ReviewInternational journal of molecular sciences2021
Incorporating Machine Learning into Established Bioinformatics Frameworks.
Review in International journal of molecular sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 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
69 citing papers in PubMed.
- Discovering potential key biomarkers and molecular mechanisms in Chronic Obstructive Pulmonary Disease and rheumatoid arthritis using integrated bioinformatics and machine learning approaches.Journal, genetic engineering & biotechnology · 2026Article
- MarkerScout: A Disease-Agnostic Machine Learning Framework for Biomarker Prediction from Multi-Scale Mechanistic Models.bioRxiv : the preprint server for biology · 2026Article
- Identification of potential biomarkers and therapeutic targets for liver cirrhosis based on Mendelian randomization and machine learning.Biochemistry and biophysics reports · 2026Article
- Identification and validation of PANX1 as an inflammasome-related biomarker in gestational diabetes mellitus: insights from machine learning and experimental approaches.Mammalian genome : official journal of the International Mammalian Genome Society · 2026Article
- Evolutionary Bioinformatics Expands its Breadth.Evolutionary bioinformatics online · 2026Article
- Article
- Protein Language Models in Virology: A Review of Advances and Applications.Methods in molecular biology (Clifton, N.J.) · 2026Review
- Bioinformatics and machine learning approaches to explore the biomarkers in fatty acid degradation linked to osteoarthritis.Frontiers in immunology · 2026Article
- Integrating co-expression network analysis and machine learning to reveal the regulatory landscape ofPeerJ · 2026Article
- Genomic determinants of antifungal activity ofFrontiers in microbiology · 2026Article
- Article
- ACmix-Swin Deep Learning of 4-Day-OldGenes · 2025Article
- A formal explanation space for the simultaneous clustering of neurologic diseases based on their signs and symptoms.BMC medical informatics and decision making · 2025Article
- Using structured libraries, selection, and machine learning to rapidly explore the sequence space of a fluorescent deoxyribozyme.Nucleic acids research · 2025Article
- DemuxTrans: Transformer and temporal convolution network for accurate barcode demultiplexing in nanopore sequencing.Bioinformatics (Oxford, England) · 2025Article
- Article
- Structural and evolutionary insights into understudied bacterial serine-threonine pseudokinase families.Biochemical Society transactions · 2025Review
- RNA Therapeutics: Delivery Problems and Solutions-A Review.Pharmaceutics · 2025Review
- Exploring the role of lipid metabolism related genes and immune microenvironment in periodontitis by integrating machine learning and bioinformatics analysis.Scientific reports · 2025Article
- Popfinder: A Highly Effective Artificial Neural Network Package for Genetic Population Assignment.Molecular ecology resources · 2025Article
9 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
3 authors.
Funding
Abstract
The exponential growth of biomedical data in recent years has urged the application of numerous machine learning techniques to address emerging problems in biology and clinical research. By enabling the automatic feature extraction, selection, and generation of predictive models, these methods can be used to efficiently study complex biological systems. Machine learning techniques are frequently integrated with bioinformatic methods, as well as curated databases and biological networks, to enhance training and validation, identify the best interpretable features, and enable feature and model investigation. Here, we review recently developed methods that incorporate machine learning within the same framework with techniques from molecular evolution, protein structure analysis, systems biology, and disease genomics. We outline the challenges posed for machine learning, and, in particular, deep learning in biomedicine, and suggest unique opportunities for machine learning techniques integrated with established bioinformatics approaches to overcome some of these challenges.
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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.