DEGnext: classification of differentially expressed genes from RNA-seq data using a convolutional neural network with transfer learning
Background A limitation of traditional differential expression analysis on small datasets
involves the possibility of false positives and false negatives due to sample variation …
involves the possibility of false positives and false negatives due to sample variation …
GeneSelectML: a comprehensive way of gene selection for RNA-Seq data via machine learning algorithms
Selection of differentially expressed genes (DEGs) is a vital process to discover the causes
of diseases. It has been shown that modelling of genomics data by considering relation …
of diseases. It has been shown that modelling of genomics data by considering relation …
Feature engineering from meta-data for prediction of differentially expressed genes: An investigation of Mus musculus exposed to space-conditions
Transcription profiling is a key process that can reveal those biological mechanisms driving
the response to various exposure conditions or gene perturbations. In this work, we …
the response to various exposure conditions or gene perturbations. In this work, we …
RNA Sequences-Based Diagnosis of Parkinson's Disease Using Various Feature Selection Methods and Machine Learning
Parkinson's disease is a neurodegenerative disease that is associated with genetic and
environmental factors. However, the genes causing this degeneration have not been …
environmental factors. However, the genes causing this degeneration have not been …