An ICA-ensemble learning approaches for prediction of RNA-seq malaria vector gene expression data classification

Malaria parasites introduce outstanding life-phase variations as they grow across multiple atmospheres of the mosquito vector. There are transcriptomes several thousand different parasites. (RNA-seq) Ribonucleic acid sequencing is a prevalent gene expression tool leading to better understanding genetic interrogations. RNA-seq measures transcriptions expressions genes. Data from necessitate procedural enhancements in machine learning techniques. Researchers have suggested various approached for study biological data. This works on ICA feature extraction algorithm realize dormant components huge dimensional vector dataset, and estimates its classification performance, Ensemble used carrying out experiment. tested RNA-Seq anopheles gambiae dataset. The results experiment obtained an output metrics with 93.3% accuracy.