Gene expression and cancer classification

TNMplot.com compares gene expression across normal, tumor, and metastatic tissues using transcriptome data to identify differentially expressed genes and validate their significance in cancer.

Super-delta2 is a differential expression analysis method for RNA-seq data that controls type I error and achieves high statistical power in multi-group comparisons.

The paper introduces FSCseq, a model-based clustering method for RNA-seq data that selects informative genes, accounts for normalization factors, and identifies cancer subtypes.

FrozenChicken provides frozen vectors for chicken microarray data to facilitate meta-analysis in vertebrate development studies.

sweetD visualizes dependence between gene abundance and log fold difference in large expression datasets using Hoeffding's D statistic.

This paper shows that gene expression data can be better understood using hyperbolic geometry instead of Euclidean geometry, improving visualization and analysis.

BiSEK improves differential expression analysis by identifying affected pathways without relying on individual gene significance, showing results relevant to FUBP1 depletion and SARS-CoV-2 infection.

The paper introduces iDEA, a method to improve differential expression analysis in cancer, revealing mechanisms of resistance to cell cycle inhibitors in retinoblastoma.

CuBlock normalizes gene-expression microarray data across platforms, effectively separating biological groups, outperforming existing methods in this task.

The paper introduces rePROBE, an R package that updates probe assignments and annotations for microarrays using current genomic and transcriptomic data, improving accuracy for multiple species.

The paper introduces AWST, a data transformation method that improves clustering accuracy in RNA-seq data by reducing the need for gene selection.

GEOlimma improves differential expression analysis by integrating pre-existing microarray data, enhancing power and classification performance compared to Limma.

The paper develops a regularized functional regression model to identify dosage-dependent gene expression associations with cancer drug response using in vitro data.

This study introduces gGAN, an adversarial simulator of gene expression data that outperforms existing simulators in realism and passes a Turing test for gene expression data.

The paper improves gene expression inference by replacing activation functions in neural networks, achieving lower mean absolute error.

This paper introduces OVE-FC and OVE-sFC tests to detect subtype-specific and differentially expressed genes, showing improved accuracy in deconvolution compared to existing methods.

COT detects marker genes across subtypes using cosine similarity in a one-sample test, applicable to multi-omics data.

HB-PLS identifies pathway regulators by integrating Huber loss and Berhu penalty with PLS, outperforming existing methods in efficiency and distinct regulator identification.

This paper uses deep learning to classify 12 cancer types and identify biomarkers from RNA-SEQ data, achieving 94-95% accuracy and uncovering common cancer-related pathways and genes.

The paper introduces a method to detect bimodal gene expression using Gaussian Mixture Models and identifies 554 bimodal genes across 25 tumor types, with 96 linked to prognosis differences.

BatchServer provides a web platform for evaluating, visualizing, and correcting batch effects in omics data using autoComBat, PVCA, and UMAP.

RSGSA is a supervised gene selection algorithm that improves stability and classification accuracy in gene expression datasets with phenotypes.

The paper constructs low-dimensional latent feature spaces of the human transcriptome using dimensionality reduction techniques and evaluates their ability to reconstruct data and improve phenotypic predictions.

This paper proposes a meta-learning framework for genomic survival analysis, showing it outperforms transfer learning with fewer samples and identifies important genes and pathways in cancer.

DESMOND identifies differentially expressed gene modules in heterogeneous diseases by integrating gene expression and protein interaction data, improving reproducibility and biological relevance.

This study shows that gene expression distributions in cancer data are often non-Normal, and using this information improves survival classification in cancer patients.

This paper introduces HC-fused, a hierarchical clustering and data fusion method for disease subtype discovery, showing superior performance on cancer data from TCGA.

This paper introduces a deep-learning based domain adaptation algorithm for RNA-seq metadata annotation, achieving higher accuracy than existing methods for tissue, sample source, and patient sex prediction.

Reboot identifies genes and splicing isoforms linked to cancer patient prognosis using penalized Cox regression and bootstrap methods, validated on glioblastoma and pancreatic adenocarcinoma datasets.

dearseq is a differential expression analysis method that controls the false discovery rate without assuming data distribution, showing improved performance over existing methods in simulations and real data.