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Advanced Computational Approaches to Biomedical Engineering [[electronic resource] /] / edited by Punam K. Saha, Ujjwal Maulik, Subhadip Basu
| Advanced Computational Approaches to Biomedical Engineering [[electronic resource] /] / edited by Punam K. Saha, Ujjwal Maulik, Subhadip Basu |
| Edizione | [1st ed. 2014.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2014 |
| Descrizione fisica | 1 online resource (224 p.) |
| Disciplina |
004
006.3 006.37 006.6 |
| Soggetto topico |
Artificial intelligence
Computational intelligence Bioinformatics Optical data processing Biomathematics Artificial Intelligence Computational Intelligence Computational Biology/Bioinformatics Image Processing and Computer Vision Mathematical and Computational Biology |
| ISBN | 3-642-41539-3 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Part I – Advanced Computational Methods -- Chap. 1 – Graph Algorithmic Techniques for Biomedical Image Segmentation -- Chap. 2 – Information Theoretic Clustering for Medical Image Segmentation -- Chap. 3 – Multiobjective Differential Evolution Based Fuzzy Clustering for MR Brain Image Segmentation -- Chap. 4 – Spectral and Non-linear Analysis of Thalamocortical Neural Mass Model Oscillatory Dynamics -- Chap. 5 – A Meta-learning Approach for Protein Function Prediction -- Part II – Biomedical Applications -- Chap. 6 – Segmentation of the Carotid Arteries from 3D Ultrasound Images -- Chap. 7 – Contemporary Problems in Quantitative Image Analysis in Structural Neuronal Plasticity -- Chap. 8 – Advanced MRI of Cartilage and Subchondral Bone in Osteoarthritis -- Chap. 9 – Computer Vision Based Hairline Mandibular Fracture Detection from Computed Tomography Images. |
| Record Nr. | UNINA-9910299057903321 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Analysis of biological data [[electronic resource] ] : a soft computing approach / / editors, Sanghamitra Bandyopadhyay, Ujjwal Maulik, Jason T.L. Wang
| Analysis of biological data [[electronic resource] ] : a soft computing approach / / editors, Sanghamitra Bandyopadhyay, Ujjwal Maulik, Jason T.L. Wang |
| Pubbl/distr/stampa | Singapore ; ; Hong Kong, : World Scientific, c2007 |
| Descrizione fisica | 1 online resource (352 p.) |
| Disciplina | 570.28563 |
| Altri autori (Persone) |
BandyopadhyaySanghamitra <1968->
MaulikUjjwal WangJason T. L |
| Collana | Science, engineering, and biology informatics |
| Soggetto topico |
Bioinformatics
Soft computing |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-281-91864-4
9786611918644 981-270-889-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
CONTENTS; Preface; Part I OVERVIEW; Chapter 1 Bioinformatics: Mining the Massive Data from High Throughput Genomics Experiments Haixu Tang and Sun Kim; 1 Introduction; 2 Recent Development of Classical Topics; 2.1 Sequence alignment; 2.2 Genome sequencing and fragment assembly; 2.3 Gene annotation; 2.4 RNA folding; 2.5 Motif finding; 2.6 Protein structure prediction; 3 Emerging Topics from New Genome Technologies; 3.1 Comparative genomics: beyond genome comparison; 3.2 Pathway reconstruction; 3.3 Microarray analysis; 3.4 Proteomics; 3.5 Protein-protein interaction; 4 Conclusion
