EVOLVING EFFICIENT CLUSTERING AND CLASSIFICATION PATTERNS IN LYMPHOGRAPHY DATA THROUGH DATA MINING TECHNIQUES

ABSTRACT Data mining refers to the process of retrieving knowledge by discovering novel and relative patterns from large datasets. Clustering and Classification are two distinct phases in data minin

Improved Classification of Lung Cancer Tumors Based on Structural and Physicochemical Properties of Proteins Using Data Mining Models

<div><p>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. Moreover, this methodology eliminated the need for extensive data cleansing strategies on the protein properties and revealed the optimal and minimal set of features that contributed to lung cancer tumor classification with an improved accuracy compared to previous work. We also attempted to predict via supervised clustering the possible clusters in the lung tumor data. Our results revealed that supervised clustering algorithms exhibited poor performance in differentiating the lung tumor classes. Hybrid feature selection identified the distribution of solvent accessibility, polarizability and hydrophobicity as the highest ranked features with Incremental feature selection and Bayesian Network prediction generating the optimal Jack-knife cross validation accuracy of 87.6%. Precise categorization of oncogenic genes causing SCLC and NSCLC based on the structural and physicochemical properties of their protein sequences is expected to unravel the functionality of proteins that are essential in maintaining the genomic integrity of a cell and also act as an informative source for drug design, targeting essential protein properties and their composition that are found to exist in lung cancer tumors.</p> </div

Optimal Performance of Novel Predictor Methods on Site-Wise P53 Mutants Transcriptional Activity.

<p>Optimal Performance of Novel Predictor Methods on Site-Wise P53 Mutants Transcriptional Activity.</p

Novel Computational Approach to Predict Site-Specific P53 Mutant Transcriptional Activity.

<p>Novel Computational Approach to Predict Site-Specific P53 Mutant Transcriptional Activity.</p

Comparison of predictor models in lung cancer tumor categorization.

<p>Comparison of predictor models in lung cancer tumor categorization.</p

Performance Comparison of Site-3 P53 Mutants Transcriptional Activity.

<p>Performance Comparison of Site-3 P53 Mutants Transcriptional Activity.</p

Prediction of P53 Mutants (Multiple Sites) Transcriptional Activity Based on Structural (2D&3D) Properties

<div><p>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. The optimal MCC obtained by the proposed approach on prediction of one-site, two-site, three-site, four-site and five-site mutants were 0.775,0.341,0.784,0.916 and 0.655 respectively, the highest reported thus far in literature. We have also demonstrated that 2D and 3D features generate higher prediction accuracy of p53 activity and our findings revealed the optimal results for prediction of p53 status, reported till date. We believe detection of the secondary site mutations that suppress tumor growth may facilitate better understanding of the relationship between p53 structure and function and further knowledge on the molecular mechanisms and biological activity of p53, a targeted source for cancer therapy. We expect that our prediction methods and reported results may provide useful insights on p53 functional mechanisms and generate more avenues for utilizing computational techniques in biological data analysis.</p> </div

Site-Specific P53 Mutant Records.

<p>Site-Specific P53 Mutant Records.</p

The IFS curves depicting classification accuracy and MCC in lung tumor categorization.

<p>(A) The IFS curve generated using Classification Accuracy in Lung Tumor categorization. The x-axis represented the number of features while the y-axis represented the jack-knife cross-validation accuracy. The peak of classification accuracy attained was 87.6% with 36 features. The top 36 features derived by Hybrid Feature Selection (Gain Ratio +CFS Subset) approach form the optimal feature set. (B) The IFS curve generated using MCC values obtained from classification algorithms. The peak of MCC is 0.812 with 36 features. The top 36 features derived by the Hybrid Feature Selection approach (Gain Ratio + CFS Subset) formed the optimal feature set.</p