrated evaluation and screening of salt-tolerant wheat germplasm and indices.BMC plant biology · 2026Article
- Artificial intelligence-based clustering to identify functional risk phenotypes in heart failure.Open heart · 2026Article
- A strategy combining UPLC fingerprint, chemometrics and quantitative analysis of multi-components by single-marker to control the quality ofRSC advances · 2026Article
- Deep Clustering-Based Metabolic Stratification of Non-Small Cell Lung Cancer Patients Through Integration of Somatic Mutation Profile and Network Propagation Algorithm.Interdisciplinary sciences, computational life sciences · 2025Article
- Exploring gene expression heterogeneity in burn wounds through machine learning models:Annals of medicine and surgery (2012) · 2025Article
- GLARE: discovering hidden patterns in spaceflight transcriptome using representation learning.NPJ microgravity · 2025Article
- Study on subtyping and Traditional Chinese Medicine treatment of depression based on machine learning and text mining.Journal of traditional Chinese medicine = Chung i tsa chih ying wen pan · 2025Article
- A variational deep-learning approach to modeling memory T cell dynamics.PLoS computational biology · 2025Article
- Value of Bioinformatics Models for Predicting Translational Control of Angiogenesis.Circulation research · 2025Review
- Interpretable discovery of patterns in tabular data via spatially semantic topographic maps.Nature biomedical engineering · 2025Article
- A variational deep-learning approach to modeling memory T cell dynamics.bioRxiv : the preprint server for biology · 2025Article
- Review
- Advancements in AI for Computational Biology and Bioinformatics: A Comprehensive Review.Methods in molecular biology (Clifton, N.J.) · 2025Review
- From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non-Invasive Precision Medicine in Cancer Patients.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Artificial intelligence and pediatric acute kidney injury: a mini-review and white paper.Frontiers in nephrology · 2025Review
- Artificial intelligence in outpatient management: a simulation-based study in a Chinese tertiary hospital.Frontiers in artificial intelligence · 2025Article
- Visualization Methods for DNA Sequences: A Review and Prospects.Biomolecules · 2024Review
- The statistical building blocks of animal movement simulations.Movement ecology · 2024Article
- Development of a Quality Evaluation Method for Allii Macrostemonis Bulbus Based on Solid-Phase Extraction-High-Performance Liquid Chromatography-Evaporative Light Scattering Detection Chromatographic Fingerprinting, Chemometrics, and Quantitative Analysis of Multi-Components via a Single-Marker Method.Molecules (Basel, Switzerland) · 2024Article
- ESCHR: a hyperparameter-randomized ensemble approach for robust clustering across diverse datasets.Genome biology · 2024Article
10 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
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Clustering is central to many data-driven bioinformatics research and serves a powerful computational method. In particular, clustering helps at analyzing unstructured and high-dimensional data in the form of sequences, expressions, texts and images. Further, clustering is used to gain insights into biological processes in the genomics level, e.g. clustering of gene expressions provides insights on the natural structure inherent in the data, understanding gene functions, cellular processes, subtypes of cells and understanding gene regulations. Subsequently, clustering approaches, including hierarchical, centroid-based, distribution-based, density-based and self-organizing maps, have long been studied and used in classical machine learning settings. In contrast, deep learning (DL)-based representation and feature learning for clustering have not been reviewed and employed extensively. Since the quality of clustering is not only dependent on the distribution of data points but also on the learned representation, deep neural networks can be effective means to transform mappings from a high-dimensional data space into a lower-dimensional feature space, leading to improved clustering results. In this paper, we review state-of-the-art DL-based approaches for cluster analysis that are based on representation learning, which we hope to be useful, particularly for bioinformatics research. Further, we explore in detail the training procedures of DL-based clustering algorithms, point out different clustering quality metrics and evaluate several DL-based approaches on three bioinformatics use cases, including bioimaging, cancer genomics and biomedical text mining. We believe this review and the evaluation results will provide valuable insights and serve a starting point for researchers wanting to apply DL-based unsupervised methods to solve emerging bioinformatics research problems.
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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.