ArticleTurkish journal of biology = Turk biyoloji dergisi2023
Deep learning in bioinformatics.
Article in Turkish journal of biology = Turk biyoloji dergisi, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence Technologies in Nursing Clinical Decision-Making: An Umbrella Review.Journal of advanced nursing · 2026Pooled it
- Cancer-associated fusion transcripts: mechanisms, functional roles, and clinical implications.Clinical and experimental medicine · 2026Review
- ESM2-Kcr: a novel model combining protein language model and deep learning methods for predicting lysine crotonylation sites.Journal of computer-aided molecular design · 2026Article
- Genotoxicity of cancer therapies and the risk of secondary malignancies: toward personalized prevention.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- TIMEL: Deep learning-statistical integration reveals spatial stromal and immune signatures of aggressive colon cancer.Journal of pathology informatics · 2026Article
- Data-intensive immune network modelling for One Health.Briefings in bioinformatics · 2026Review
- Olfactory Science and Technology in Prostate Cancer Diagnosis: From Invertebrate Models to Artificial Intelligence.Life (Basel, Switzerland) · 2026Review
- The potential of deep learning on the discovery of new genes implicated in differences of sex development.Computational and structural biotechnology journal · 2026Article
- Artificial Intelligence in Ocular Transcriptomics: Applications of Unsupervised and Supervised Learning.Cells · 2025Review
- Innovative data augmentation strategy for deep learning on biological datasets with limited gene representations focused on chloroplast genomes.Scientific reports · 2025Article
- Conventional and cutting-edge advances in plant virus detection: emerging trends and techniques.3 Biotech · 2025Review
- FSFT6mA: a feature-synthesis fine-tuning framework for DNA 6mA site prediction.Frontiers in genetics · 2025Article
- Article
- Neural signals, machine learning, and the future of inner speech recognition.Frontiers in human neuroscience · 2025Review
- Machine learning-based drought prediction using Palmer Drought Severity Index and TerraClimate data in Ethiopia.PloS one · 2025Article
- Artificial Intelligence Transforming Post-Translational Modification Research.Bioengineering (Basel, Switzerland) · 2024Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning is a powerful machine learning technique that can learn from large amounts of data using multiple layers of artificial neural networks. This paper reviews some applications of deep learning in bioinformatics, a field that deals with analyzing and interpreting biological data. We first introduce the basic concepts of deep learning and then survey the recent advances and challenges of applying deep learning to various bioinformatics problems, such as genome sequencing, gene expression analysis, protein structure prediction, drug discovery, and disease diagnosis. We also discuss future directions and opportunities for deep learning in bioinformatics. We aim to provide an overview of deep learning so that bioinformaticians applying deep learning models can consider all critical technical and ethical aspects. Thus, our target audience is biomedical informatics researchers who use deep learning models for inference. This review will inspire more bioinformatics researchers to adopt deep-learning methods for their research questions while considering fairness, potential biases, explainability, and accountability.
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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.