AcknowledgementReferences; Chapter 2 An Introduction to Soft Computing Amit Konar and Swagatam Das; 1 Classical AI and its Pitfalls; 2 What is Soft Computing?; 3 Fundamental Components of Soft Computing; 3.1 Fuzzy sets and fuzzy logic; 3.2 Neural networks; 3.3 Genetic algorithms; 3.4 Belief networks; 4 Synergism in Soft Computing; 4.1 Neuro-fuzzy synergism; 4.2 Neuro-GA synergism; 4.3 Fuzzy-GA synergism; 4.4 Neuro-belief network synergism; 4.5 GA-belief network synergism; 4.6 Neuro-fuzzy-GA synergism; 5 Some Emerging Areas of Soft Computing; 5.1 Artificial life 5.2 Particle swarm optimization (PSO)5.3 Artificial immune system; 5.4 Rough sets and granular computing; 5.5 Chaos theory; 5.6 Ant colony systems (ACS); 6 Summary; References; Part II BIOLOGICAL SEQUENCE AND STRUCTURE ANALYSIS; Chapter 3 Reconstructing Phylogenies with Memetic Algorithms and Branch-and-Bound José E. Gallardo, Carlos Cotta and Antonio J. Fernández; 1 Introduction; 2 A Crash Introduction to Phylogenetic Inference; 3 Evolutionary Algorithms for the Phylogeny Problem; 4 A BnB Algorithm for Phylogenetic Inference; 5 A Memetic Algorithm for Phylogenetic Inference 6 A Hybrid Algorithm7 Experimental Results; 7.1 Experimental setting; 7.2 Sensitivity analysis on the hybrid algorithm; 7.3 Analysis of results; 8 Conclusions; Acknowledgment; References; Chapter 4 Classification ofRNASequences with Support Vector Machines Jason T. L. Wang and Xiaoming Wu; 1 Introduction; 2 Count Kernels and Marginalized Count Kernels; 2.1 RNA sequences with known secondary structures; 2.2 RNA sequences with unknown secondary structures; 3 Kernel Based on Labeled Dual Graphs; 3.1 Labeled dual graphs; 3.2 Marginalized kernel for labeled dual graphs; 4 A New Kernel 4.1 Extracting features for global structural information4.2 Extracting features for local structural information; 5 Experiments and Results; 5.1 Data and parameters; 5.2 Results; 6 Conclusion; Acknowledgment; References; Chapter 5 Beyond String Algorithms: Protein Sequence Analysis using Wavelet Transforms Arun Krishnan and Kuo-Bin Li; 1 Introduction; 1.1 String algorithms; 1.2 Sequence analysis; 1.3 Wavelet transform; 2 Motif Searching; 2.1 Introduction; 2.2 Methods; 2.3 Results; 2.4 Allergenicity prediction; 3 Transmembrane Helix Region (HTM) Prediction; 4 Hydrophobic Cores 5 Protein Repeat Motifs |
| Record Nr. | UNINA-9910451542303321 |
| Singapore ; ; Hong Kong, : World Scientific, c2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Analysis of biological data [[electronic resource] ] : a soft computing approach / / editors, Sanghamitra Bandyopadhyay, Ujjwal Maulik, Jason T.L. Wang
| Analysis of biological data [[electronic resource] ] : a soft computing approach / / editors, Sanghamitra Bandyopadhyay, Ujjwal Maulik, Jason T.L. Wang |
| Pubbl/distr/stampa | Singapore ; ; Hong Kong, : World Scientific, c2007 |
| Descrizione fisica | 1 online resource (352 p.) |
| Disciplina | 570.28563 |
| Altri autori (Persone) |
BandyopadhyaySanghamitra <1968->
MaulikUjjwal WangJason T. L |
| Collana | Science, engineering, and biology informatics |
| Soggetto topico |
Bioinformatics
Soft computing |
| ISBN |
1-281-91864-4
9786611918644 981-270-889-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
CONTENTS; Preface; Part I OVERVIEW; Chapter 1 Bioinformatics: Mining the Massive Data from High Throughput Genomics Experiments Haixu Tang and Sun Kim; 1 Introduction; 2 Recent Development of Classical Topics; 2.1 Sequence alignment; 2.2 Genome sequencing and fragment assembly; 2.3 Gene annotation; 2.4 RNA folding; 2.5 Motif finding; 2.6 Protein structure prediction; 3 Emerging Topics from New Genome Technologies; 3.1 Comparative genomics: beyond genome comparison; 3.2 Pathway reconstruction; 3.3 Microarray analysis; 3.4 Proteomics; 3.5 Protein-protein interaction; 4 Conclusion
