Olufemi Aromolaran

TL;DR: This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge of the appropriate clustering technique that will guarantee stability and high degree of accuracy in its analysis procedure.

TL;DR: Application of data clustering was systematically discussed in view of the characteristics of the different clustering techniques that make them better suited or biased when applied to several types of data, such as uncertain data, multimedia data, graph data, biological data, stream data, text data, time series data, categorical data and big data.

TL;DR: This review examines various methods applied to essential gene prediction task, their strengths, limitations and the factors responsible for effective computational prediction of essential genes to present the standard procedure and resources available for predicting essential genes in organisms.

TL;DR: In this paper, the authors employed machine learning to predict essential genes in Drosophila melanogaster using 27,340 features including nucleotide and protein sequences, gene networks, protein-protein interactions, evolutionary conservation and functional annotations.

TL;DR: A novel computational model that makes the prediction of new and validated antimalarial drug target cheaper, easier, and faster has been developed and new essential reactions as potential targets for drugs in the metabolic network of the parasite are identified.