Saiyed Umer – författare

This text emphasizes the importance of artificial intelligence techniques in the field of biological computation. It also discusses fundamental principles that can be applied beyond bio-inspired computing.It comprehensively covers important topics including data integration, data mining, machine learning, genetic algorithms, evolutionary computation, evolved neural networks, nature-inspired algorithms, and protein structure alignment. The text covers the application of evolutionary computations for fractal visualization of sequence data, artificial intelligence, and automatic image interpretation in modern biological systems.The text is primarily written for graduate students and academic researchers in areas of electrical engineering, electronics engineering, computer engineering, and computational biology.This book:• Covers algorithms in the fields of artificial intelligence, and machine learning useful in biological data analysis.• Discusses comprehensively artificial intelligence and automatic image interpretation in modern biological systems.• Presents the application of evolutionary computations for fractal visualization of sequence data.• Explores the use of genetic algorithms for pair-wise and multiple sequence alignments.• Examines the roles of efficient computational techniques in biology.

This text emphasizes the importance of artificial intelligence techniques in the field of biological computation. It also discusses fundamental principles that can be applied beyond bio-inspired computing.It comprehensively covers important topics including data integration, data mining, machine learning, genetic algorithms, evolutionary computation, evolved neural networks, nature-inspired algorithms, and protein structure alignment. The text covers the application of evolutionary computations for fractal visualization of sequence data, artificial intelligence, and automatic image interpretation in modern biological systems.The text is primarily written for graduate students and academic researchers in areas of electrical engineering, electronics engineering, computer engineering, and computational biology.This book:• Covers algorithms in the fields of artificial intelligence, and machine learning useful in biological data analysis.• Discusses comprehensively artificial intelligence and automatic image interpretation in modern biological systems.• Presents the application of evolutionary computations for fractal visualization of sequence data.• Explores the use of genetic algorithms for pair-wise and multiple sequence alignments.• Examines the roles of efficient computational techniques in biology.

This book provides an overview of basic and advanced computational techniques for analyzing and understanding protein, RNA, and DNA sequences. It covers effective computing techniques for DNA and protein classifications, evolutionary and sequence information analysis, evolutionary algorithms, and ensemble algorithms. Furthermore, the book reviews the role of machine learning techniques, artificial intelligence, ensemble learning, and sequence-based features in predicting post-translational modifications in proteins, DNA methylation, and mRNA methylation, along with their functional implications. The book also discusses the prediction of protein–protein and protein–DNA interactions, protein structure, and function using computational methods. It also presents techniques for quantitative analysis of protein–DNA interactions and protein methylation and their involvement in gene regulation. Additionally, the use of nature-inspired algorithms to gain insights into gene regulatory mechanisms and metabolic pathways in human diseases is explored. This book acts as a useful reference for bioinformaticians and computational biologists working in the fields of molecular biology, genomics, and bioinformatics.Key Features:Reviews machine learning techniques for DNA sequence classification and protein structure predictionDiscusses genetic algorithms for analyzing multiple sequence alignments and predicting protein–protein interaction sitesExplores computational methods for quantitative analysis of protein–DNA interactionsExamine the role of nature-inspired algorithms in understanding the gene regulation and metabolic pathwaysCovers evolutionary algorithms and sequence-based features in predicting post-translational modifications

This reference text analyzes the integrated model of artificial intelligence, deep learning, and cloud computing for various bioinformatic problems including gene signature discovery, rare cell detection, disease classification, and rare disease detection. It discusses biomedical image processing, and deep learning integrated tools for stage detection of critical diseases like cancer.This book:Presents a comparative study of gradient boosted classifier and deep learning classifier for disease classification of multi-omics sequencing data. Showcases biomarker discovery and differential expression analysis in Single-cell RNA sequencing data through the integrated model of data imputation, feature selection, and cell clustering. Explains artificial intelligence, machine learning, and integrated models for DNA methylation and gene expression data analysis, and disease classification. Covers the fundamentals of artificial intelligence, machine learning, deep learning, and cloud computing tools in bioinformatics.Explores the rare cell detection, and transcriptomic analysis in single-cell RNA sequencing. It is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communications engineering, computer science and engineering, and biomedical engineering.