AcknowledgementReferences; Chapter 2 An Introduction to Soft Computing Amit Konar and Swagatam Das; 1 Classical AI and its Pitfalls; 2 What is Soft Computing?; 3 Fundamental Components of Soft Computing; 3.1 Fuzzy sets and fuzzy logic; 3.2 Neural networks; 3.3 Genetic algorithms; 3.4 Belief networks; 4 Synergism in Soft Computing; 4.1 Neuro-fuzzy synergism; 4.2 Neuro-GA synergism; 4.3 Fuzzy-GA synergism; 4.4 Neuro-belief network synergism; 4.5 GA-belief network synergism; 4.6 Neuro-fuzzy-GA synergism; 5 Some Emerging Areas of Soft Computing; 5.1 Artificial life 5.2 Particle swarm optimization (PSO)5.3 Artificial immune system; 5.4 Rough sets and granular computing; 5.5 Chaos theory; 5.6 Ant colony systems (ACS); 6 Summary; References; Part II BIOLOGICAL SEQUENCE AND STRUCTURE ANALYSIS; Chapter 3 Reconstructing Phylogenies with Memetic Algorithms and Branch-and-Bound José E. Gallardo, Carlos Cotta and Antonio J. Fernández; 1 Introduction; 2 A Crash Introduction to Phylogenetic Inference; 3 Evolutionary Algorithms for the Phylogeny Problem; 4 A BnB Algorithm for Phylogenetic Inference; 5 A Memetic Algorithm for Phylogenetic Inference 6 A Hybrid Algorithm7 Experimental Results; 7.1 Experimental setting; 7.2 Sensitivity analysis on the hybrid algorithm; 7.3 Analysis of results; 8 Conclusions; Acknowledgment; References; Chapter 4 Classification ofRNASequences with Support Vector Machines Jason T. L. Wang and Xiaoming Wu; 1 Introduction; 2 Count Kernels and Marginalized Count Kernels; 2.1 RNA sequences with known secondary structures; 2.2 RNA sequences with unknown secondary structures; 3 Kernel Based on Labeled Dual Graphs; 3.1 Labeled dual graphs; 3.2 Marginalized kernel for labeled dual graphs; 4 A New Kernel 4.1 Extracting features for global structural information4.2 Extracting features for local structural information; 5 Experiments and Results; 5.1 Data and parameters; 5.2 Results; 6 Conclusion; Acknowledgment; References; Chapter 5 Beyond String Algorithms: Protein Sequence Analysis using Wavelet Transforms Arun Krishnan and Kuo-Bin Li; 1 Introduction; 1.1 String algorithms; 1.2 Sequence analysis; 1.3 Wavelet transform; 2 Motif Searching; 2.1 Introduction; 2.2 Methods; 2.3 Results; 2.4 Allergenicity prediction; 3 Transmembrane Helix Region (HTM) Prediction; 4 Hydrophobic Cores 5 Protein Repeat Motifs |
| Record Nr. | UNINA-9910784816603321 |
| Singapore ; ; Hong Kong, : World Scientific, c2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Analysis of biological data [[electronic resource] ] : a soft computing approach / / editors, Sanghamitra Bandyopadhyay, Ujjwal Maulik, Jason T.L. Wang
| Analysis of biological data [[electronic resource] ] : a soft computing approach / / editors, Sanghamitra Bandyopadhyay, Ujjwal Maulik, Jason T.L. Wang |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Singapore ; ; Hong Kong, : World Scientific, c2007 |
| Descrizione fisica | 1 online resource (352 p.) |
| Disciplina | 570.28563 |
| Altri autori (Persone) |
BandyopadhyaySanghamitra <1968->
MaulikUjjwal WangJason T. L |
| Collana | Science, engineering, and biology informatics |
| Soggetto topico |
Bioinformatics
Soft computing |
| ISBN |
1-281-91864-4
9786611918644 981-270-889-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
