In silico ranking of phenolics for therapeutic effectiveness on cancer stem cells.

Background: Cancer stem cells (CSCs) have features such as the ability to self-renew, differentiate into defined progenies and initiate the tumor growth. Treatments of cancer include drugs, chemotherapy and radiotherapy or a combination. However, treatment of cancer by various therapeutic strategies often fail.

Identifying Non-Redundant Gene Markers from Microarray Data: A Multiobjective Variable Length PSO-Based Approach.

Identifying relevant genes which are responsible for various types of cancer is an important problem. In this context, important genes refer to the marker genes which change their expression level in correlation with the risk or progression of a disease, or with the susceptibility of the disease to a given treatment. Gene expression profiling by microarray technology has been successfully applied to classification and diagnostic prediction of cancers.

A PSO-Based Approach for Pathway Marker Identification From Gene Expression Data.

In this article, a new and robust pathway activity inference scheme is proposed from gene expression data using Particle Swarm Optimization (PSO). From microarray gene expression data, the corresponding pathway information of the genes are collected from a public database. For identifying the pathway markers, the expression values of each pathway consisting of genes, termed as pathway activity, are summarized.

Prediction of protein subcellular localization by incorporating multiobjective PSO-based feature subset selection into the general form of Chou's PseAAC.

In this article, the possible subcellular location of a protein is predicted using multiobjective particle swarm optimization-based feature selection technique. In general form of pseudo-amino acid composition, the protein sequences are used for constructing protein features. Here, the different amino acids compositions are used to construct the feature sets.

A graph-theoretic approach for identifying non-redundant and relevant gene markers from microarray data using multiobjective binary PSO.

The purpose of feature selection is to identify the relevant and non-redundant features from a dataset. In this article, the feature selection problem is organized as a graph-theoretic problem where a feature-dissimilarity graph is shaped from the data matrix. The nodes represent features and the edges represent their dissimilarity.