Bioinformatics and Biomedical Engineering

Name: Bioinformatics and Biomedical Engineering | 10th International Work-Conference, IWBBIO 2023, Meloneras, Gran Canaria, Spain, July 12-14, 2023, Proceedings, Part II
Brand: Springer
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10th International Work-Conference, IWBBIO 2023, Meloneras, Gran Canaria, Spain, July 12-14, 2023, Proceedings, Part II

Ignacio Rojas Olga Valenzuela Fernando Rojas Ruiz Luis Javier Herrera Francisco Ortuño(Editor)

Springer (Publisher)

Published on 28. June 2023

XXIX, 508 pages

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978-3-031-34960-7 (ISBN)

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Content

Intro
Preface
Organization
Contents - Part II
Contents - Part I
Feature Selection, Extraction, and Data Mining in Bioinformatics
Agent Based Modeling of Fish Shoal Behavior
1 Introduction
2 Modeling
3 Methods
3.1 Multi agents Based Modeling and Simulation
4 Results and Discussion
References
Entropy Approach of Processing for Fish Acoustic Telemetry Data to Detect Atypical Behavior During Welfare Evaluation
1 Introduction
1.1 Biotelemetry
1.2 Fish Welfare
2 Dataset
3 Methods
4 Results
5 Conclusion
References
Determining HPV Status in Patients with Oropharyngeal Cancer from 3D CT Images Using Radiomics: Effect of Sampling Methods
1 Introduction
2 Material and Methods
2.1 Data Set
2.2 Image Pre-processing
2.3 Feature Extraction
2.4 Data Pre-processing and Resampling
2.5 Feature Selection
2.6 Model Training and Evaluation
3 Results
3.1 Data Pre-processing and Resampling
3.2 Feature Extraction
3.3 Feature Selection
3.4 Performance Evaluation
4 Discussion
5 Conclusion
References
MetaLLM: Residue-Wise Metal Ion Prediction Using Deep Transformer Model
1 Introduction
2 Methodology
2.1 MetaLLM: Residue-Wise Metal Ion Prediction
3 Experiments and Results
3.1 Experimental Details
3.2 Result Analysis
4 Conclusion
References
Genome-Phenome Analysis
Prediction of Functional Effects of Protein Amino Acid Mutations
1 Introduction
2 Methods
2.1 nsSNV Datasets
2.2 Protein Mutation Prediction Methodology: The Holdout- nsSNV Algorithm
2.3 Consensus Holdout Training and Selection
2.4 Extreme Learning Machine
2.5 Random Forests
3 Results
4 Conclusions and Future Directions
References
Optimizing Variant Calling for Human Genome Analysis: A Comprehensive Pipeline Approach
1 Introduction
2 Background
3 Methods
3.1 Reference
3.2 Dataset
3.3 Quality and Control
3.4 Pipeline
3.5 Workflow Management and Reproducibility
3.6 Benchmarking
3.7 Computational Resources
4 Results
4.1 Different Methods Performance
4.2 Computational Time
5 Discussion
6 Conclusion
References
Healthcare and Diseases
Improving Fetal Health Monitoring: A Review of the Latest Developments and Future Directions
1 Introduction
2 Methods
2.1 Study Design
2.2 Search Strategy
2.3 Inclusion Criteria
2.4 Selection of Studies
2.5 Data Analysis
3 Result
4 Discussion
4.1 The Development of Monitoring Devices for Fetal Well-Being
4.2 Algorithm for More Accurate Maternal-Fetal FHR Filtering
4.3 Fetal Well-Being Indicators
4.4 Target Users
5 Conclusion
References
Deep Learning for Parkinson's Disease Severity Stage Prediction Using a New Dataset
1 Introduction
2 Related Work
3 Materials and Methods
3.1 Data Acquisition
3.2 Dataset Construction
3.3 Data Pre-processing
3.4 Proposed LSTM Model
4 Experiments
5 Conclusion
References
Improved Long-Term Forecasting of Emergency Department Arrivals with LSTM-Based Networks