CONTENTS; Preface; Part I OVERVIEW; Chapter 1 Bioinformatics: Mining the Massive Data from High Throughput Genomics Experiments Haixu Tang and Sun Kim; 1 Introduction; 2 Recent Development of Classical Topics; 2.1 Sequence alignment; 2.2 Genome sequencing and fragment assembly; 2.3 Gene annotation; 2.4 RNA folding; 2.5 Motif finding; 2.6 Protein structure prediction; 3 Emerging Topics from New Genome Technologies; 3.1 Comparative genomics: beyond genome comparison; 3.2 Pathway reconstruction; 3.3 Microarray analysis; 3.4 Proteomics; 3.5 Protein-protein interaction; 4 Conclusion
AcknowledgementReferences; Chapter 2 An Introduction to Soft Computing Amit Konar and Swagatam Das; 1 Classical AI and its Pitfalls; 2 What is Soft Computing?; 3 Fundamental Components of Soft Computing; 3.1 Fuzzy sets and fuzzy logic; 3.2 Neural networks; 3.3 Genetic algorithms; 3.4 Belief networks; 4 Synergism in Soft Computing; 4.1 Neuro-fuzzy synergism; 4.2 Neuro-GA synergism; 4.3 Fuzzy-GA synergism; 4.4 Neuro-belief network synergism; 4.5 GA-belief network synergism; 4.6 Neuro-fuzzy-GA synergism; 5 Some Emerging Areas of Soft Computing; 5.1 Artificial life 5.2 Particle swarm optimization (PSO)5.3 Artificial immune system; 5.4 Rough sets and granular computing; 5.5 Chaos theory; 5.6 Ant colony systems (ACS); 6 Summary; References; Part II BIOLOGICAL SEQUENCE AND STRUCTURE ANALYSIS; Chapter 3 Reconstructing Phylogenies with Memetic Algorithms and Branch-and-Bound José E. Gallardo, Carlos Cotta and Antonio J. Fernández; 1 Introduction; 2 A Crash Introduction to Phylogenetic Inference; 3 Evolutionary Algorithms for the Phylogeny Problem; 4 A BnB Algorithm for Phylogenetic Inference; 5 A Memetic Algorithm for Phylogenetic Inference 6 A Hybrid Algorithm7 Experimental Results; 7.1 Experimental setting; 7.2 Sensitivity analysis on the hybrid algorithm; 7.3 Analysis of results; 8 Conclusions; Acknowledgment; References; Chapter 4 Classification ofRNASequences with Support Vector Machines Jason T. L. Wang and Xiaoming Wu; 1 Introduction; 2 Count Kernels and Marginalized Count Kernels; 2.1 RNA sequences with known secondary structures; 2.2 RNA sequences with unknown secondary structures; 3 Kernel Based on Labeled Dual Graphs; 3.1 Labeled dual graphs; 3.2 Marginalized kernel for labeled dual graphs; 4 A New Kernel 4.1 Extracting features for global structural information4.2 Extracting features for local structural information; 5 Experiments and Results; 5.1 Data and parameters; 5.2 Results; 6 Conclusion; Acknowledgment; References; Chapter 5 Beyond String Algorithms: Protein Sequence Analysis using Wavelet Transforms Arun Krishnan and Kuo-Bin Li; 1 Introduction; 1.1 String algorithms; 1.2 Sequence analysis; 1.3 Wavelet transform; 2 Motif Searching; 2.1 Introduction; 2.2 Methods; 2.3 Results; 2.4 Allergenicity prediction; 3 Transmembrane Helix Region (HTM) Prediction; 4 Hydrophobic Cores 5 Protein Repeat Motifs |
| Record Nr. | UNINA-9910827968903321 |
| Singapore ; ; Hong Kong, : World Scientific, c2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Computational intelligence and pattern analysis in biology informatics [[electronic resource] /] / edited by Ujjwal Maulik, Sanghamitra Bandyopadhyay, Jason T. Wang
| Computational intelligence and pattern analysis in biology informatics [[electronic resource] /] / edited by Ujjwal Maulik, Sanghamitra Bandyopadhyay, Jason T. Wang |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Hoboken, NJ, : John Wiley & Sons, c2010 |
| Descrizione fisica | 1 online resource (395 p.) |
| Disciplina | 570.285 |
| Altri autori (Persone) |
MaulikUjjwal
BandyopadhyaySanghamitra <1968-> WangJason T. L |
| Collana | Wiley Series in Bioinformatics |
| Soggetto topico |
Bioinformatics
Computational biology Artificial intelligence Pattern recognition systems |
