Current Topics in Computational Molecular Biology 1st Edition by Tao Jiang, Ying Xu, Michael Q Zhang ISBN 0585437300 9780585437309

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ISBN 10: 0585437300 
ISBN 13: 9780585437309
Author: Tao Jiang, Ying Xu, Michael Q Zhang

Current Topics in Computational Molecular Biology 1st Table of contents:

• Bioinformatics and Sequence Analysis

  • DNA, RNA, and Protein Sequences: Basics and Analysis Techniques
  • Sequence Alignment: Pairwise and Multiple Sequence Alignment
  • Genome Assembly and Annotation
  • Database Search and Retrieval Tools (BLAST, FASTA, etc.)
  • Evolutionary Analysis and Phylogenetic Trees

• Structural Bioinformatics

  • Protein Structure Prediction and Modeling
  • Structural Alignments: Comparing Protein Structures
  • Computational Approaches to Protein Folding
  • Molecular Docking and Drug Discovery
  • Biomolecular Simulations: Applications and Methods

• Genomics and Transcriptomics

  • Genome Sequencing and Mapping Technologies
  • Gene Expression Data Analysis
  • Transcriptome Profiling and Microarrays
  • RNA-Seq and Next-Generation Sequencing
  • Data Integration in Genomics and Transcriptomics

• Systems Biology and Network Modeling

  • Introduction to Systems Biology
  • Biological Networks: Gene Regulatory Networks, Metabolic Pathways, and Protein Interactions
  • Computational Modeling of Cellular Processes
  • Quantitative Analysis of Biological Systems
  • Tools for Systems Biology Analysis (Cytoscape, Pathway Analysis, etc.)

• Computational Approaches to Evolutionary Biology

  • Population Genetics and Evolutionary Models
  • Computational Methods in Phylogeny Inference
  • Evolutionary Genomics: Comparative Genomics Approaches
  • Molecular Evolution and the Neutral Theory
  • Adaptive Evolution and Selection Detection

• Machine Learning in Computational Biology

  • Overview of Machine Learning Techniques in Biology
  • Supervised and Unsupervised Learning in Bioinformatics
  • Applications in Sequence Classification, Feature Selection, and Clustering
  • Neural Networks and Deep Learning in Genomic Data
  • Case Studies of Machine Learning in Molecular Biology

• Computational Approaches to Proteomics

  • Mass Spectrometry Data Analysis
  • Protein Quantification and Identification
  • Protein-Protein Interaction Networks
  • High-Throughput Proteomics Data Interpretation
  • Computational Tools for Proteomics (e.g., MaxQuant, PeptideProphet)

• Metabolomics and Computational Approaches

  • Metabolomics: Data Generation and Analysis
  • Computational Models for Metabolic Pathways
  • Quantification and Visualization of Metabolite Profiles
  • Integration of Genomics, Transcriptomics, and Metabolomics
  • Challenges in Computational Metabolomics

• Computational Approaches in Personalized Medicine

  • Genomic Medicine and Data Integration for Personalized Therapy
  • Computational Tools for Pharmacogenomics
  • Predicting Drug Responses Using Computational Models
  • Applications of Computational Biology in Cancer and Rare Diseases
  • Ethical and Practical Considerations in Personalized Medicine

• Computational Tools and Databases

  • Key Databases in Molecular Biology (GenBank, PDB, UniProt, etc.)
  • Online Tools and Resources for Sequence Analysis
  • Computational Platforms for High-Throughput Data Analysis
  • Cloud Computing in Computational Biology
  • Software for Data Visualization and Interpretation

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