Gene expression and cancer classification

Towards the Genome-scale Discovery of Bivariate Monotonic Classifiers

The paper introduces fastBMC, an algorithm that accelerates the identification of bivariate monotonic classifiers, improving classification performance on clinical datasets.

Robust and Adaptive Non-Parametric Tests for Detecting General Distributional Shifts in Gene Expression

This paper introduces QRscore, a non-parametric test for detecting distributional shifts in gene expression, showing it outperforms existing methods in detecting variance and mean shifts.

EXPRESSION GRAPH NETWORK FRAMEWORK FOR BIOMARKER DISCOVERY

The paper introduces EGNF, a graph-based framework for biomarker discovery that outperforms traditional methods in classification accuracy and interpretability across cancer datasets.

Autoencoders with shared and specific embeddings formulti-omics data integration

This paper introduces an autoencoder with orthogonal constraints for multi-omics data integration, showing improved reconstruction and classification performance on cancer data.

The Limitations of TabPFN for High-Dimensional RNA-seq Analysis

This study evaluates TabPFN's performance on high-dimensional RNA-seq data and finds that adaptations underperform compared to classical methods when using full feature sets.

Predictive Gene Discovery with EPCY: A Density-Based Alternative to DE analysis

EPCY ranks genes by predictive power using cross-validated classifiers and density estimation, outperforming DEG-based methods in selecting robust candidate genes.

Anticlustering for Sample Allocation To Minimize Batch Effects

The paper introduces anticlustering to balance batches and minimize batch effects in high-throughput sequencing, showing improved balance compared to existing methods.

Discovery of Prognostic Biomarkers in Gastric Cancer Through Machine Learning and Bioinformatics Analysis of Gene Expression Data

NanoString Technologies Neuropathology Panel Produces Unreliable Measurements of Mouse Hippocampal Gene Expression

The paper evaluates the reliability of NanoString Neuropathology panel measurements for mouse hippocampal gene expression and finds low reliability.

Implementation and Evaluation of Support Vector Machine-Based Models for Cancer Detection Using Multi-Omic Data: A Systematic Review

This review evaluates SVM-based cancer detection models using multi-omic data, highlighting their potential but noting limitations in generalizability due to lack of external validation.

Explainable AI and Multiclassifiers for Staging Biomarker Discovery in Lung Squamous Cell Carcinoma

This study uses machine learning and explainable AI to identify potential staging biomarkers in lung squamous cell carcinoma using TCGA-LUSC gene expression data.

Multiblock LASSO Framework for Cancer Gene Selection from RNA-Seq PANCAN Data

This study applies Multiblock Lasso to RNA-Seq PANCAN data to select significant genes for cancer classification, demonstrating effective dimensionality reduction and accuracy maintenance.

Integrative Machine Learning Reveals Potential Signature Genes Using Transcriptomics in Colon Cancer

This study uses transcriptomics and machine learning to identify signature genes and pathways in colon cancer, finding high classification accuracy and potential biomarkers.

Assessing Random Forest self-reproducibility for optimal short biomarker signature discovery

The paper introduces AUC hyper-stability scores to improve the selection of Random Forest implementations for stable short biomarker signature discovery.

SurfacOmics: an R shiny application integrating variable Feature Selection for gene biomarker discovery using Elastic-Net Regularization

SurfacOmics is an R shiny application that identifies gene biomarkers using Elastic-Net Regularization and integrates biological knowledge for interpretation.

A genetically informed atlas of transcriptomic programs from massive human RNA-seq compendia

CLAMP integrates genetic associations and drug signatures with large-scale RNA-seq data to identify interpretable gene modules linked to complex diseases.

Characterizing and evaluating cell specialization through Gini index of gene expression: TCGA normal vs tumor case study

This study uses the Gini Index to compare gene expression specialization between normal and tumor cells, finding that tumor cells show reduced specialization.

Deep representation learning for temporal inference in cancer omics: a systematic review

This paper reviews the use of deep representation learning, particularly VAEs, in cancer omics to model temporal processes, highlighting limitations in capturing cancer progression dynamics.

Cancer Diagnostics using Machine Learning of Tumor- and Tissue- Specific RNA Transcripts

This study develops SRT-CD, a machine learning system using tumor- and tissue-specific RNA transcripts to diagnose cancers and determine tissue origin with high accuracy.

RNAcare: Integrating Clinical Data with Transcriptomic Evidence using Rheumatoid Arthritis as a Case Study

RNAcare integrates clinical and transcriptomic data for bulk RNA-Seq analysis, enabling hypothesis generation about gene expression-clinical symptom relationships in rheumatoid arthritis.

CorrAdjust unveils biologically relevant transcriptomic correlations by efficiently eliminating hidden confounders

CorrAdjust improves RNA-RNA correlation analysis by removing hidden confounders, enhancing the detection of biologically relevant interactions in human RNA-seq data.

MettleRNASeq: Complex RNA-Seq Data Analysis and Gene Relationships Exploration Based on Machine Learning

MettleRNASeq improves RNA-Seq analysis by integrating machine learning to identify key genes and relationships in complex datasets, outperforming traditional methods in radiotherapy studies.

RPSLearner: A novel approach based on random projection and deep stacking learning for categorizing NSCLC

RPSLearner improves NSCLC subtype classification using random projection and stacking learning, achieving higher accuracy and AUC than existing methods.

GTEx_Pro: A robust and accurate preprocessing pipeline for GTEx data: TMM+CPM normalization and SVA batch correction for enhanced multi-tissue analysis

GTEx_Pro preprocesses GTEx v8 data using TMM+CPM normalization and SVA batch correction to enhance multi-tissue analysis accuracy and reduce systematic variation.

Identification of the Best Filter-based Feature Selection Techniques for Microarray Datasets

This paper evaluates filter-based feature selection methods on microarray datasets, finding that mRMR outperforms others in classification accuracy, F1-score, and RMSE.

miRglmm: a generalized linear mixed model of isomiR-level counts improves estimation of miRNA-level differential expression and uncovers variable differential expression between isomiRs

sampleclusteR: A lightweight R package for automated clustering of transcriptomics samples using metadata

sampleclusteR automates clustering of transcriptomics samples using metadata from GEO and ArrayExpress, correctly grouping most samples across multiple datasets.

Repurposing without Reinventing the Wheel - Ensemble Models for Differential Analysis

The paper introduces an ensemble framework for differential analysis that combines multiple models to improve performance across various omics data types.

Optimizing miRNA-Based Breast Cancer Subtyping with AHALA: A Multi-Stage Classification Approach

This paper introduces AHALA, a novel optimization method for miRNA-based breast cancer subtyping, achieving high accuracy and identifying key miRNAs associated with subtypes.

Exploring the latent space of transcriptomic data withtopic modeling

This paper compares topic modeling methods on transcriptomic data to identify tissue structure and distinguish healthy from cancerous tissues, showing their effectiveness as embedding spaces for classification.