| ISBN |
1-282-72891-1
9786612728914 0-470-87235-7 0-470-87234-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
COMPUTATIONAL INTELLIGENCE AND PATTERN ANALYSIS IN BIOLOGICAL INFORMATICS; CONTENTS; Preface; Contributors; PART I INTRODUCTION; 1 Computational Intelligence: Foundations, Perspectives, and Recent Trends; 2 Fundamentals of Pattern Analysis: A Brief Overview; 3 Biological Informatics: Data, Tools, and Applications; PART II SEQUENCE ANALYSIS; 4 Promoter Recognition Using Neural Network Approaches; 5 Predicting microRNA Prostate Cancer Target Genes; PART III STRUCTURE ANALYSIS; 6 Structural Search in RNA Motif Databases; 7 Kernels on Protein Structures
8 Characterization of Conformational Patterns in Active and Inactive Forms of Kinases using Protein Blocks Approach9 Kernel Function Applications in Cheminformatics; 10 In Silico Drug Design Using a Computational Intelligence Technique; PART IV MICROARRAY DATA ANALYSIS; 11 Integrated Differential Fuzzy Clustering for Analysis of Microarray Data; 12 Identifying Potential Gene Markers Using SVM Classifier Ensemble; 13 Gene Microarray Data Analysis Using Parallel Point Symmetry-Based Clustering; PART V SYSTEMS BIOLOGY; 14 Techniques for Prioritization of Candidate Disease Genes 15 Prediction of Protein-Protein Interactions16 Analyzing Topological Properties of Protein-Protein Interaction Networks: A Perspective Toward Systems Biology; Index; Wiley Series on Bioinformatics: Computational Techniques and Engineering |
| Record Nr. | UNINA-9910140822003321 |
| Hoboken, NJ, : John Wiley & Sons, c2010 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Computational intelligence and pattern analysis in biology informatics [[electronic resource] /] / edited by Ujjwal Maulik, Sanghamitra Bandyopadhyay, Jason T. Wang
| Computational intelligence and pattern analysis in biology informatics [[electronic resource] /] / edited by Ujjwal Maulik, Sanghamitra Bandyopadhyay, Jason T. Wang |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Hoboken, NJ, : John Wiley & Sons, c2010 |
| Descrizione fisica | 1 online resource (395 p.) |
| Disciplina | 570.285 |
| Altri autori (Persone) |
MaulikUjjwal
BandyopadhyaySanghamitra <1968-> WangJason T. L |
| Collana | Wiley Series in Bioinformatics |
| Soggetto topico |
Bioinformatics
Computational biology Artificial intelligence Pattern recognition systems |
| ISBN |
1-282-72891-1
9786612728914 0-470-87235-7 0-470-87234-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
COMPUTATIONAL INTELLIGENCE AND PATTERN ANALYSIS IN BIOLOGICAL INFORMATICS; CONTENTS; Preface; Contributors; PART I INTRODUCTION; 1 Computational Intelligence: Foundations, Perspectives, and Recent Trends; 2 Fundamentals of Pattern Analysis: A Brief Overview; 3 Biological Informatics: Data, Tools, and Applications; PART II SEQUENCE ANALYSIS; 4 Promoter Recognition Using Neural Network Approaches; 5 Predicting microRNA Prostate Cancer Target Genes; PART III STRUCTURE ANALYSIS; 6 Structural Search in RNA Motif Databases; 7 Kernels on Protein Structures
8 Characterization of Conformational Patterns in Active and Inactive Forms of Kinases using Protein Blocks Approach9 Kernel Function Applications in Cheminformatics; 10 In Silico Drug Design Using a Computational Intelligence Technique; PART IV MICROARRAY DATA ANALYSIS; 11 Integrated Differential Fuzzy Clustering for Analysis of Microarray Data; 12 Identifying Potential Gene Markers Using SVM Classifier Ensemble; 13 Gene Microarray Data Analysis Using Parallel Point Symmetry-Based Clustering; PART V SYSTEMS BIOLOGY; 14 Techniques for Prioritization of Candidate Disease Genes 15 Prediction of Protein-Protein Interactions16 Analyzing Topological Properties of Protein-Protein Interaction Networks: A Perspective Toward Systems Biology; Index; Wiley Series on Bioinformatics: Computational Techniques and Engineering |
