|
0
|
|
Graph-based drug-target interaction modeling: from representation learning to output-driven drug discovery.
(academic.oup.com)
|
Nguyen T
…
Nguyen G
Briefings in Bioinformatics
2026-07-03
|
#machine learning
#drug discovery
#computational modeling
#protein-interactions
#deep learning
|
|
0
|
|
WDCN: a comprehensive neural network based approach for estimating breast cancer risk.
(academic.oup.com)
|
Xu H
…
Lu J
Briefings in Bioinformatics
2026-07-03
|
#breast cancer
#deep learning
#risk stratification
#prediction
#polygenic
|
|
0
|
|
STRESS: spatial transcriptomics resolution enhancing method based on the state-space model.
(academic.oup.com)
|
Wang X
…
Liu X
Briefings in Bioinformatics
2026-07-03
|
#transcriptomics
#gene expression
#deep learning
#tissue
|
|
0
|
|
Topological deep learning for drug-target interaction, virtual screening, and docking scoring: a practical, benchmark-driven review.
(academic.oup.com)
|
Suay-Garcia B
…
Falco A
Briefings in Bioinformatics
2026-07-03
|
#deep learning
#drug discovery
#topology
#docking
#virtual screening
|
|
0
|
|
DeepKOALA: a scalable deep learning framework for KEGG Orthology assignment.
(academic.oup.com)
|
Yu Z
…
Ogata H
Briefings in Bioinformatics
2026-07-03
|
#deep learning
#annotation
#sequencing
#metagenomics
|
|
0
|
|
DeepPANB: integrating protein language model with PaiNN equivariant graph neural networks for prediction of protein-nucleic acid binding sites.
(academic.oup.com)
|
Zhang J
…
Li C
Briefings in Bioinformatics
2026-07-03
|
#deep learning
#protein-nucleic-acid-binding
#graph neural network
#binding-site-prediction
|
|
0
|
|
GLMYsymm: inferring symmetry categories of protein complexes from single sequences using persistent GLMY homology.
(academic.oup.com)
|
Zhai J
…
Gong X
Briefings in Bioinformatics
2026-07-03
|
#symmetry
#structure prediction
#proteomics
#deep learning
#topology
|
|
0
|
|
Deep learning models for cell cycle phase prediction from single-cell RNA sequencing data.
(academic.oup.com)
|
Akhter H
…
Adjeroh DA
Briefings in Bioinformatics
2026-07-03
|
#transcriptomics
#machine learning
#cell cycle
#deep learning
|
|
0
|
|
Investigating the anticancer activity of eravacycline in pancreatic cancer via target-based deep learning and experimental validation.
(academic.oup.com)
|
Jabarin A
…
Ben-Shabat S
Briefings in Bioinformatics
2026-07-03
|
#pancreatic cancer
#deep learning
#drug discovery
#p53
#computational-screening
|
|
0
|
|
An overview of self-supervised deep learning applications to molecular data.
(academic.oup.com)
|
Borras Ferris L
…
Muller H
Briefings in Bioinformatics
2026-07-03
|
#deep learning
#self-supervised learning
#omics
#sequencing
#representation learning
|
|
0
|
|
K-attention: a biologically informed attention operator for data-efficient sequence-based omics modeling.
(academic.oup.com)
|
Liu T
…
Gao G
Briefings in Bioinformatics
2026-07-03
|
#deep learning
#omics
#sequence modeling
#attention-mechanisms
#machine learning
|
|
0
|
|
Bridging local-global transmembrane protein contexts with contrastive pretraining for alignment-free pathogenicity prediction.
(academic.oup.com)
|
Bao Y
…
Lin GN
Briefings in Bioinformatics
2026-07-03
|
#pathogenicity
#transmembrane
#deep learning
#variant prediction
#protein structure
|
|
0
|
|
VCboost: reducing false positives in long-read variant calling for single-nucleotide polymorphism and indel detection in challenging genomic regions.
(academic.oup.com)
|
Zhang Y
…
Dai Q
Briefings in Bioinformatics
2026-07-03
|
#sequencing
#algorithm
#deep learning
#indels
|
|
0
|
|
Eras of bioinformatics technologies from command-line interfaces to artificial intelligence (AI) chatbots.
(academic.oup.com)
|
Truong VQ
…
Ritchie MD
Briefings in Bioinformatics
2026-07-03
|
#bioinformatics
#ai
#sequencing
#deep learning
#structural biology
|
|
0
|
|
scHashFormer: a hash-driven graph transformer for scalable scRNA-seq clustering.
(academic.oup.com)
|
Lu Z
…
Liang J
Briefings in Bioinformatics
2026-07-03
|
#transcriptomics
#clustering
#single-cell sequencing
#deep learning
|
|
0
|
|
DeNovoSeer:a deep learning framework for pathogenicity prediction of De novo mutations.
(academic.oup.com)
|
Qiu R
…
Zhao G
Briefings in Bioinformatics
2026-07-03
|
#pathogenicity
#variants
#deep learning
#prediction
#annotation
|
|
0
|
|
UDEC-MO: an uncertainty-guided deep embedded clustering framework for bulk and single-cell multi-omics data.
(academic.oup.com)
|
Li J
…
Tang J
Briefings in Bioinformatics
2026-07-03
|
#multi-omics
#clustering
#single-cell
#deep learning
#cancer
|
|
0
|
|
CTMAP: an adversarial cross-modal learning framework for accurate and robust cell-type annotation in single-cell resolution spatial transcriptomics.
(academic.oup.com)
|
Wang Y
…
Sun D
Briefings in Bioinformatics
2026-07-03
|
#transcriptomics
#cell-type
#deep learning
#integration
|
|
0
|
|
LipiDetective: a deep learning model for the identification of molecular lipid species in tandem mass spectra.
(academic.oup.com)
|
Wurf V
…
Pauling JK
Briefings in Bioinformatics
2026-07-03
|
#mass spectrometry
#lipidomics
#deep learning
#annotation
|
|
0
|
|
ERICA-trio: an outgroup-free deep learning method for topology inference and introgression detection.
(academic.oup.com)
|
Zhang Y
…
Zhang W
Briefings in Bioinformatics
2026-07-03
|
#introgression
#phylogenetics
#deep learning
#admixture
#evolution
|