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دانلود کتاب Computational Intelligence Methods for Bioinformatics and Biostatistics: 15th International Meeting, CIBB 2018, Caparica, Portugal, September 6–8, ... (Lecture Notes in Computer Science, 11925)

دانلود کتاب روش‌های هوش محاسباتی برای بیوانفورماتیک و آمار زیستی: پانزدهمین نشست بین‌المللی، CIBB 2018، کاپاریکا، پرتغال، 6 تا 8 سپتامبر، ... (یادداشت‌های سخنرانی در علوم کامپیوتر، 11925)

Computational Intelligence Methods for Bioinformatics and Biostatistics: 15th International Meeting, CIBB 2018, Caparica, Portugal, September 6–8, ... (Lecture Notes in Computer Science, 11925)

مشخصات کتاب

Computational Intelligence Methods for Bioinformatics and Biostatistics: 15th International Meeting, CIBB 2018, Caparica, Portugal, September 6–8, ... (Lecture Notes in Computer Science, 11925)

ویرایش:  
نویسندگان: , , , ,   
سری:  
ISBN (شابک) : 303034584X, 9783030345846 
ناشر: Springer 
سال نشر: 2020 
تعداد صفحات: 351 
زبان: English 
فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود) 
حجم فایل: 36 مگابایت 

قیمت کتاب (تومان) : 45,000



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در صورت تبدیل فایل کتاب Computational Intelligence Methods for Bioinformatics and Biostatistics: 15th International Meeting, CIBB 2018, Caparica, Portugal, September 6–8, ... (Lecture Notes in Computer Science, 11925) به فرمت های PDF، EPUB، AZW3، MOBI و یا DJVU می توانید به پشتیبان اطلاع دهید تا فایل مورد نظر را تبدیل نمایند.

توجه داشته باشید کتاب روش‌های هوش محاسباتی برای بیوانفورماتیک و آمار زیستی: پانزدهمین نشست بین‌المللی، CIBB 2018، کاپاریکا، پرتغال، 6 تا 8 سپتامبر، ... (یادداشت‌های سخنرانی در علوم کامپیوتر، 11925) نسخه زبان اصلی می باشد و کتاب ترجمه شده به فارسی نمی باشد. وبسایت اینترنشنال لایبرری ارائه دهنده کتاب های زبان اصلی می باشد و هیچ گونه کتاب ترجمه شده یا نوشته شده به فارسی را ارائه نمی دهد.


توضیحاتی در مورد کتاب روش‌های هوش محاسباتی برای بیوانفورماتیک و آمار زیستی: پانزدهمین نشست بین‌المللی، CIBB 2018، کاپاریکا، پرتغال، 6 تا 8 سپتامبر، ... (یادداشت‌های سخنرانی در علوم کامپیوتر، 11925)

این کتاب مجموعه مقالات پس از کنفرانس با داوری کامل پانزدهامین نشست بین المللی روش های هوش محاسباتی برای بیوانفورماتیک و آمار زیستی است.، CIBB 2018، در کاپاریکا، پرتغال، در سپتامبر 2018 برگزار شد. 32 مقاله کامل اصلاح شده با دقت بررسی و از بین 51 مورد ارسالی انتخاب شدند. این مقالات روندهای فعلی در لبه علوم کامپیوتر و زندگی، کاربرد هوش محاسباتی در یک سیستم و زیست شناسی مصنوعی و تأثیر متعاقب آن بر پزشکی نوآور ارائه شده است. زیست‌شناسان نظری و تجربی نیز چالش‌های جدیدی را ارائه کردند و همکاری چند رشته‌ای را با هدف ترکیب تئوری و عمل تقویت کردند، جایی که نظریه‌های پایه‌گذاری تکنیک‌های مورد استفاده برای مدل‌سازی و تحلیل سیستم‌های بیولوژیکی بررسی شده و برای کاربردهای عملی و فناوری‌های پشتیبان مورد استفاده قرار می‌گیرند.


توضیحاتی درمورد کتاب به خارجی

This book constitutes the thoroughly refereed post-conference proceedings of the 15th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics., CIBB 2018, held in Caparica, Portugal, in September 2018. The 32 revised full papers were carefully reviewed and selected from 51 submissions. The papers present current trends at the edge of computer and life sciences, the application of computational intelligence to a system and synthetic biology and the consequent impact on innovative medicine were presented. Theoretical and experimental biologists also presented novel challenges and fostered multidisciplinary collaboration aiming to blend theory and practice, where the founding theories of the techniques used for modelling and analyzing biological systems are investigated and used for practical applications and the supporting technologies.