| Record Nr. | UNINA-9910822611603321 |
| Hoboken, NJ, : John Wiley & Sons, c2010 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Multiobjective Optimization Algorithms for Bioinformatics
| Multiobjective Optimization Algorithms for Bioinformatics |
| Autore | Mukhopadhyay Anirban |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore Pte. Limited, , 2024 |
| Descrizione fisica | 1 online resource (246 pages) |
| Altri autori (Persone) |
RaySumanta
MaulikUjjwal BandyopadhyaySanghamitra |
| ISBN | 981-9716-31-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| 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. |
| Record Nr. | UNINA-9910865244303321 |
Mukhopadhyay Anirban
|
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| Singapore : , : Springer Singapore Pte. Limited, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Operations Research and Optimization [[electronic resource] ] : FOTA 2016, Kolkata, India, November 24-26 / / edited by Samarjit Kar, Ujjwal Maulik, Xiang Li
| Operations Research and Optimization [[electronic resource] ] : FOTA 2016, Kolkata, India, November 24-26 / / edited by Samarjit Kar, Ujjwal Maulik, Xiang Li |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (399 pages) |
| Disciplina | 658.4034 |
| Collana | Springer Proceedings in Mathematics & Statistics |
| Soggetto topico |
Calculus of variations
Operations research Management science Mathematical optimization Calculus of Variations and Optimal Control; Optimization Operations Research, Management Science Continuous Optimization Discrete Optimization |
| ISBN | 981-10-7814-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Monalisa Mandal, Anirban Mukhopadhyay and Ujjwal Maulik, A Genetic Algorithm-based Clustering Approach for Selecting Non-redundant Micro-RNA Markers from Microarray Expression Data -- Nan-Bin Cao and Yu-Ping Zhang, The General Solutions to Some Systems of Adjointable Operator Equations -- Kaushik Das Sharma, Amitava Chatterjee, Patrick Siarry and Anjan Rakshit, CMA-H∞ Hybrid Design of Robust Stable Adaptive Fuzzy Controllers for Non-Linear Systems -- Hongfang Bai and Rui Xu, Global Stability of a Delayed Eco-epidemiological Model with Holling Type-III Functional Response -- Jyotirmoy Sarkar, Score-Based Secretary Problem -- Monalisa Pal, Amr Alzouhri Alya, Sanghamitra Bandyopadhyay, Stephane Ploix and Patrick Reignier, Enhancing Comfort of Occupants in Energy Buildings -- Tatsua Oyama, Applying OR Theory and Techniques to Social Systems Analysis -- A.D. Banik, Souvik Ghosh and Debasis Basu, Computational Analysis of a Single Server Queue with Batch Markovian Arrival and Exponential Single Working Vacation -- Gopinath Panda, A.D. Banik and M.L. Chaudhry, Computational Analysis of the GI/G/1 Risk Process using Roots -- Banerjee, Ant Lion Optimization: A Novel Algorithm Applied to Load Frequency Control Problem in Power System -- Suvasis Nayak and A.K. Ojha, A Solution Approach to Multi-level Nonlinear Fractional Programming Problem -- D. Sadhukhan, B. Mondal and M. Maiti, Bio-economic Prey-predstor Fishery Model with Intratrophic Predation, Time-delay in Reserved and Unreserved Area -- A.K. Das, R. Jana