1 Introduction
2 Materials and Methods
2.1 Data Acquisition
2.2 Exploration
2.3 Analysis Techniques
3 Results
4 Conclusion
References
High-Throughput Genomics: Bioinformatic Tools and Medical Applications
Targeted Next Generation Sequencing of a Custom Capture Panel to Target Sequence 112 Cancer Related Genes in Breast Cancer Tumors ERBB2 Positive from Lleida (Spain)
1 Introduction
2 Methods
2.1 Breast Cancer Samples
2.2 Targeted Sequencing
2.3 Data Analysis
3 Results
3.1 Targeted Sequencing
3.2 Gene Signatures
4 Discussion
References
An Accurate Algorithm for Identifying Mutually Exclusive Patterns on Multiple Sets of Genomic Mutations
1 Introduction
2 Method
2.1 Similarity and Mutual Exclusion Measure
2.2 Weighted Probability Search
2.3 Algorithm Steps
3 Discussion
3.1 ME Simulation Principle and Evaluation of Result
3.2 The Result and Analysis of ME Simulation Experiment
3.3 Ms Simulation Data
3.4 Real Datasets
4 Comparative Experiments and Results
5 Conclusion
References
A 20-Year Journey of Tracing the Development of Web Catalogues for Rare Diseases
1 Introduction
2 Related Work
3 Methods
3.1 Implementation
3.2 Relationship Between Data Sources
3.3 Semantic Structure
3.4 Data Readers
4 Results
5 Discussion
6 Conclusions
References
Unsupervised Investigation of Information Captured in Pathway Activity Score in scRNA-Seq Analysis
1 Introduction
2 Materials and Methods
2.1 Data Acquisition and Pre-processing
2.2 Tested Pathway Activity Transformation Algorithms
2.3 Algorithm's Evaluation
3 Results
3.1 Cell Type Separation
3.2 Clustering Accuracy
3.3 Biological Validation
4 Conclusions
References
Meta-analysis of Gene Activity (MAGA) Contributions and Correlation with Gene Expression, Through GAGAM
1 Introduction
2 Background
2.1 Single-Cell Sequencing Technologies
2.2 GAGAM
3 Meta-analysis
3.1 Peaks Information
3.2 Activity-Expression Correlation
3.3 Activity-Expression Coherence
3.4 Conclusions
References
Predicting Papillary Renal Cell Carcinoma Prognosis Using Integrative Analysis of Histopathological Images and Genomic Data
1 Introduction
2 Materials and Methods
2.1 Dataset Overview
2.2 Histopathological Image Processing
2.3 Gene Coexpression Analysis
2.4 Risk Categorization
2.5 Prognosis Prediction Model
3 Results
3.1 Patient Characteristics
3.2 Prognosis-related Image Features and Co-expression Gene Module Selection
3.3 Enrichment Analysis of the Key Gene Modules
3.4 Construction and Evaluation of the Integrative Prognostic Model
4 Discussion and Conclusion
References
Image Visualization and Signal Analysis
Medical X-ray Image Classification Method Based on Convolutional Neural Networks
1 Introduction
2 Image Classification Optimization
3 Segmentaion of X-ray Image
3.1 Conventional Diagnostics and Segmentation Data
3.2 Creating and Training a Segmenting Convolutional Network
4 Image Reduction and Neural Network Training
4.1 Generating of Segmented Images
4.2 Active Regions Computation
4.3 Analysis of the Segmented Image
4.4 Image Reduction
4.5 Training of a Classifying Convolutional Neural Network
5 Experimental Results and Discussion
6 Conclusion
References
Digital Breast Tomosynthesis Reconstruction Techniques in Healthcare Systems: A Review
1 Introduction
2 Tomosynthesis Technology for Breast Imaging
2.1 The Advantages of Tomosynthesis Compared to 2D Mammography
3 Methods of Reconstruction Phase
3.1 The Importance of the Reconstruction Phase in CAD Systems