فهرست مطالب

Preface
Organization
Keynote Abstracts
A Unified Regularized Group PLS Algorithm Scalable to Big Data. Application on Genomics Data
Sparse Graphical Models in Genomics: An Application to Censored qPCR Data
Answering Questions in Biology and Medicine by Making Inferences on Networks
Model Selection for Temporal Biomedical Data
Ethics and Our Moral in Research, Let’s Think About It!
Contents
Computational Intelligence Methods for Bioinformatics and Biostatistics
Compressive Sensing and Hierarchical Clustering for Microarray Data with Missing Values
	1 Scientific Background
	2 Materials and Methods
	3 Experimental Results
	4 Conclusions
	References
Variational Inference in Probabilistic Single-cell RNA-seq Models
	1 Scientific Background
	2 Materials and Methods
		2.1 Variational Inference
		2.2 Probabilistic Count Matrix Factorization (pCMF)
		2.3 Single Cell Variational Inference (scVI)
	3 Results
	4 Conclusion
	References
Centrality Speeds the Subgraph Isomorphism Search Up in Target Aware Contexts
	1 Introduction
	2 Materials and Methods
		2.1 Basic Notions
		2.2 Variable Ordering in Subgraph Isomorphism
		2.3 Ordering Strategies by Weighted Centralities
		2.4 Data
	3 Results
	4 Conclusion
	References
Structure-Based Antibody Paratope Prediction with 3D Zernike Descriptors and SVM
	1 Introduction
		1.1 Algorithm Overview
	2 Scientific Background
	3 Materials and Methods
		3.1 Antibody Surface Representation
		3.2 Paratope Definition
		3.3 Datasets
		3.4 Feature Selection
		3.5 3D Zernike Descriptors
		3.6 Patch Representation Using 3D Zernike Descriptors
		3.7 Support Vector Machine
		3.8 SVM Model Selection
		3.9 Post-processing
		3.10 Identifying Paratope Residues
	4 Experimental Results
	5 Conclusions
	References
Simultaneous Phasing of Multiple Polyploids
	1 Introduction
	2 Scientific Background
	3 Materials and Methods
		3.1 Greedy Algorithm
		3.2 Algebraic Method
	4 Experimental Results
		4.1 Data Simulation
		4.2 Measuring Accuracy
		4.3 Comparing the Performance of the Methods
	5 Conclusion
	6  Supplement
		6.1  On the Greedy Method
		6.2  On the Algebraic Method
	References
Classification of Epileptic Activity Through Temporal and Spatial Characterization of Intracranial Recordings
	1 Introduction
		1.1 Scientific Background
	2 Material and Methods
		2.1 Time-Frequency Features
		2.2 Spatial Feature
		2.3 Data Representation
		2.4 Machine Learning Methods
	3 Experimental Results
	4 Conclusions
	References
Committee-Based Active Learning to Select Negative Examples for Predicting Protein Functions
	1 Scientific Background
	2 Materials and Methods
		2.1 Preliminaries and Notations
		2.2 Data
		2.3 Algorithm
	3 Results
	4 Conclusion
	References
A Graphical Tool for the Exploration and Visual Analysis of Biomolecular Networks
	1 Scientific Background
	2 Materials and Methods
	3 Results
	4 Conclusion
	References
Improved Predictor-Corrector Algorithm
	1 Introduction
	2 Scientific Background
	3 Materials and Methods
	4 Experimental Results
	5 Conclusion
	References
Identification of Key miRNAs in Regulation of PPI Networks
	1 Introduction
	2 Materials and Methods
		2.1 miRNA-Protein Interactions Network Model
		2.2 Validation Technique
		2.3 Analysis of miRNA-Regulated PPI Network
	3 Results
	4 Conclusion
	References
Recurrent Deep Neural Networks for Nucleosome Classification
	1 Scientific Background
	2 Materials and Methods
		2.1 Dataset
		2.2 The Adopted Network
	3 Results
		3.1 Comparison with a State of the Art Method
		3.2 Informative Content of Nucleosomes with Regard to Organism
	4 Conclusion
	References
Modeling and Simulation Methods in System Biology
