Browsing by Author "Adebiyi, Ayodele A."

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    An ICA-ensemble learning approaches for prediction of RNAseq malaria vector gene expression data classification
    (International Journal of Electrical and Computer Engineering (IJECE), 2020-09-23) Arowolo, Micheal Olaolu; Adebiyi, Marion O.; Adebiyi, Ayodele A.
    Malaria parasites introduce outstanding life-phase variations as they grow across multiple atmospheres of the mosquito vector. There are transcriptomes of several thousand different parasites. Ribonucleic acid sequencing (RNAseq) is a prevalent gene expression tool leading to better understanding of genetic interrogations. RNA-seq measures transcriptions of expressions of genes. Data from RNA-seq necessitate procedural enhancements in machine learning techniques. Researchers have suggested various approached learning for the study of biological data. This study works on ICA feature extraction algorithm to realize dormant components from a huge dimensional RNA-seq vector dataset, and estimates its classification performance, Ensemble classification algorithm is used in carrying out the experiment. This study is tested on RNA-seq mosquito anopheles gambiae dataset. The results of the experiment obtained an output metrics with a 93.3% classification accuracy.
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    Predicting RNA-Seq data using genetic algorithm and ensemble classification algorithms
    (Indonesian Journal of Electrical Engineering and Computer Science, 2020-05-03) Arowolo, Micheal Olaolu; Adebiyi, Marion O.; Adebiyi, Ayodele A.
    Malaria parasites accept uncertain, inconsistent life span breeding through vectors of mosquitoes stratospheres. Thousands of different transcriptome parasites exist. A prevalent Ribonucleic acid sequencing (RNA-seq) technique for gene expression has brought about enhanced identifications of genetical queries. Computation of RNA-seq gene expression data transcripts requires enhancements using analytical machine learning procedures. Numerous learning approaches have been adopted for analyzing and enhancing the performance of biological data and machines. In this study, a Genetic algorithm dimensionality reduction technique is proposed to fetch relevant information from a huge dimensional RNA-seq dataset, and classification uses Ensemble classification algorithms. The experiment is performed using a mosquito Anopheles gambiae dataset with a classification accuracy of 81.7% and 88.3%