Feature Signature Discovery for Autism Detection: An Automated Machine Learning Based Feature Ranking Framework.

Autism spectrum disorder is the most used umbrella term for a myriad of neuro-degenerative/developmental conditions typified by inappropriate social behavior, lack of communication/comprehension skills, and restricted mental and emotional maturity. The intriguing factor of this disorder is attributed to the fact that it can be detected only by close monitoring of developmental milestones after childbirth. Moreover, the exact causes for the occurrence of this neurodevelopmental condition are still unknown.

Improving classification accuracy of cancer types using parallel hybrid feature selection on microarray gene expression data.

Background: Data mining techniques are used to mine unknown knowledge from huge data. Microarray gene expression (MGE) data plays a major role in predicting type of cancer. But as MGE data is huge in volume, applying traditional data mining approaches is time consuming.

Prediction of cancer rescue p53 mutants in silico using Naïve Bayes learning methodology.

This research is focussed on predicting through Naïve Bayes learning, the possible p53 rescue mutants from amino-acid substitutions at the second, third and fourth site recombination that could reinstate normal p53 activity. The Naïve Bayes probability values of the amino-acid substitutions at the respective site-wise recombination were utilized to formulate the proposed Genetic Mutant Marker Extraction (GMME) technique that could unearth the hot spot cancer, strong rescue and weak rescue mutants. The p53 mutation records depicting the amino-acid substitutions obtained by yeast assays comprising of nearly 16,700 records, available at the University of California, Machine Learning Repository, were utilized as the training dataset for the GMME technique.

Improved classification of lung cancer tumors based on structural and physicochemical properties of proteins using data mining models.

Detecting divergence between oncogenic tumors plays a pivotal role in cancer diagnosis and therapy. This research work was focused on designing a computational strategy to predict the class of lung cancer tumors from the structural and physicochemical properties (1497 attributes) of protein sequences obtained from genes defined by microarray analysis. The proposed methodology involved the use of hybrid feature selection techniques (gain ratio and correlation based subset evaluators with Incremental Feature Selection) followed by Bayesian Network prediction to discriminate lung cancer tumors as Small Cell Lung Cancer (SCLC), Non-Small Cell Lung Cancer (NSCLC) and the COMMON classes.

Prediction of P53 mutants (multiple sites) transcriptional activity based on structural (2D&3D) properties.

Prediction of secondary site mutations that reinstate mutated p53 to normalcy has been the focus of intense research in the recent past owing to the fact that p53 mutants have been implicated in more than half of all human cancers and restoration of p53 causes tumor regression. However laboratory investigations are more often laborious and resource intensive but computational techniques could well surmount these drawbacks. In view of this, we formulated a novel approach utilizing computational techniques to predict the transcriptional activity of multiple site (one-site to five-site) p53 mutants.