and Deepmala, On Generalized Positive Subdefinite Matrices and Interior Point Algorithm -- Totan Garai, Dipankar Chakraborty and Tapan Kumar Roy, A Multi-item Inventory Model with Fuzzy Rough Coefficients via Fuzzy Rough Expectation. Shantanu Jana, Nibaran Das, Ram Sarkar and Mita Nasipuri, Recognition System to Separate Text and Graphics from Indian Newspaper -- Yadvendra Singh and S. K. Mishra, Saddle Point Criteria for Semi-infinite Programming Problems via an h-Approximation Method -- A. Banerjee, K. Sidkar and G.K. Gupta, On Finite Buffer BMAP/G/1 Queue with Queue Length Dependent Service -- Deepa Naik, Himansu Rathi, Asish Dhara and Tanmay De, Ageing and Priority Based Scheduling for Uplink in WiMAX Networks -- Bindu Kaushal and Shalini Arora, Fixed Charge Bulk Transportation Problem -- Himanshu Shrivastava, Pankaj Dutta, Mohan Krishnamoorthy and Pravin Suryawanshi, Designing a Resilient Supply Chain Network for Perishable Products with Random Disruptions -- Anupama Chanda, R.N. Mukherjee and Bijan Sarkar, Performance Evaluation of Management Faculty using Hybrid Model of Logic-AHP -- Oshmita Dey and Anindita Mukherjee, An Integrated Imperfect Production-Inventory Model with Lot-Size Dependent Lead-Time and Quality Control -- Kajal Chatterjee, Edmundas Kazimieras Zavadskas, Jagannath Roy and Samarjit Kar, Performance Evaluation of Green Supply Chain Management using the Grey DEMATEL-ARAS model -- Dipanjana Sengupta and Uttam Kumar Bera, Reduction of Type-2 Lognormal Uncertain Variable and Its Application to a Two-stage Solid Transportation Problem -- Naorem Nalini Devi, Khundrakpam Johnson Singh and Tanmay De, ICMP-DDoS Attack Detection using Clustering-Based Neural Networks Techniques -- Arindam Roy, Goutam Chakraborty, Indadul Khan, Samir Maity, Manoranjan Maiti, A Hybrid Heuristic for Restricted 4-Dimensional TSP (r-4DTSP). |
| Record Nr. | UNINA-9910300098103321 |
| Singapore : , : Springer Singapore : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Quantum inspired meta-heuristics for image analysis / / Sandip Dey, Siddhartha Bhattacharyya, Ujjwal Maulik
| Quantum inspired meta-heuristics for image analysis / / Sandip Dey, Siddhartha Bhattacharyya, Ujjwal Maulik |
| Autore | Dey Sandip <1977-> |
| Pubbl/distr/stampa | Hoboken, NJ : , : Wiley, , 2019 |
| Descrizione fisica | 1 online resource (359 pages) |
| Disciplina | 006.42015181 |
| Soggetto topico |
Image segmentation
Metaheuristics Image analysis Heuristic algorithms |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-119-48876-1
1-119-48877-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910555024903321 |
|
Dey Sandip <1977->
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| Hoboken, NJ : , : Wiley, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Quantum inspired meta-heuristics for image analysis / / Sandip Dey, Siddhartha Bhattacharyya, Ujjwal Maulik
| Quantum inspired meta-heuristics for image analysis / / Sandip Dey, Siddhartha Bhattacharyya, Ujjwal Maulik |
| Autore | Dey Sandip <1977-> |
| Pubbl/distr/stampa | Hoboken, NJ : , : Wiley, , 2019 |
| Descrizione fisica | 1 online resource (359 pages) |
| Disciplina | 006.42015181 |
| Soggetto topico |
Image segmentation
Metaheuristics Image analysis Heuristic algorithms |
| ISBN |
1-119-48876-1
1-119-48877-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910677895003321 |
|
Dey Sandip <1977->
|
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| Hoboken, NJ : , : Wiley, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
