|
0
|
|
Comparative review of artificial intelligence for transcriptomic biomarker discovery in coronavirus disease 2019 (COVID-19).
(academic.oup.com)
|
Khoo LY
…
Dhillon SK
Briefings in Bioinformatics
2026-05-04
|
#transcriptomics
#biomarker
#machine learning
#covid-19
#classification
|
|
0
|
|
The next paradigm in bioinformatics: a review of multi-agent systems and foundational models for end-to-end scientific discovery.
(academic.oup.com)
|
Branda F
…
Scarpa F
Briefings in Bioinformatics
2026-05-04
|
#modeling
#transcriptomics
#drug discovery
#personalized medicine
|
|
0
|
|
The impact of transcriptome assembly algorithms on downstream quantification in RNA-seq data analysis.
(academic.oup.com)
|
Tan Z
…
Yu T
Briefings in Bioinformatics
2026-05-04
|
#transcriptomics
#assembly
#sequencing
#quantification
|
|
0
|
|
SPOmiAlign: a modality-agnostic computational framework for multimodal spatial omics alignment enabled by a feature matching foundation model.
(academic.oup.com)
|
Wang Y
…
Yan Y
Briefings in Bioinformatics
2026-05-04
|
#spatial omics
#alignment
#multimodal
#transcriptomics
#proteomics
|
|
0
|
|
Porcine MutBERT: a family of lightweight genomic foundation models for functional element prediction in pigs.
(academic.oup.com)
|
Long W
…
Wang Z
Briefings in Bioinformatics
2026-05-04
|
#genomics
#modeling
#transcriptomics
#livestock
|
|
0
|
|
ceQTL: a co-expression QTL model to detect a variant that affects transcription factor binding and its target regulation.
(academic.oup.com)
|
Wang P
…
Sun Z
Briefings in Bioinformatics
2026-05-04
|
#eqtl
#transcriptomics
#transcription factor
#genomics
#modeling
|
|
0
|
|
MMP3C v2: a network-based framework decoding metabolic plasticity in rheumatoid arthritis, enabling accurate diagnosis and uncovering cell-type-specific metabolic rewiring.
(academic.oup.com)
|
Chen X
…
Huang C
Briefings in Bioinformatics
2026-05-04
|
#metabolism
#rheumatoid arthritis
#transcriptomics
#immunity
#modeling
|
|
0
|
|
ModelistsGCN: a multimodal graph convolutional network framework for single-cell spatial transcriptomic cell typing.
(academic.oup.com)
|
Konforti N
…
Alon S
Briefings in Bioinformatics
2026-05-04
|
#transcriptomics
#cell typing
#clustering
#morphology
#spatial omics
|
|
0
|
|
Cross-modality representation and multi-sample integration of spatially resolved omics data.
(academic.oup.com)
|
Li Z
…
Jiang R
Briefings in Bioinformatics
2026-05-04
|
#multi-omics
#integration
#machine learning
#transcriptomics
|
|
0
|
|
GEDO: topology-based inference of gene module activity in Sjogren's disease.
(academic.oup.com)
|
Bezier C
…
Foulquier N
Briefings in Bioinformatics
2026-05-04
|
#transcriptomics
#gene modules
#autoimmunity
#classification
|
|
0
|
|
DADA-EV: domain-adaptive diffusion autoencoder for estimating tissue- and cell-type-specific origin in extracellular vesicle transcriptomes.
(academic.oup.com)
|
Liao S
…
Dong M
Briefings in Bioinformatics
2026-05-04
|
#transcriptomics
#deconvolution
#extracellular vesicles
#liquid biopsy
#machine learning
|
|
0
|
|
MLN2SVG: domain-aware spatially variable gene detection using contrastive variational autoencoder and multi-level neighbor search.
(academic.oup.com)
|
Hussain S
…
Liu X
Briefings in Bioinformatics
2026-05-04
|
#transcriptomics
#clustering
#cancer
|
|
0
|
|
ST2HE: enhancing spatial transcriptomics interpretability via virtual staining for histological annotation.
(academic.oup.com)
|
Liu Z
…
Huang Y
Briefings in Bioinformatics
2026-05-04
|
#transcriptomics
#histology
#pathology
#imaging
#cancer
|
|
0
|
|
scMVAF: a multi-view adaptive fusion clustering approach for single-cell RNA-sequencing data.
(academic.oup.com)
|
Wang J
…
Liang Y
Briefings in Bioinformatics
2026-05-04
|
#clustering
#transcriptomics
#single-cell
#embedding
#autoencoder
|
|
0
|
|
Digital twins of ex vivo human lungs enable accurate and personalized evaluation of therapeutic efficacy.
(nature.com)
|
Zhou X
…
Sage AT
Nature Biotechnology
2026-05-04
|
#modeling
#transcriptomics
#proteomics
#metabolomics
#therapeutics
|
|
0
|
|
A comprehensive survey of computer vision methods for spatial transcriptomics.
