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Novel two-stage deep learning-based approach applied to gene expression data pertaining to esophageal adenocarcinoma boosting biological knowledge discovery

The paper introduces a two-stage deep learning approach for gene expression analysis in esophageal adenocarcinoma, identifying more expressed genes than traditional methods.

Implicit Hierarchical Tensor Decomposition of Single-Cell Four-Omics Data Reveals Cell-Type-Linked Candidate Enhancer--Promoter-Associated Programs

This paper develops a tensor decomposition framework to integrate single-cell four-omics data, revealing cell-type-specific enhancer-promoter programs that cannot be identified by individual modalities.

Interpretable Integration of CITE-seq RNA and ADT Profiles Without Explicit Modality-Weight Tuning via Tensor Decomposition-Based Unsupervised Feature Extraction

The paper introduces a tensor decomposition method for integrating CITE-seq RNA and ADT data without modality-weight tuning, showing competitive but not superior performance compared to existing methods.

Novel 4D tensor decomposition-based approach integrating tri-omics profiling data can identify functionally relevant gene clusters

This paper introduces a 4D tensor decomposition method to integrate transcriptome, Ribo-seq, and proteome data, identifying gene clusters related to ribosome stacking and translational buffering.

Automatic Bevacizumab Response Prediction in Ovarian Cancer from Digital Pathology Images via Novel AI-based Computational Pipeline

The paper presents an AI-based pipeline for predicting bevacizumab response in ovarian cancer using digital pathology images, achieving improved AUC performance compared to existing models.

Gene and cell line efficiency of CRISPR computed by tensor decomposition in genome-wide CRISPR-Cas9 knockout screens

This study uses tensor decomposition to analyze CRISPR-Cas9 knockout screens, distinguishing essential from non-essential genes with performance comparable to JACKS without requiring control samples or logarithmic transformations.

Use of RHO GTPase by L1 GABAergic neurons in frontal cortex and L6 glutamatergic neurons in prefrontal cortex differentiates states of unconsciousness

The paper compares gene expression profiles from sleep and anaesthesia-induced unconsciousness to identify gene sets associated with specific neuronal populations and pathways.

De novo single-cell biological analysis of drug resistance in human melanoma through a novel deep learning-powered approach

The paper develops a deep learning approach to analyze drug resistance in melanoma using scRNA-seq data, identifying key genes and drugs associated with resistance.

Novel tensor decomposition-based approach for cell type deconvolution in Visium datasets when reference scRNA-seqs include multiple minor cell types

This paper introduces a tensor decomposition-based method for cell-type deconvolution in Visium spatial transcriptomics data, showing success where conventional methods fail due to inclusion of multiple minor cell types in reference profiles.

Novel AI-powered computational method using tensor decomposition for identification of common optimal bin sizes when integrating multiple Hi-C datasets

This paper introduces a tensor decomposition method to automatically determine common optimal bin sizes for integrating multiple Hi-C datasets without manual parameter tuning.

Novel artificial intelligence-based identification of drug-gene-disease interaction using protein-protein interaction

This study introduces a novel AI-based method using tensor decomposition to identify drug-gene-disease interactions from protein-protein interactions without prior disease or drug information.

Novel Automatic Classification of Human Adult Lung Alveolar Type II Cells Infected with SARS-CoV-2 through Deep Transfer Learning Approach

This paper develops a deep transfer learning approach to classify healthy and SARS-CoV-2 infected human alveolar type II cells using electron microscopy images, achieving high accuracy and speed.

A novel interpretable deep transfer learning combining diverse learnable parameters for improved T2D prediction based on single-cell gene regulatory networks

This paper introduces an interpretable deep transfer learning model for T2D prediction using single-cell gene regulatory network images, achieving higher balanced accuracy than baseline methods.

maGENEgerZ: An Efficient AI-Based Framework Can Extract More Expressed Genes and Biological Insights Underlying Breast Cancer Drug Response Mechanism

The paper introduces maGENEgerZ, an AI-based framework that identifies more expressed genes and biological insights related to breast cancer drug response using support vector machines.

