ReviewBriefings in bioinformatics2024
Deep learning in structural bioinformatics: current applications and future perspectives.
Review in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 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
28 citing papers in PubMed.
- Artificial intelligence-driven discovery of coumarin-based therapeutics: Revolutionizing target identification and validation.Pharmaceutical science advances · 2026Review
- Pockets to products: a data-driven approach for classification of monoterpene synthases.Digital discovery · 2026Article
- Machine learning-enabled chemical ecology for integrated pest management: from volatiles to field applications.Journal of insect science (Online) · 2026Review
- Article
- Inferring dynamic information from protein structures by Gaussian integrals and deep learning.Bioinformatics (Oxford, England) · 2026Article
- Artificial intelligence in biologic drug discovery: A review of methodological evolution and therapeutic applications.Acta pharmaceutica Sinica. B · 2026Review
- Artificial intelligence-driven multi-omics integration for plant enhancement: advances, challenges, and future perspectives.Functional & integrative genomics · 2026Review
- Integrating artificial intelligence and in-silico modeling to redefine therapeutic strategies for peritoneal seeding: A disulfiram repurposing concept.Pakistan journal of medical sciences · 2026Article
- CellMate─A Deep Learning-Assisted Single-Cell Data Processing Platform.Analytical chemistry · 2026Article
- Decoding the reproductive microbiome: enabling clinical and biological insights through machine and deep learning.Frontiers in endocrinology · 2026Review
- Animal Venoms as Peptide Libraries for the Discovery of Antiglioblastoma Agents.Biochemistry research international · 2026Review
- Beyond AlphaFold: Charting the Future of AI in Protein Structure Prediction.Current drug targets · 2026Article
- Exploiting fuzzy weights in CNN model-based taxonomic classification of 500-bp sequence bacterial dataset.Scientific reports · 2025Article
- BridgeNet: a high-efficiency framework integrating sequence and structure for protein and enzyme function prediction.Briefings in bioinformatics · 2025Article
- Inferring Dynamic Information from Protein Structures by Gaussian Integrals and Deep Learning.bioRxiv : the preprint server for biology · 2025Article
- CryoDataBot: a pipeline to curate cryoEM datasets for AI-driven structural biology.bioRxiv : the preprint server for biology · 2025Article
- Network analysis of multivariate time series data in biological systems: methods and applications.Briefings in bioinformatics · 2025Article
- "Weibing" in traditional Chinese medicine-biological basis and mathematical representation of disease-susceptible state.Acta pharmaceutica Sinica. B · 2025Review
- CryoDataBot: a pipeline to curate cryoEM datasets for AI-driven structural biology.GigaScience · 2025Article
- ToxDL 2.0: Protein toxicity prediction using a pretrained language model and graph neural networks.Computational and structural biotechnology journal · 2025Article
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
In this review article, we explore the transformative impact of deep learning (DL) on structural bioinformatics, emphasizing its pivotal role in a scientific revolution driven by extensive data, accessible toolkits and robust computing resources. As big data continue to advance, DL is poised to become an integral component in healthcare and biology, revolutionizing analytical processes. Our comprehensive review provides detailed insights into DL, featuring specific demonstrations of its notable applications in bioinformatics. We address challenges tailored for DL, spotlight recent successes in structural bioinformatics and present a clear exposition of DL-from basic shallow neural networks to advanced models such as convolution, recurrent, artificial and transformer neural networks. This paper discusses the emerging use of DL for understanding biomolecular structures, anticipating ongoing developments and applications in the realm of structural bioinformatics.
Indexed as
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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.