Multiobjective Optimization Algorithms for Bioinformatics

• Autore: Mukhopadhyay Anirban
• Pubblicazione: Singapore : , : Springer Singapore Pte. Limited, , 2024; ©2024
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (246 pages)
• Altri autori: RaySumanta MaulikUjjwal BandyopadhyaySanghamitra
• Nota di contenuto: 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 -- 3.3 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 -- 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 -- 4.3.1.2 Real-life Datasets -- 4.3.1.3 Parameter Settings -- 4.3.1.4 Performance Measures -- 4.3.1.5 Competitive Methods -- 4.3.2 Result and Discussion -- 4.4 Summary -- 5 Multiobjective Feature Selection for Identifying MicroRNA Markers -- 5.1 Introduction -- 5.2 Multiobjective Feature Selection -- 5.2.1 Encoding Scheme and Initialization -- 5.2.2 Computing the Objectives -- 5.2.3 Reproduction Using Selection, Crossover, and Mutation -- 5.2.4 Maintaining an Archive -- 5.2.5 Selecting the Final Solution -- 5.3 Experimental Results -- 5.3.1 Comparative Methods -- 5.3.2 Datasets and Preprocessing -- 5.3.3 Evaluation Metrics -- 5.3.4 Results and Discussion -- 5.4 Summary -- 6 Multiobjective Approach to Detection of Differentially Coexpressed Modules -- 6.1 Introduction. ; 6.2 DiffCoMO: Differential Coexpressed Module Detection -- 6.2.1 Differential Coexpression of Gene in Two Phenotypes -- 6.2.2 The DiffCoMO Framework -- 6.2.2.1 Objective Functions -- 6.2.3 Evaluating Objective Functions -- 6.3 Experimental Results -- 6.3.1 Description of Dataset -- 6.3.2 Comparing DiffCoMO with Some State of the Art -- 6.3.3 Statistical Significance of Identified Modules -- 6.3.4 Performance on a Simulated Dataset -- 6.3.5 Biological Validation of Modules -- 6.3.5.1 GO and Pathway Enrichment -- 6.3.5.2 miRNA Enrichment -- 6.3.6 Performance of DiffCoMO in Expression Data with Large Samples -- 6.4 Summary -- 7 Multiobjective Approach to Cancer-Associated MicroRNA Module Detection -- 7.1 Introduction -- 7.2 Construction of Differential Coexpression Network -- 7.3 Semantic Similarity Measure for MicroRNA Pairs -- 7.4 Multiobjective Module Detection -- 7.4.1 Chromosome Encoding -- 7.4.2 Computation of Objective Functions -- 7.4.3 Process of Obtaining Non-dominated Solutions -- 7.4.4 Obtaining the miRNA Subset from the Non-dominated Solutions -- 7.5 Experimental Results -- 7.5.1 Dataset Details and Preprocessing -- 7.5.2 Parameter Setting -- 7.5.3 Results -- 7.5.4 Statistical Significance of the Identified Module -- 7.5.5 Comparison with State-of-the-Art Algorithms -- 7.5.6 Biological Relevance Study -- 7.6 Summary -- 8 Multiobjective Approach to Prediction of Protein Subcellular Locations -- 8.1 Introduction -- 8.2 Feature Extraction from Amino Acid Sequence -- 8.3 Relevance and Redundancy of Features -- 8.4 Multiobjective PSO-Based Feature Selection Technique -- 8.4.1 Particle Encoding -- 8.4.2 Initialization and Inputs -- 8.4.3 Objective Functions -- 8.4.4 Updating Position and Velocity -- 8.4.5 Updating Archive -- 8.4.6 Final Solution Selection -- 8.4.7 Overall MOPSO Algorithm -- 8.5 Other Comparative Methods. ; 8.6 Dataset and Preprocessing -- 8.7 Experimental Results -- 8.7.1 Results -- 8.7.2 Results on Independent Dataset -- 8.8 Summary -- 9 Multiobjective Approach to Gene Ontology-Based Protein-Protein Interaction Prediction -- 9.1 Introduction -- 9.2 GO-Based Semantic Similarity -- 9.2.1 Resnik Measure -- 9.2.2 Lin Measure -- 9.2.3 Jiang-Conrath Measure -- 9.2.4 Relevance Measure -- 9.2.5 Cosine Measure -- 9.2.6 Kappa Measure -- 9.2.7 Czekanowski-Dice Measure -- 9.2.8 Weighted Jaccard Measure -- 9.2.9 Graph-Based Similarity Measure -- 9.2.10 Avg, Max, Rcmax -- 9.3 Dataset Preparation -- 9.3.1 Calculation of GO-Based Semantic Similarity of Protein Pairs -- 9.3.2 Dataset Creation -- 9.4 DEMO-Based Feature Selection -- 9.4.1 Chromosome Encoding -- 9.4.2 Evaluating Chromosomes -- 9.4.3 Offspring Creation -- 9.4.4 Truncation of Population -- 9.4.5 Selecting the Final Solution -- 9.5 Experimental Results -- 9.6 Summary -- 10 Multiobjective Approach to Protein Complex Detection -- 10.1 Introduction -- 10.2 Multiobjective Protein Complex Detection -- 10.2.1 Chromosome Representation -- 10.2.2 Population Initialization -- 10.2.3 Representation of Objective Functions -- 10.2.3.1 Topological Property-Based Objective Functions -- 10.2.3.2 Gene Ontology-Based Objective Function -- 10.2.4 Mutation Procedure -- 10.2.5 Final Solution -- 10.3 Experimental Results -- 10.3.1 Performance Comparisons Among Different Methods -- 10.3.1.1 Sensitivity -- 10.3.1.2 Positive Predictive Value -- 10.3.1.3 Accuracy -- 10.3.2 Analysis of Predicted Complexes -- 10.3.3 Association of Predicted Complexes in Disorders/Diseases -- 10.3.3.1 Involvement of Identified Complexes in 22 Primary Disorders/Disease Classes -- 10.3.3.2 Complex-Disease Bipartite Network -- 10.4 Summary -- 11 Multiobjective Biclustering for Analyzing HIV-1-Human Protein-Protein Interaction Network -- 11.1 Introduction. ; 11.2 Strong PPI Module Finding Using Biclustering -- 11.2.1 Biclustering -- 11.2.2 Bipartite Graph Representation of PPIN -- 11.2.3 Quasi-Biclique Finding Through Biclustering -- 11.3 Multiobjective Biclustering for Finding Quasi-Bicliques -- 11.3.1 MOBICLUST Algorithm -- 11.4 Evaluation of MOBICLUST Using Artificial Data -- 11.4.1 Preparing the Artificial Dataset -- 11.4.2 Performance Metric -- 11.4.3 Results of Comparison -- 11.5 Analysis of Quasi-Bicliques from HIV-1-Human PPIN -- 11.5.1 Preparation of the HIV-1-Human PPIN -- 11.5.2 Results of MOBICLUST Biclustering -- 11.5.3 Biological Significance of the Quasi-Bicliques -- 11.5.4 Biological Significance of the Strong BipartiteModule -- 11.5.4.1 Study from Gene Ontology -- 11.5.4.2 Study from KEGG Pathway -- 11.5.4.3 Interactions Within Human PPIN -- 11.6 Summary -- References -- Index.
• Titolo autorizzato: Multiobjective Optimization Algorithms for Bioinformatics
• ISBN: 981-9716-31-4
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910865244303321
• Lo trovi qui: Univ. Federico II