Performance Analysis of Deep Learning Models for Binary Classification of Cancer Gene Expression Data.

The classification of patients as cancer and normal patients by applying the computational methods on their gene expression profiles is an extremely important task. Recently, deep learning models, mainly multilayer perceptron and convolutional neural networks, have gained popularity for being applied on this type of datasets. This paper aims to analyze the performance of deep learning models on different types of cancer gene expression datasets as no such consolidated work is available.

Fuzzy and Rough Set Theory Based Computational Framework for Mining Genetic Interaction Triplets From Gene Expression Profiles for Lung Adenocarcinoma.

Genetic interactions are very helpful in understanding different disease and discovering drugs for it. Compared to the gene pairs that represent the genetic interactions between two genes, the gene triplets are more informative and useful. However, existing works on genetic interactions among gene triplets have primarily focused on detecting gene triplets from time series gene expression profiles.