Omic data analysis is steadily growing as a driver of basic and applied molecular biology
research. Core to the interpretation of complex and heterogeneous biological phenotypes …

Computational-based analysis of gene expression to evaluate the genetic pattern provides
better breast cancer prediction. It is a challenge to identify these samples correctly and …

Most of the conventional feature selection algorithms have a drawback whereby a weakly
ranked gene that could perform well in terms of classification accuracy with an appropriate …

The recurrent neural network with the long short-term memory cell (LSTM-NN) is employed
to simulate the long-time dynamics of open quantum systems. The bootstrap method is …

For each cancer type, only a few genes are informative. Due to the so-called 'curse of
dimensionality'problem, the gene selection task remains a challenge. To overcome this …

Abstract Computer-Aided Drug Design topic deals with the application of computer
hardware and software to provide solutions at every stage of drug discovery. QSAR methods …

Machine learning (ML) models have become key in decision-making for many disciplines,
including drug discovery and medicinal chemistry. ML models are generally evaluated prior …

In this paper, we propose a framework for privacy-preserving outsourced drug discovery in
the cloud, which we refer to as POD. Specifically, POD is designed to allow the cloud to …

This chapter gives a broad outline of machine learning on Android mobile phones using the
Scikit-learn module. The first section introduces the reader to Python language; next, Python …

This research presents an innovative method for cancer identification and type classification
using microarray data. The method is based on gene selection with shuffling in association …