Gene expression and cancer classification

This paper introduces iPCR, a model that predicts patient drug responses by integrating genomic data from cell lines and tumors, showing improved performance over existing methods.

DRAB identifies genes with context-specific genetic regulation by comparing models across tissues using bootstrapping and elastic net.

This study evaluates deep learning-based representation methods for survival and gene essentiality prediction on bulk RNA-seq data, finding that non-DL methods perform comparably on survival tasks while DL methods excel in gene essentiality prediction.

The paper introduces DEHOGT, a method for detecting differentially expressed genes in RNA-seq data that accounts for overdispersion, and shows it outperforms DESeq and EdgeR on synthetic data and detects more DE genes in microglial cells under stress hormone treatments.

The paper analyzes the geometric relationship between normal tissue and cancer gene expression manifolds, showing they are disjoint and that tumors are translations of normal tissues along a cancer progression axis.

The paper introduces Mime, an R package for building and visualizing machine learning models to predict clinical outcomes and select features using transcriptional data.

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.

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.

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

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

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

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.

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

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.

This study evaluates normalization procedures for transcriptome profiles of compounds, finding that batch-corrected baseline distributions work well for large datasets and offering guidance for small datasets.

This study compares the impact of different optimization methods on model performance and sparsity in transcriptomic prediction using LASSO logistic regression.

The paper demonstrates that regression coefficients and R2 from sampling distributions of the mean match those from individual data and shows applications in gene correlation analysis.

The paper provides a framework for molecular cancer subtyping that integrates quality control, normalization, batch correction, classification, and reporting, with a use case in breast cancer.

The paper estimates cell-type-specific gene co-expression networks from bulk gene expression data and identifies novel co-expressions among Alzheimer's disease risk genes.

This study evaluates noninvasive biospecimens for transcriptome studies, finding hair follicles and urine cell pellets to be most promising for disease-relevant applications.

pyComBat provides a Python implementation of ComBat and ComBat-Seq for batch effect correction in microarray and RNA-Seq data, offering similar performance with improved speed and open-source accessibility.

BioDiscViz provides visualization tools and consensus signature selection for BioDiscML results to improve model interpretation and usability.

This paper introduces a deep generative model to perform differential gene expression analysis on single cancer samples without control samples, outperforming DESeq2 in detecting differentially expressed genes.

This paper introduces a Bayesian hierarchical mixture model with covariate adjustment to detect differentially expressed genes in paired design whole-genome data, showing improved performance over existing methods.

The paper develops a logistic regression model with a propensity index panel of ten genes to accurately screen prostate cancer, achieving 96.36% accuracy and 0.99 AUROC.

BEST provides a web tool for comprehensive biomarker analysis in solid tumors using large-scale, multi-cohort data with standardized annotations and diverse analytical functions.