Searching for the Source of Difference: A Graphical Model Approach
	1 Introduction
	2 Scientific Background
	3 Materials and Methods
	4 Experimental Results
	5 Conclusions
	References
A New Partially Segment-Wise Coupled Piece-Wise Linear Regression Model for Statistical Network Structure Inference
	1 Introduction
	2 Methods
		2.1 The New Partially Segment-Wise Coupled Model
		2.2 Covariate Set and Data Segmentation Learning
		2.3 Network Structure Learning
	3 Hyperparameter Settings and RJMCMC Simulation Run Lengths
	4 Results
		4.1 Synthetic RAF Protein Pathway Data
		4.2 Saccharomyces Cerevisiae Gene Expression Data
		4.3 Arabidopsis Thaliana Gene Expression Data
	5 Conclusion
	References
Inhibition of Primed Ebola Virus Glycoprotein by Peptide Compound Conjugated to HIV-1 Tat Peptide Through a Virtual Screening Approach
	Abstract
	1 Introduction
	2 Scientific Background
	3 Materials and Methods
	4 Experimental Results
		4.1 Initial Toxicological Screening
		4.2 Pre-docking Preparation
		4.3 Pharmacophore-Based Virtual Screening
		4.4 Molecular Docking Simulation of the Ligand
		4.5 ADME-Tox Prediction
		4.6 Molecular Docking Simulation of the Conjugated Ligand
	5 Conclusions
	Acknowledgments
	References
Pharmacophore Modelling, Virtual Screening, and Molecular Docking Simulations of Natural Product Compounds as Potential Inhibitors of Ebola Virus Nucleoprotein
	Abstract
	1 Introduction
	2 Scientific Background
	3 Materials and Methods
		3.1 Pre-docking Preparation
		3.2 Pharmacophore Modelling and Pharmacophore-Based Virtual Screening
		3.3 Molecular Docking Simulations
	4 Experimental Results
		4.1 Preparation of the Standard Ligand and the Indonesia Natural Product
		4.2 Preparation and Optimization of EBOV NP 3D Structure
		4.3 Pharmacophore Generation, Validation, and Database Screening
		4.4 Pharmacophore-Based Molecular Docking Simulation
		4.5 Binding Mode Interaction Analysis from Docking Simulation
	5 Conclusions
	Acknowledgements
	References
Global Sensitivity Analysis of Constraint-Based Metabolic Models
	1 Scientific Background
	2 Materials and Methods
		2.1 Constraint-Based Modeling
		2.2 Sensitivity Analysis
	3 Results
	4 Conclusion
	References
Efficient and Settings-Free Calibration of Detailed Kinetic Metabolic Models with Enzyme Isoforms Characterization
	1 Introduction
	2 Materials and Methods
		2.1 The SSN Formalism
		2.2 GPU-Powered Parameter Estimation
		2.3 Human Intracellular Metabolic Network
	3 Results and Discussion
	4 Conclusions
	References
Computational Models in Health Informatics and Medicine
Automatic Discrimination of Auditory Stimuli Perceived by the Human Brain
	1 Introduction
	2 Materials and Methods
		2.1 Dataset
		2.2 fMRI Data Acquisition
		2.3 Data Preprocessing
		2.4 Proposed Methodology
	3 Results
	4 Conclusion
	References
Neural Models for Brain Networks Connectivity Analysis
	1 Introduction
	2 Scientific Background
		2.1 fMRI: functional Magnetic Resonance Imaging
		2.2 Data Augmentation Techniques
	3 Materials and Methods
		3.1 The Datasets: HCP
		3.2 Methods: Data Augmentation Techinque
		3.3 Noise
		3.4 Modelling Different Labels
	4 Experimental Results
		4.1 LSTM: Long Short Term Memory
		4.2 Seq2seq: Sequence to Sequence Autoencoders
	5 Conclusions
	References
Exposing and Characterizing Subpopulations of Distinctly Regulated Genes by K-Plane Regression
	1 Introduction
	2 Scientific Background
	3 Materials and Methods
		3.1 Biological Setting and Data
		3.2 K-Plane Regression
	4 Experimental Results
		4.1 Enhanced (Cluster-wise) Fitting
		4.2 Cluster Characterization
	5 Conclusions
	References
