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 …

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 …

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 …

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 …

In big data, clustering is the process through which analysis is performed. Since the data is
big, it is very difficult to perform clustering approach. Big data is mainly termed as petabytes …

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 …