TDbasedUFE and TDbasedUFEadv: bioconductor packages to perform tensor decomposition based unsupervised feature extraction

The paper introduces two Bioconductor packages for tensor decomposition-based unsupervised feature extraction, showing they outperform existing methods in gene expression analysis and multi-omics integration.

Integrated analysis of gene expression and protein-protein interaction with tensor decomposition

The paper integrates gene expression and protein-protein interaction data using tensor decomposition, improving classification and enrichment across 27 cancer types from TCGA.

Tensor decomposition and principal component analysis-based unsupervised feature extraction outperforms state-of-the-art methods when applied to histone modification profiles

The paper introduces a method combining tensor decomposition and PCA to identify histone modifications from genomic data, outperforming existing methods.

GENEvaRX: A Novel AI-Driven Method and Web Tool Can Identify Critical Genes and Effective Drugs for Lichen Planus

The paper introduces GENEvaRX, a computational framework that identifies critical genes and drugs for lichen planus using gene expression data and enrichment analysis.

Tensor Decomposition Discriminates Tissues Using scATAC-seq

This study uses tensor decomposition to address data sparsity in scATAC-seq and identifies tissue-specific patterns and gene associations.

Principal component analysis- and tensor decomposition-based unsupervised feature extraction to select more reasonable differentially methylated cytosines: Optimization of standard deviation versus state-of-the-art methods

The paper introduces a method combining principal component analysis and tensor decomposition to identify differentially methylated cytosines, outperforming existing methods and showing functional relevance.

A new machine learning based computational framework identifies therapeutic targets and unveils influential genes in pancreatic islet cells

The paper presents a computational framework using machine learning to identify therapeutic targets and influential genes in pancreatic islet cells, finding 44 unique genes and 10 transcription factors related to pancreatic islet functions and diseases.

Tensor decomposition- and principal component analysis-based unsupervised feature extraction to select more reasonable differentially expressed genes: Optimization of standard deviation versus state-of-art methods

The paper optimizes standard deviation in tensor decomposition and PCA to improve differential gene selection, achieving better alignment with the null hypothesis and increased biological reliability.

Projection in genomic analysis: A theoretical basis to rationalize tensor decomposition and principal component analysis as feature selection tools

This paper explains why PCA and tensor decomposition work well for feature selection in genomic data by relating them to projection pursuit and empirical P-value thresholds.

Integrated Analysis of Tissue-specific Gene Expression in Diabetes by Tensor Decomposition Can Identify Possible Associated Diseases.

The paper improves a tensor decomposition method for integrating gene expression profiles from multiple tissues and identifies disease associations beyond individual tissue analyses.

Novel feature selection method via kernel tensor decomposition for improved multi-omics data analysis

This paper introduces an advanced kernel tensor decomposition method for multi-omics data analysis, showing improved performance and feature attribution.

Effects of the collagen-glycosaminoglycan mesh on gene expression as determined by using principal component analysis-based unsupervised feature extraction

The paper uses principal component analysis to identify differentially expressed genes in cells exposed to a collagen-glycosaminoglycan mesh compared to controls, finding enriched biological terms and better performance than linear regression.

Tensor-decomposition--based unsupervised feature extraction in single-cell multiomics data analysis

This study applies tensor-decomposition-based unsupervised feature extraction to integrate single-cell multiomics data without filling missing values, producing biologically relevant embeddings.

Unsupervised tensor decomposition-based method to extract candidate transcription factors as histone modification bookmarks in post-mitotic transcriptional reactivation

Discriminating the Single-cell Gene Regulatory Networks of Human Pancreatic Islets: A Novel Deep Learning Application

This study develops a deep learning application to classify single-cell gene regulatory networks from pancreatic islets, finding that VGG19 performs best in distinguishing healthy from T2D networks.

Mathematical formulation and application of kernel tensor decomposition based unsupervised feature extraction

This paper introduces a kernel-based tensor decomposition method for unsupervised feature extraction and evaluates its performance on synthetic and real data, showing it outperforms or matches traditional tensor decomposition in large p small n scenarios.