Network Propagation-Based Semi-supervised Identification of Genes Associated with Autism Spectrum Disorder
	1 Introduction
	2 Scientific Background
	3 Materials and Methods
		3.1 Semi-supervised Learning Algorithm
		3.2 Gene Sets
		3.3 Biological Network
		3.4 Enrichment Analysis
	4 Results and Discussion
		4.1 Performance Evaluation
		4.2 Selection and Characterization of Top Ranking Genes
	5 Conclusion
	References
Designing and Evaluating Deep Learning Models for Cancer Detection on Gene Expression Data
	1 Introduction
	2 Scientific Background
	3 Materials and Methods
		3.1 Datasets
		3.2 Baselines
		3.3 Ladder Network
		3.4 Ontology-Driven Convolutional Neural Networks
		3.5 Transfer Learning Using a Combined Set of Tumors
	4 Experimental Results
	5 Conclusions
	References
Analysis of Extremely Obese Individuals Using Deep Learning Stacked Autoencoders and Genome-Wide Genetic Data
	Abstract
	1 Introduction
	2 Materials and Methods
		2.1 Study Participants
		2.2 Genetic Analysis
		2.3 Multi-layer Feedforward Artificial Neural Network
		2.4 Autoencoders
	3 Results
		3.1 Hyperparameters Selection
		3.2 Classifier Performance
	4 Discussion
	5 Conclusion
	References
Predicting the Oncogenic Potential of Gene Fusions Using Convolutional Neural Networks
	1 Introduction
	2 Scientific Background
	3 Materials and Methods
		3.1 Fusion Data Retrieval
		3.2 Encoding: From Sequences to Images
		3.3 CNN Architecture and Training Paradigm
	4 Experimental Results
	5 Conclusion
	References
Unravelling Breast and Prostate Common Gene Signatures by Bayesian Network Learning
	1 Scientific Background
	2 Materials and Methods
		2.1 Dimensionality Reduction
		2.2 Bayesian Networks
		2.3 Datasets
		2.4 Finding Common Gene Signatures
	3 Results
	4 Conclusion
	References
Engineering Bio-Interfaces and Rudimentary Cells as a Way to Develop Synthetic Biology
Effect of Epigallocatechin-3-gallate on DMPC Oxidation Revealed by Infrared Spectroscopy
	1 Introduction
	2 Scientific Background
	3 Materials and Methods
	4 Experimental Results
		4.1 Analysis of Vibrational IR Spectra of DMPC and DMPC+EGCG Liposomes
	5 Conclusions
	References
Effect of EGCG on the DNA in Presence of UV Radiation
	1 Introduction
	2 Scientific Background
	3 Materials and Methods
	4 Experimental Results
	5 Conclusions
	References
Non-thermal Atmospheric Pressure Plasmas: Generation, Sources and Applications
	1 Introduction
	2 Scientific Background
		2.1 Generation of Cold Atmospheric Plasmas
		2.2 Cold Atmospheric Plasma Sources
	3 Materials and Methods
		3.1 Plasma Jet Device
		3.2 Cell Line and Cell Culture
		3.3 Plasma Treatments
		3.4 Cell Viability Assays
		3.5 Statistical Analysis
	4 Experimental Results
		4.1 In Vitro CAPS Direct Treatments
		4.2 Effectiveness of Indirect Treatments
	5 Conclusions
	References
Adsorption of Triclosan on Sensors Based on PAH/PAZO Thin-Films: The Effect of pH
	1 Introduction
	2 Scientific Background
	3 Materials and Methods
		3.1 Layer-by-Layer (LbL) Thin Film Preparation
		3.2 Adsorption Experiments
	4 Results
	5 Conclusions
	References
Detection of Triclosan Dioxins After UV Irradiation – A Preliminar Study
	1 Introduction
	2 Scientific Background
	3 Materials and Methods
	4 Experimental Results
		4.1 UV-Visible Spectra
		4.2 Impedance Spectroscopy
		4.3 Principal Component Analysis
	5 Conclusion
	References
Correction to: Computational Intelligence Methods for Bioinformatics and Biostatistics
	Correction to: M. Raposo et al. (Eds.): Computational Intelligence Methods for Bioinformatics and Biostatistics, LNBI 11925, https://doi.org/10.1007/978-3-030-34585-3
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