(academic.oup.com)
|
Zhu J
…
Huo Y
Briefings in Bioinformatics
2026-05-04
|
#transcriptomics
#imaging
#histology
#morphology
#reconstruction
|
|
0
|
|
scHILL: deciphering individual-level immune cell heterogeneity with single-cell RNA sequencing data.
(academic.oup.com)
|
Wang Y
…
Song S
Briefings in Bioinformatics
2026-05-04
|
#transcriptomics
#immunity
#modeling
#heterogeneity
|
|
0
|
|
Identifying batch-integrated domains from spatial transcriptomics via graph autoencoder with contrastive learning based on cross-modality and data augmentation.
(academic.oup.com)
|
Mao Y
…
Lyu Q
Briefings in Bioinformatics
2026-05-04
|
#transcriptomics
#clustering
#integration
#autoencoders
|
|
0
|
|
Biased multi-view contrastive learning with attentive masking for spatial transcriptomic analysis.
(academic.oup.com)
|
Fu L
…
Sun H
Briefings in Bioinformatics
2026-05-04
|
#transcriptomics
#clustering
#representation learning
#tissue architecture
#embedding
|
|
0
|
|
A novel machine learning-based algorithm for eQTL identification reveals complex pleiotropic effects in the MHC region.
(academic.oup.com)
|
Li RY
…
Qin ZS
Briefings in Bioinformatics
2026-05-04
|
#eqtl
#pleiotropy
#machine learning
#transcriptomics
#variant
|
|
0
|
|
Statistical knockoffs improve biomarker discovery from transcriptomic data.
(academic.oup.com)
|
Cartier J
…
Massip F
Briefings in Bioinformatics
2026-05-04
|
#transcriptomics
#biomarker
#variable selection
#classification
#false discovery rate
|
|
0
|
|
PVAED: prior-guided variational autoencoders with diffusion denoising for interpretable single-cell representation learning.
(academic.oup.com)
|
Niu Y
…
Ma W
Briefings in Bioinformatics
2026-05-04
|
#transcriptomics
#dimensionality reduction
#clustering
#representation learning
#gene regulation
|
|
0
|
|
Current trends and challenges in deciphering single molecule resolution maps of single cell transcriptomes.
(academic.oup.com)
|
Schaeper D
…
Janga SC
Briefings in Bioinformatics
2026-05-04
|
#transcriptomics
#sequencing
#single-cell
#isoform
#barcoding
|
|
0
|
|
Semi-supervised disentangled representation learning for single-cell RNA sequencing data.
(academic.oup.com)
|
Liu H
…
Wei Z
Briefings in Bioinformatics
2026-05-04
|
#transcriptomics
#clustering
#modeling
#sequencing
|
|
0
|
|
geneSCOPE: gene spatial co-occurrence of pairwise expression.
(academic.oup.com)
|
Zhang S
…
Haeno H
Briefings in Bioinformatics
2026-05-04
|
#transcriptomics
#clustering
#network
|
|
0
|
|
AnnQ: reference-based quantification of cellular abnormality at single-cell resolution.
(academic.oup.com)
|
Lee D
…
An JY
Briefings in Bioinformatics
2026-05-04
|
#transcriptomics
#clustering
#annotation
#perturbation
|
|
0
|
|
GatorSC: multi-scale cell and gene graphs with mixture-of-experts fusion for single-cell transcriptomics.
(academic.oup.com)
|
Liu Y
…
Bian J
Briefings in Bioinformatics
2026-05-04
|
#transcriptomics
#clustering
#representation learning
#graph neural network
|
|
0
|
|
STORM: spatial transcriptomics optimization by resolution via matrix factorization.
(academic.oup.com)
|
Gurarslan D
…
Kahveci T
Briefings in Bioinformatics
2026-05-04
|
#transcriptomics
#modeling
#cancer
|
|
0
|
|
Lense: optimizing data preprocessing in single-cell omics using large language models.
(academic.oup.com)
|
Liu J
…
Ji Z
Briefings in Bioinformatics
2026-05-04
|
#transcriptomics
#preprocessing
#clustering
#sequencing
|
|
0
|
|
A unified framework for selecting and evaluating cell-type-specific gene co-expressions in single-cell data.
(academic.oup.com)
|
Shan X
…
Zhao H
Briefings in Bioinformatics
2026-05-04
|
#transcriptomics
#clustering
#benchmarking
#coexpression
|
|
0
|
|
Machine learning-guided multimodal profiling defines perturbed immune states at the time of cancer diagnosis.
(academic.oup.com)
|
Berlin P
…
Maletzki C
Briefings in Bioinformatics
2026-05-04
|
#immunity
#cancer
#transcriptomics
#modeling
#inflammation
|
|
0
|
|
USADAE: a deep learning approach to disentangle hidden covariates in RNA-seq data.