3.2 Back-Projection Algorithms
3.3 Transform Algorithms
3.4 Algebraic Reconstruction Techniques
3.5 Statistical Reconstruction Techniques
4 Analysis and Discussions
5 Conclusion and Future Works
References
BCAnalyzer: A Semi-automated Tool for the Rapid Quantification of Cell Monolayer from Microscopic Images in Scratch Assay
1 Introduction
2 Materials and Methods
2.1 Sample Images Used for the Algorithm Demonstration
2.2 Image Set for a Systematic Algorithm Performance Validation
3 Software Implementation
4 Results and Discussion
4.1 Prominent Examples of Cell Scratch Assay Analysis for Different Cell Lines
4.2 Systematic Algorithm Validation Using Previously Reported Reference Image Sets
5 Conclusion
References
Color Hippocampus Image Segmentation Using Quantum Inspired Firefly Algorithm and Merging of Channel-Wise Optimums
1 Introduction
2 Literature Survey
3 Proposed Method
4 Experimental Setup
5 Result and Analysis
6 Conclusion
References
Breast Cancer Histologic Grade Identification by Graph Neural Network Embeddings
1 Introduction
2 Related Works
3 Materials and Methods
3.1 The Used Datasets
3.2 Graph Neural Networks
3.3 Graph Construction
3.4 Experimental Setup
4 Results
5 Ablation Study
6 Conclusions
References
A Pilot Study of Neuroaesthetics Based on the Analysis of Electroencephalographic Connectivity Networks in the Visualization of Different Dance Choreography Styles
1 Introduction
2 Methods of Data Recording and Analysis
2.1 Experimental Paradigm
2.2 Processing and Analysis Methods
3 Results
4 Discussion
5 Conclusions
References
Machine Learning in Bioinformatics and Biomedicine
Ethical Dilemmas, Mental Health, Artificial Intelligence, and LLM-Based Chatbots
1 Introduction
2 Methodology
3 Data Extraction and Analysis Procedure
4 Results
5 Ethical Analysis
6 Conclusions
7 LLM Chatbots, Quality of Care, Responsible Research, and Development in Mental Health
8 Access, Exclusion, and User Dependence on Chatbots
9 Responsibility and Human Supervision of Chatbots
10 Regulation and Chatbot Usage Policies
11 Limitations of the Present Review
References
Cyclical Learning Rates (CLR'S) for Improving Training Accuracies and Lowering Computational Cost
1 Introduction
2 Experimental Results and Analysis
2.1 Data Collection, Preprocessing, Model Architecture, and Learning Rates
3 Discussion and Future Scope
References
Relation Predictions in Comorbid Disease Centric Knowledge Graph Using Heterogeneous GNN Models
1 Introduction
2 Related Work
3 Dataset and Method
3.1 Brief Description of the Biological KG and Its Associated Datasets
3.2 BioBERT Embedding for Entity Concepts and Data Pre-processing
3.3 Brief Overview of GNN
4 Experiment
5 Results and Discussion
5.1 Novel Link Predictions
6 Conclusion
References
Inter-helical Residue Contact Prediction in -Helical Transmembrane Proteins Using Structural Features
1 Introduction
2 Materials and Methods
2.1 Dataset
2.2 Feature Extraction
2.3 Classification Experiment
2.4 Performance Metrics
2.5 Noise Injection
3 Results and Discussion
3.1 Classification
3.2 Robustness
4 Conclusion
References
Degree-Normalization Improves Random-Walk-Based Embedding Accuracy in PPI Graphs
1 Introduction
2 Background
3 Methods: Degree Normalization (DN)
4 Datasets
5 Experimental Results
6 Conclusion
A Additional Results
References
Medical Image Processing
Role of Parallel Processing in Brain Magnetic Resonance Imaging
1 Introduction
2 Methodology
