|
0
|
|
Unveiling patterns: an exploration of machine learning techniques for unsupervised feature selection in single-cell data.
(academic.oup.com)
|
Chatterjee N
…
Alimadadi A
Briefings in Bioinformatics
2026-01-07
|
#transcriptomics
#machine learning
#clustering
#dimensionality reduction
|
|
0
|
|
GATCL: graph attention network meets contrastive learning for spatial domain identification.
(academic.oup.com)
|
Mu J
…
Zhao T
Briefings in Bioinformatics
2026-01-07
|
#deep learning
#multi-omics
#clustering
#spatial omics
|
|
0
|
|
Spatial information matters: are traditional imputation methods effective for spatial transcriptomics data?
(academic.oup.com)
|
Hafiz F
…
Shatabda S
Briefings in Bioinformatics
2026-01-07
|
#transcriptomics
#clustering
#imputation
#dropout
|
|
0
|
|
PathCLAST: pathway-augmented contrastive learning with attention for interpretable spatial transcriptomics.
(academic.oup.com)
|
Noh M
…
Lim S
Briefings in Bioinformatics
2026-01-07
|
#transcriptomics
#pathway
#tumor
#clustering
|
|
0
|
|
Clustering single-cell multi-omics data via multi-subspace contrastive learning with structural smoothness.
(academic.oup.com)
|
Ding Y
…
Zheng C
Briefings in Bioinformatics
2026-01-07
|
#single-cell
#multi-omics
#integration
#clustering
#contrastive learning
|
|
0
|
|
3D reconstruction of spatial transcriptomics with spatial pattern enhanced graph convolutional neural network.
(academic.oup.com)
|
Tang C
…
Xu L
Briefings in Bioinformatics
2026-01-07
|
#transcriptomics
#clustering
#reconstruction
#neural network
|
|
0
|
|
Turning heterogeneity of statistical epistasis networks to an advantage.
(academic.oup.com)
|
Duroux D
…
Van Steen K
Briefings in Bioinformatics
2026-01-07
|
#epistasis
#clustering
#methodology
#interaction
#genome-wide association
|
|
0
|
|
BayesRare: Bayesian mixture model for population-level rare cell type detection in multi-subject single-cell RNA sequencing data.
(academic.oup.com)
|
Yan Y
…
Wu H
Briefings in Bioinformatics
2026-01-07
|
#modeling
#immunity
#transcriptomics
#clustering
|
|
0
|
|
Adaptive multi-view information bottleneck for multi-omics data clustering.
(academic.oup.com)
|
Tian Z
…
Fu S
Briefings in Bioinformatics
2026-01-07
|
#modeling
#transcriptomics
#single-cell
#multi-omics
#clustering
|
|
0
|
|
SGCRNA: spectral clustering-guided co-expression network analysis without scale-free constraints for multi-omic data.
(academic.oup.com)
|
Osone T
…
Takarada T
Briefings in Bioinformatics
2026-01-07
|
#transcriptomics
#biomarker
#clustering
#network analysis
|
|
0
|
|
UBD: incorporating uncertainty in cell type proportion estimates from bulk samples to infer cell-type-specific profiles.
(academic.oup.com)
|
Cheng Y
…
Zhao H
Briefings in Bioinformatics
2026-01-07
|
#transcriptomics
#clustering
#deconvolution
#bayesian
|
|
0
|
|
PHScaffolding: a hypergraph clustering and dual-weight integration strategy for scaffolding with Pore-C reads.
(academic.oup.com)
|
Su Q
…
Guo F
Briefings in Bioinformatics
2026-01-07
|
#assembly
#sequencing
#clustering
#scaffolding
|
|
0
|
|
iceDP: identifying inter-chromatin engagement via density peaks clustering algorithm.
(academic.oup.com)
|
Chen R
…
He J
Briefings in Bioinformatics
2026-01-07
|
#chromatin
#clustering
#topology
|
|
0
|
|
Imputing missing values in single-cell RNA-sequencing data: a statistical and machine learning-based approach.
(academic.oup.com)
|
Shamsuzzaman AFM
…
Mukhopadhyay A
Briefings in Bioinformatics
2026-01-07
|
#modeling
#transcriptomics
#clustering
#imputation
|
|
0
|
|
Advances and challenges in single-cell RNA sequencing data analysis: a comprehensive review.
(academic.oup.com)
|
Nesari AM
…
Ghorbian S
Briefings in Bioinformatics
2026-01-07
|
#modeling
#immunity
#transcriptomics
#clustering
#preprocessing
|
|
0
|
|
SHEST: single-cell-level artificial intelligence from haematoxylin and eosin morphology for cell-type prediction and spatial transcriptomics reconstruction.
(academic.oup.com)
|
Jeong H
…
Choi YL
Briefings in Bioinformatics
2026-01-07
|
#transcriptomics
#cancer
#clustering
#morphology
|
|
0
|
|
An unsupervised method for spatial transcriptomics analysis based on adversarial autoencoder.
(academic.oup.com)
|
Lan W
…
Pan Y
Briefings in Bioinformatics
2026-01-07
|
#transcriptomics
#clustering
#denoising
|
|
0
|
|
Signal-based spatial domain identification of spatially resolved transcriptomics with multigraph fusion.
(academic.oup.com)
|
Ma Y
…
Ma X
Briefings in Bioinformatics
2026-01-07
|
#transcriptomics
#cancer
#clustering
|