Neural Network Intelligence In Medical Application Gene Prediction
- New Delhi DPS 2017
- 320p. 22cm.
CONTENTS Introduction Rioinformatie's Rielogical Rackground DNA Concept of Gene Computer Science and Gene Soft Computing Methods Soft Computing Techniques for Gene Prediction Soft Computing in Cancer Biology Microarray Technology Principal Component Analysis Neural Network Neural Network Architecture Objectives of the Research Work Outline of the Report LITERATURE SURVEY Introduction Review on Classification of Gene Prediction Techniques SVM Classifier for miRNA Gene Prediction SVM based Bayesian protein-protein interaction Computational method for prediction of genomes Hidden Markov Model (HMM) used in DNA sequence Web Server programs on prediction of genes Computational Methodologies Gene Prediction algorithm based on Machine Learning Techniques Gene Prediction using Digital Signal Processing Phenotypic Gene Prediction Experimental Technique for Gene Prediction ab initio model for Gene Prediction ORS Neural Network Other Gene Prediction Methodology Supplementary Research Conducted Support Vector Mochote Gene Ontology Homology Hidden Markov Model (IMM) Different Software programs for Gene Prediction Other Training Methodologies Other Machine Learning Techniques Digital Signal Processing Other techniques Conclusion ANN GENE CLASSIFIER: PPCA-EP TECHNIQUE Introduction Background Information Cancer Classification and the Challenges The Challenges Public Repository of Gene Expression Data ANN and Gene Classification Classification Technique for Microarray Gene Expression Data Dimensionality Reduction using PPCA Enhancement of Feed Forward ANNs Gene Expession Data Classification Classification of Microarray Gene Expression Data using Enhanced Classifier Training Phase: Minimization of Error by BF algorithm Testing Phase: Classification of Microarray Gem Sequence Conclusion 4. DOMINANT GENE PREDICTOR: GA-ANN TECHNIQUE Introduction Genetic Algorithm Dominant gene prediction using Genetic algorithm Preprocess for dominant gene prediction Generation of Training Data Selection of Optimal Solution Dimensionality reduction by PPCA Draming phase Training through Feed Forward ANN Mromization of Eve by BP algorithm Tin Phave Geners Algorithm Based domemsant gew prediction of AML ALL Clevation of Promotomes Crecemeer and Morton Conclusion CANCER PREDICTION: ANN CLASSIFIER-DOMINANT GENE PREDICTOR Introduction Dominant Gene prediction using PPCA and GA-ANN Classifier Preprocess for dominant gene prediction Training through Feed Forward ANN Minimization of Error by BP algorithm Genetic Algorithm based dominant gene prediction of cancer diagnosis Conclusion RESULTS AND DISCUSSION Introduction Results of the proposed classification technique on ALL/AML dataset Results of the proposed gene prediction technique on ALL/AML dataset Cancer Prediction in CNS tumor, Colon tumor, Lung tumor and Diffuse Large B-Cell datasets Lymphoma Discussion and Conclusion CONCLUSION Introduction Shortcomings of Existing Methods Advantages of New Method Future Research Direction and Suggestions