Gene expression and cancer classification

Linear regression of sampling distributions of the mean

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.

Building flexible and robust analysis frameworks for molecular subtyping of cancers

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.

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.

Estimating cell-type-specific gene co-expression networks from bulk gene expression data with an application to Alzheimer's disease

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.

Evaluation of noninvasive biospecimens for transcriptome studies

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

Toward Best Practice in Identifying Subtle Differential Expression with RNA-seq: A Real-World Multi-Center Benchmarking Study Using Quartet and MAQC Reference Materials

This study benchmarks RNA-seq methods across 45 labs to identify sources of variation in detecting subtle differential expression, highlighting experimental and bioinformatics factors affecting reliability.

pyComBat, a Python tool for batch effects correction in high-throughput molecular data using empirical Bayes methods

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.

Predicting Phenotypic Traits Using a Massive RNA-seq Dataset

The paper uses a large RNA-seq dataset to determine the sample size and normalization needed for accurate phenotypic trait prediction.

BioDiscViz : a visualization support and consensus signature selector for BioDiscML results

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

High Frequency Longitudinal RNAseq Reveals Temporally Varying Genes and Recovery Trajectories in Rats

This study uses longitudinal RNAseq in rats to identify temporally variable genes and recovery trajectories after chemical exposure.

N-of-one differential gene expression without control samples using a deep generative model

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.

A Model-Based Clustering via Mixture of Bayesian Hierarchical Models with Covariate Adjustment for Detecting Differentially Expressed Genes from Paired Design

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.

Application of Attention and Graph Transformer-Based Approaches for RNA Biomarker Discovery in Metabolically-Associated Fatty Liver Disease (MAFL/NASH)

This study uses attention and graph transformer models to identify RNA biomarkers for NAFLD/NASH, achieving higher accuracy than baseline methods and validating top candidates in lab experiments.

A highly accurate model for screening prostate cancer using propensity index panel of ten genes

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: a web application for comprehensive biomarker exploration on large-scale data in solid tumors

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.

A Simple Bias Reduction Algorithm for RNA Sequencing Datasets

This paper introduces Local Leveling, a method to reduce bias in RNA-seq data, improving gene-gene relationship accuracy and statistical test results.

Replicability of bulk RNA-Seq differential expression and enrichment analysis results in cancer research

This study evaluates the replicability of RNA-Seq differential expression and enrichment analysis in cancer research, finding that underpowered experiments produce inflated effect sizes and limited replicability.

Batch Normalization Followed by Merging Is Powerful for Phenotype Prediction Integrating Multiple Heterogeneous Studies

This paper evaluates methods for integrating heterogeneous genomic studies to improve phenotype prediction, finding that batch normalization with merging or integration strategies enhances classifier performance.

A Non-invasive Detection of Parkinson's Disease using PitArray: An Integrative Meta-Analysis and Machine Learning Approach

This study identifies potential biomarkers for Parkinson's disease using blood-based gene expression data and develops a machine learning classifier called PitArray for non-invasive detection.

Benchmarking Variational AutoEncoders on cancer transcriptomics data

This study benchmarks variational autoencoders on cancer transcriptomics data to evaluate hyperparameter effects and latent space biological relevance.

XA4C: eXplainable representation learning via Autoencoders revealing Critical genes

XA4C identifies Critical genes using explainable autoencoders, revealing novel biological insights in cancer gene expression data.

Adjustment of spurious correlations in co-expression measurements from RNA-Sequencing data

The paper introduces SNAIL, a normalization method that reduces false-positive co-expression associations in RNA-sequencing data, improving downstream network analysis.

clrDV: A differential variability test for RNA-Seqdata based on the skew-normal distribution

clrDV detects genes with differential variability between two populations using a skew-normal distribution, showing competitive performance and faster runtime than existing methods.

TransGeneSelector: A Transformer-based Approach Tailored for Key Gene Mining with Small Plant Transcriptomic Datasets

TransGeneSelector uses a Transformer-based classifier to identify key genes in small plant transcriptomic datasets, outperforming traditional methods in classification and gene discovery.

Annotated Compendium of 102 Breast Cancer Gene-Expression Datasets

The paper curates 102 breast cancer gene-expression datasets and provides a computational pipeline for data processing and metadata annotation to facilitate reuse and comparative analysis.

Exploring tumor-normal cross-talk with TranNet: role of the environment in tumor progression

TranNet identifies predictors of tumor progression by analyzing gene expression differences between tumor and adjacent normal tissues using TCGA data.

Correcting batch effects in large-scale multiomic studies using a reference-material-based ratio method

The paper evaluates seven batch-effect correction algorithms for multiomic data and finds the ratio-based method is more effective, especially when batch effects are confounded with biological factors.

Quantum machine learning for untangling the real-world problem of cancers classification based on gene expressions

This study uses quantum machine learning to classify 11 cancers from 4495 gene expression samples and identifies biomarkers linked to DNA promoter methylation and patient survival.

Evaluating cancer cell line and patient-derived xenograft recapitulation of tumor and non-diseasedtissue gene expression profiles in silico

The paper evaluates how well cancer cell lines and PDX models recapitulate human tumor and non-diseased tissue gene expression profiles using in silico analysis.

dRFEtools: Dynamic recursive feature elimination for omics

dRFEtools implements dynamic recursive feature elimination to improve prediction accuracy and reduce computational time in omics data analysis.