Gene expression and cancer classification

Multi-cancer classification; an analysis of neural network complexity

This study compares neural network architectures for multi-cancer classification using miRNA, protein, and mRNA data, finding that shallow networks outperform deeper models.

oCEM: Automatic detection and analysis of overlapping co-expressed gene modules

oCEM detects and analyzes overlapping co-expressed gene modules using decomposition methods and a permutation-based approach to select principal components, outperforming existing techniques.

PanClassif: Improving pan cancer classification of single cell RNA-seq using machine learning

PanClassif improves cancer classification from single-cell RNA-seq using machine learning by selecting effective genes and applying smoothing and oversampling techniques, achieving better performance than existing methods.

reComBat: Batch effect removal in large-scale, multi-source omics data integration

reComBat removes batch effects from large-scale, multi-source omics data while preserving biological variation, outperforming existing methods for public gene expression data of Pseudomonas aeruginosa.

Explainable autoencoder-based representation learning for gene expression data

The paper introduces XAE4Exp, an explainable autoencoder for gene expression data, identifying non-differentially expressed genes and cancer-related pathways using TCGA data.

MFmap: A semi-supervised generative model matching cell lines to tumours and cancer subtypes

MFmap predicts cancer subtypes and similarity between cell lines and tumours using a semi-supervised generative model with high accuracy.

Restricted maximum-likelihood method for learning latent variance components in gene expression data with known and unknown confounders

This paper introduces a restricted maximum-likelihood method to estimate latent variance components in gene expression data, improving efficiency and accuracy over gradient-based approaches.

Optimal dimensionality selection for independent component analysis of transcriptomic data

This paper introduces OptICA, a method for selecting optimal dimensionality in ICA of transcriptomic data, showing it outperforms existing methods across multiple datasets.

Automated identification of cell-type-specific genes and alternative promoters

This paper introduces CLA, a method to identify cell-type-specific genes and alternative promoters by integrating Cell Ontology and random forest classification with stratified bootstrapping.

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.

Discriminative feature of cells characterizes cell populations of interest by a small subset of genes

The paper introduces DFC, a method using adaptive LASSO logistic regression to identify a small subset of genes that distinguish cell populations, addressing issues with P-value inflation in single-cell transcriptomics.

A Practical Guide to Sparse k-means Clustering for Studying Molecular Development of the Human Brain

The paper applies sparse k-means clustering to human brain development data to identify age-related clusters and reveal lifespan-stage changes in the visual cortex.

GEDI: an R package for integration of transcriptomic data from multiple high-throughput platforms

GEDI integrates transcriptomic data from microarray and NGS platforms by removing batch effects and re-annotating data, verified through PCA and logistic regression.

Dimensionality reduction by sparse orthogonal projection with applications to miRNA expression analysis and cancer prediction

The paper introduces a sparse orthogonal projection method for dimensionality reduction, applied to miRNA expression analysis and cancer prediction, showing competitive classification performance.

Overcoming the impacts of two-step batch effect correction on gene expression estimation and inference

The paper evaluates the impact of two-step batch correction on gene expression analysis and proposes strategies to mitigate its effects on statistical inference.

Functional Effects of Four or Fewer Critical Genes Linked to Lung Cancers and New Subtypes Detected by A New Machine Learning Classifier

The paper identifies four critical genes with high accuracy in lung cancer subtypes and functional effects, showing robustness across different cohorts and revealing new biological mechanisms.

GEOexplorer: an R/Bioconductor package for gene expression analysis and visualisation

GEOexplorer is an R/Bioconductor package that enables interactive and reproducible microarray gene expression analysis with visualisation tools.

A causality-inspired feature selection method for cancer imbalanced high-dimensional data

This paper introduces a causality-inspired feature selection method, CLAND, to improve accuracy and stability in cancer biomarker discovery from imbalanced omics data.

Predicting chemotherapy response using a variational autoencoder approach

This study uses a variational autoencoder to encode tumor transcriptomes and combines it with XGBoost to predict chemotherapy response across five cancer types, achieving better performance than PCA or raw data in four types.

SGI: Automatic clinical subgroup identification in omics datasets

SGI identifies clinical subgroups in omics datasets using hierarchical clustering and association testing, demonstrated on a metabolomics dataset from a type 2 diabetes study.

Hobotnica: exploring molecular signature quality

Hobotnica estimates molecular signature quality by assessing their ability to stratify data and assign significance scores for comparing different groups.

Stratified neural networks in a time-to-event setting

This paper demonstrates that using stratified loss functions in deep learning improves survival prediction in multi-dataset settings by accounting for varying baseline hazards.

High Dimensionality Reduction by Matrix Factorization for Systems Pharmacology

The paper applies matrix factorization methods to reduce high-dimensional multi-omic data in systems pharmacology, identifying predictive gene signatures for drug response.

Effective expression analysis using gene interaction matrices and convolutional neural networks

This paper introduces GIM-CNNs, a framework using gene interaction matrices and convolutional neural networks to detect biological entities, outperforming standard methods in identifying disease-relevant factors beyond individual genes.

Comparison of Approaches to Transcriptomic Analysis in Multi-Sampled Tumors

This study compares five methods for analyzing gene expression in multi-sampled prostate tumors and benchmarks linear mixed models against generalized linear models for differential expression analysis.

Integrating ensemble systems biology feature selection and bimodal deep neural network for breast cancer prognosis prediction

This study combines ensemble systems biology feature selection with a bimodal deep neural network to improve breast cancer prognosis prediction, achieving high AUC and validated survival differentiation.

Modeling transcriptional profiles of gene perturbation with deep neural network

The paper uses deep neural networks to predict gene perturbation from transcriptional profiles, achieving 74.9% accuracy but with limited generalizability due to data consistency issues.

Optimal balancing of clinical factors in large scale clinical RNA-Seq studies

This paper introduces BalanceIT, a method using genetic algorithms to balance clinical factors in RNA-Seq studies, improving differential gene expression and pathway analysis in autism research.

BlackSheep: A Bioconductor and Bioconda package for differential extreme value analysis

BlackSheep identifies molecules aberrantly enriched in small sample cohorts using non-parametric differential extreme value analysis.

WMW-A: Rank-based two-sample independent test for small sample sizes through an auxiliary sample

WMW-A improves test power for small sample two-sample tests by using an auxiliary sample to construct a three-sample rank-based statistic.