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Multiobjective optimization algorithms for bioinformatics [electronic resource] / Anirban Mukhopadhyay, Sumanta Ray, Ujjwal Maulik, Sanghamitra Bandyopadhyay.

This book provides an updated and in-depth introduction to the application of multiobjective optimization techniques in bioinformatics. In particular, it presents multiobjective solutions to a range of complex real-world bioinformatics problems. The authors first provide a comprehensive yet concise...

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Bibliographic Details
Online Access: Full Text (via Springer)
Main Author: Mukhopadhyay, Anirban
Other Authors: Ray, Sumanta, Maulik, Ujjwal, Bandyopadhyay, Sanghamitra, 1968-
Format: Electronic eBook
Language:English
Published: Singapore : Springer, 2024.
Subjects:
Data mining.
Mathematical optimization.
Bioinformatics.
Table of Contents:
  • Intro
  • Preface
  • Contents
  • 1 Introduction
  • 1.1 Concepts of Multiobjective Optimization
  • 1.2 MOO in Data Mining and Machine Learning
  • 1.2.1 Multiobjective Optimization in Clustering
  • 1.2.2 Multiobjective Optimization in Classification
  • 1.2.3 Multiobjective Optimization in Feature Selection
  • 1.2.4 Multiobjective Optimization in AssociationRule Mining
  • 1.2.5 Multiobjective Optimization in Other Data Mining Tasks
  • 1.3 Multiobjective Optimization for Bioinformatics Tasks
  • 1.3.1 Gene Expression Analysis
  • 1.3.2 Gene Clustering
  • 1.3.3 Coexpression Clustering
  • 1.3.4 Gene and MicroRNA Marker Detection
  • 1.3.5 Module Detection in Biological Networks
  • 1.4 Summary and Scope of the Book
  • 2 Multiobjective Interactive Fuzzy Clustering for Gene Expression Data
  • 2.1 Clustering and Validity Indices
  • 2.1.1 Fuzzy C-means Clustering
  • 2.1.2 Hierarchical Clustering
  • 2.1.3 Cluster Validity Indices
  • 2.1.3.1 Davies-Bouldin Index
  • 2.1.3.2 Xie-Beni Index
  • 2.1.3.3 Jm Index
  • 2.1.3.4 PBM Index
  • 2.1.3.5 Silhouette Index
  • 2.2 Multiobjective Fuzzy Clustering
  • 2.2.1 NSGA-II Algorithm
  • 2.2.2 Multiobjective Clustering
  • 2.3 Interactive Multiobjective Fuzzy Clustering (IMOC)
  • 2.4 Experimental Results
  • 2.4.1 Datasets for Experiments
  • 2.4.1.1 Human Fibroblasts Serum Dataset
  • 2.4.1.2 Yeast Cell Cycle
  • 2.4.2 Performance Measures
  • 2.4.3 Input Parameters
  • 2.4.4 Results and Discussion
  • 2.4.5 Statistical Significance Test
  • 2.5 Summary
  • 3 Multiobjective Rank Aggregation for Gene Prioritization
  • 3.1 Introduction
  • 3.2 Rank Aggregation Techniques
  • 3.2.1 MC4 Algorithm
  • 3.2.2 MCT Algorithm
  • 3.2.3 Robust Rank Aggregation
  • 3.2.4 Condorcet Ranking
  • 3.2.5 Rank Aggregation by Voting
  • 3.6.1.3 Preprocessing of the Datasets
  • 3.6.2 Results and Discussion
  • 3.6.2.1 Results for Artificial Datasets
  • 3.6.2.2 Results for Real-Life Datasets
  • 3.7 Summary
  • 4 Multiobjective Simultaneous Gene Ranking and Clustering
  • 4.1 Introduction
  • 4.2 Multiobjective Simultaneous Clustering and Gene Ranking
  • 4.2.1 Chromosome Representation and Initial Population
  • 4.2.2 Fitness Computation
  • 4.2.3 Crossover and Mutation
  • 4.2.4 Selection, Elitism, and Termination
  • 4.2.5 Final Solution Selection
  • 4.3 Experimental Results
  • 4.3.1 Experimental Design
  • 4.3.1.1 Artificial Datasets
  • 3.3 Distance Metrics for Ranking
  • 3.3.1 Kendall's Tau Distance (τ)
  • 3.3.2 Spearman's Footrule Distance (ρ)
  • 3.4 Objective Functions for Multiobjective Rank Aggregation
  • 3.5 Multiobjective PSO-based Rank Aggregation
  • 3.5.1 Encoding Mechanism of a Particle
  • 3.5.2 Initialization
  • 3.5.3 Computing the Fitness Values
  • 3.5.4 Updating the Position and Velocity
  • 3.5.5 Updating the Non-dominated Archive
  • 3.5.6 Overall Algorithm
  • 3.6 Experimental Results
  • 3.6.1 Datasets and Preprocessing
  • 3.6.1.1 Artificial Datasets
  • 3.6.1.2 Real-Life Datasets

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