3 Results and Discussion
4 Conclusions
References
Deep Learning Systems for the Classification of Cardiac Pathologies Using ECG Signals
1 Introduction
2 Electrocardiogram
3 Arrhythmias
4 Discrete Wavelet Transform (DWT)
4.1 Scale Functions and Wavelet Functions
4.2 Scale Coefficients (cj, k) and Wavelet Coefficients (dj, k)
4.3 Scalogram Based on Discrete Wavelet Transform
5 Data Set. Physionet
5.1 ECG an Scalogram Examples from the Database
6 Different Deep Learning Models for ECG Classification
7 Results
8 Conclusion
References
Transparent Machine Learning Algorithms for Explainable AI on Motor fMRI Data
1 Introduction
2 Methods
2.1 Dataset
2.2 Data Processing
2.3 Machine Learning Models and Algorithms
2.4 Artificial Neural Network Models
2.5 Evaluation Metrics
2.6 Feature Importance
3 Results
3.1 FCANN
3.2 Feature Importance
4 Discussion
References
A Guide and Mini-Review on the Performance Evaluation Metrics in Binary Segmentation of Magnetic Resonance Images
1 Introduction
2 Prerequisites of Binary Segmentation
3 Pixel-Wise Metrics
3.1 True-Positives and True-Negatives
3.2 False-Positives and False-Negatives
3.3 False-Positive and False-Negative Rates
3.4 Sensitivity and Specificity
3.5 Precision, Accuracy
4 Area-Wise Metrics
4.1 Jaccard Index (IoU)
4.2 Dice Score Coefficient (F1 Score)
4.3 Interconvertibility
5 Discussion and Conclusion
References
Next Generation Sequencing and Sequence Analysis
The Pathogenetic Significance of miR-143 in Atherosclerosis Development
1 Introduction
2 Materials and Methods
2.1 Dataset Selection
2.2 Differentially Expressed Genes Calculation
2.3 PPI Extended Network Reconstruction
2.4 Clusters Detection and Hub Genes Identification in Them
2.5 MicroRNA-Hub Genes Network Reconstruction
3 Results
4 Discussion
5 Conclusion
References
Comparison of VCFs Generated from Different Software in the Evaluation of Variants in Genes Responsible for Rare Thrombophilic Conditions
1 Introduction
2 Methods
3 Analysis of Sequencing Data
3.1 Torrent Suite Software
3.2 Ion Reporter
3.3 NextGene
3.4 VCF File Annotation
4 Results
5 Conclusion
References
Uterine Cervix and Corpus Cancers Characterization Through Gene Expression Analysis Using the KnowSeq Tool
1 Introduction and Background
2 Materials and Methods
2.1 TCGA Database
2.2 KnowSeq
2.3 Classification of Healthy, Cervical Cancer and Uterine Corpus Cancer Samples
2.4 Classification of Cervical Adenocarcinoma and Cervical Squamous Cell Carcinoma Samples
3 Results
3.1 Classification of Healthy, Cervical Cancer and Uterine Corpus Cancer Samples
3.2 Classification of Cervical Adenocarcinoma and Cervical Squamous Cell Carcinoma Samples Using the KnowSeq kNN Algorithm
4 Conclusions
References
Sensor-Based Ambient Assisted Living Systems and Medical Applications
Smart Wearables Data Collection and Analysis for Medical Applications: A Preliminary Approach for Functional Reach Test
1 Introduction
2 Methods
2.1 Description of the Test
2.2 Proposed Approach
2.3 Proposed Mobile Application
3 Expected Results
4 Discussion and Conclusions
References
Radar Sensing in Healthcare: Challenges and Achievements in Human Activity Classification & Vital Signs Monitoring
1 Introduction
2 Principles of Radar Signal Processing in Healthcare
3 Representative Results and Open Challenges
4 Conclusions
References
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Bioinformatics and Biomedical Engineering

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