(academic.oup.com)
|
Chen X
…
Liu X
Briefings in Bioinformatics
2026-05-04
|
#transcriptomics
#deep learning
#batch correction
#autoencoder
#confounding
|
|
0
|
|
Empowering multifaceted analysis of spatial transcriptomics data with RGAST.
(academic.oup.com)
|
Gong Y
…
Yu Z
Briefings in Bioinformatics
2026-05-04
|
#transcriptomics
#clustering
#neuroimaging
#modeling
|
|
0
|
|
Continuous multi-omics pathway enrichment analysis resolves hidden functional heterogeneity.
(academic.oup.com)
|
Amerifar S
…
Buettner F
Briefings in Bioinformatics
2026-05-04
|
#transcriptomics
#pathway-enrichment
#multi-omics
#proteomics
#bayesian-methods
|
|
0
|
|
scDeepAPA: a deep learning framework for single-cell alternative polyadenylation identification.
(academic.oup.com)
|
Liang J
…
Song Q
Briefings in Bioinformatics
2026-05-04
|
#transcriptomics
#polyadenylation
#single-cell
#deep learning
#isoforms
|
|
0
|
|
Chronic intermittent hypoxia exacerbates hepatic steatosis in a microbiota-dependent manner in lean mice.
(journals.asm.org)
|
Zhang X
…
Yin S
mSystems
2026-05-04
|
#microbiome
#metagenomics
#hepatic steatosis
#metabolomics
#transcriptomics
|
|
0
|
|
scMarkerGene: an interpretable neural network framework for cell-type-specific marker gene discovery.
(academic.oup.com)
|
Zhang J
…
Zhao Y
Briefings in Bioinformatics
2026-05-04
|
#transcriptomics
#clustering
#neural network
#cell-type
#heterogeneity
|
|
0
|
|
Reflections on the use of LLMs for cell annotation.
(academic.oup.com)
|
Ray PP
Briefings in Bioinformatics
2026-05-04
|
#transcriptomics
#annotation
#machine learning
#bioinformatics
|
|
0
|
|
APAdeg enhances differentially expressed gene inference by leveraging site-specific signals in APA-seq data.
(academic.oup.com)
|
Sarker B
…
Xu C
Briefings in Bioinformatics
2026-05-04
|
#transcriptomics
#polyadenylation
#sequencing
#cancer
|
|
0
|
|
scTumorDrug: predicting cell-type-specific drug responses for heterogeneous tumors.
(academic.oup.com)
|
Zhang Q
…
Peng C
Briefings in Bioinformatics
2026-05-04
|
#transcriptomics
#cancer
#pharmacogenomics
#heterogeneity
#precision medicine
|
|
0
|
|
Transformers for single-cell RNA sequencing: a survey.
(academic.oup.com)
|
Hu T
…
Wei Z
Briefings in Bioinformatics
2026-05-04
|
#transcriptomics
#single-cell
#deep learning
#dimensionality reduction
|
|
0
|
|
Optimal transport for label transfer in single-cell multi-omics integration.
(academic.oup.com)
|
Ren J
…
Liu Y
Briefings in Bioinformatics
2026-05-04
|
#multi-omics
#single-cell
#transcriptomics
#epigenomics
#integration
|
|
0
|
|
STED: flexible cross-modal topic modeling infers cell-type-specific regulatory landscapes from bulk epigenomics.
(academic.oup.com)
|
Liao Y
…
Zhao D
Briefings in Bioinformatics
2026-05-04
|
#epigenomics
#deconvolution
#transcriptomics
#regulatory
#topic-modeling
|
|
0
|
|
Cell line-specific gene network enrichment analysis for interpreting continuous phenotypes.
(academic.oup.com)
|
Park H
…
Miyano S
Briefings in Bioinformatics
2026-05-04
|
#networks
#enrichment
#pathways
#transcriptomics
|
|
0
|
|
Navigating cell maps by deep learning integration of single-cell and spatially resolved transcriptomics.
(academic.oup.com)
|
Chen Y
Briefings in Bioinformatics
2026-05-04
|
#transcriptomics
#spatial-biology
#single-cell
#deep learning
#cancer
|
|
0
|
|
Cell type-specific dissection of cell death programs during ovarian aging.
(academic.oup.com)
|
Wang R
…
Xue H
Briefings in Bioinformatics
2026-05-04
|
#transcriptomics
#apoptosis
#aging
#deconvolution
#ovarian
|
|
0
|
|
STGAT: spatial domain identification of consecutive slices based on graph contrastive learning.
(academic.oup.com)
|
Feng Y
…
Jin S
Briefings in Bioinformatics
2026-05-04
|
#transcriptomics
#clustering
#graph learning
|
|
0
|
|
Efficient reconstruction of full-length RNA isoforms using ISAtools and large-scale PacBio circular consensus sequencing data.
(academic.oup.com)
|
Chen H
…
Shi ZX
Briefings in Bioinformatics
2026-05-04
|
#isoforms
#sequencing
#transcriptomics
#splicing
|