Gene expression and cancer classification

The paper uses graph convolutional neural networks with layer-wise relevance propagation to analyze gene expression data and identify genes and pathways associated with BRCAness, outperforming traditional methods.

BulkRNABert uses a pre-trained transformer model on bulk RNA-seq data to improve cancer classification and survival prediction across multiple cancer types.

This paper develops a deep learning model using single-cell transcriptomics to classify HNSCC and distinguish HPV+ from HPV- patients, achieving high AUROC scores on a validation dataset.

The paper introduces a gene expression stability metric, the gene homeostasis Z-index, to identify genes under active regulation in different organs and highlights its advantage over traditional mean-based methods.

This study integrates multi-omics data using autoencoders and tensor analysis to identify cancer risk groups, showing significant stratification with p-value < 0.05.

Phantasus is a web-application that provides interactive gene expression analysis and access to over 84000 public datasets, enabling users to perform analysis from data loading to differential expression.

This study introduces NBSR, a method for differential expression analysis of miRNA-seq data that outperforms existing approaches on simulated and experimental datasets.

The paper combines LASSO regression with a deep neural network to improve lung cancer diagnosis from transcriptomic data, achieving high accuracy and specificity.

NPmatch corrects latent batch effects in omics data using nearest-pair matching, outperforming existing methods across diverse datasets.

PerSEveML identifies persistent biomarker structures for rare events using an integrative machine learning approach and provides visualization tools for hypothesis generation.

SIEVEseq provides a unified statistical framework for analyzing RNA-Seq data to detect differential expression, variability, and skewness, showing improved performance over existing methods.

This paper introduces Robseq, a statistical model for RNA-Seq differential expression that addresses FDR inflation caused by group heteroscedasticity, showing improved control of false discovery and type-I error rates.

This paper improves RNA-seq normalization by adapting the TMM method using Jaeckels Estimator, showing better performance in differential expression analysis.

The paper predicts missing toxicogenomics data in the DrugMatrix database using ToxiCompl, a hybrid method combining matrix factorization and Bayesian optimization, showing alignment with biological expectations.

This paper reviews consensus clustering methods, their stability scores, and applications to gene expression data, providing open-source R implementations.

This paper benchmarks feature selection and extraction methods to enhance machine-learning performance in patient classification using metabolomics data.

GeneSurv enables multi-gene cancer survival analysis by correlating gene expression with patient outcomes, demonstrating its utility in identifying prognostic gene associations in breast cancer.

The paper introduces DeepProfile, a deep learning framework for analyzing gene expression in 18 cancers, revealing biological insights through latent space analysis and linking gene expression to clinical outcomes.

The paper evaluates RNA-Seq analysis methods to identify key components of the estrogen receptor-alpha signaling pathway in breast cancer, finding that SAM-Seq uncovers more significant genes than DESeq2.

INSIDER is a statistical framework for analyzing bulk RNA expression data that models interactions between biological variables and decomposes variation into a shared latent space to improve interpretation and downstream analysis.

The paper evaluates the stability of feature selection using GCNN+LRP and GCNN+SHAP for gene expression data, comparing them to classical ML methods.

BatchEval Pipeline evaluates batch effects in multi-dataset transcriptomics analysis and provides a report to guide batch correction.

The paper evaluates differential expression methods in bulk RNA-Seq by comparing edgeR-robust and DESeq2 in cell line and cancer data, highlighting the need for method testing in genomics projects.

DeepAdapter is a deep neural network that removes undesirable variations from transcriptomic data, improving accuracy across different batches, platforms, and sample types.

GeneSelectR is an R package that integrates machine learning and bioinformatics for feature selection in RNAseq data, prioritizing biologically relevant features through ML performance and GO analysis.

BAMBI identifies coding and non-coding RNA genes as biomarkers for disease diagnosis and prognosis with high accuracy and biological interpretability.

PCTA provides a curated pan-cancer cell line transcriptome atlas with user-friendly tools for gene expression analysis across diverse cancer types.

PCA-Plus enhances principal component analysis to better detect, visualize, and quantify batch effects and group differences in multi-omic data.

Omada automates transcriptomic clustering by integrating machine learning, improving accessibility and robustness across diverse RNA expression datasets.

MetaNorm improves normalization of NanoString nCounter data by incorporating meta-analytic priors, enhancing stability and computational efficiency.