In predictive model development, gene expression data is associated with the unique
challenge that the number of samples (n) is much smaller than the amount of features (p) …

Gene expression profiles could be generated in large quantities by utilizing microarray
techniques. Currently, the task of diagnosing diseases relies on gene expression data. One …

Creating a system of complex disease diagnosis based on gene expression data using
modern data mining and machine learning techniques is one of the topical areas of recent …

In recent years, a deep learning model called convolutional neural network with an ability of
extracting features of high-level abstraction from minimum preprocessing data has been …

The application of genomics in identifying the nature and cause of diseases has
predominantly increased in this decade. This field of study in life sciences combined with …

Recently, deep learning has opened a remarkable research direction in the track of
bioinformatics, especially for the applications that need classification and regression. With …

This book focuses on an eminent technology called next generation sequencing (NGS)
which has entirely changed the procedure of examining organisms and will have a great …

Gene expression analysis is the study of the way genes are transcribed to synthesize
functional gene products, functional RNA species, or protein products. Its study can provide …

Feature extraction is essential in bioinformatics because it transforms genome sequences
into the feature vectors required for data mining activities such as classification and …

A downside of next-generation sequencing technology is the high technical error rate. We
built a tool, which uses array-based genotype information to classify next-generation …