Biological Data Mining And Its Applications In Healthcare, 1st Edition

  • Published By: World Scientific Publishing Company
  • ISBN-10: 9814551015
  • ISBN-13: 9789814551014
  • DDC: 610.285
  • Grade Level Range: College Freshman - College Senior
  • 436 Pages | eBook
  • Original Copyright 2013 | Published/Released December 2014
  • This publication's content originally published in print form: 2013

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Biologists are stepping up their efforts in understanding the biological processes that underlie disease pathways in the clinical contexts. This has resulted in a flood of biological and clinical data from genomic and protein sequences, DNA microarrays, protein interactions, biomedical images, to disease pathways and electronic health records. To exploit these data for discovering new knowledge that can be translated into clinical applications, there are fundamental data analysis difficulties that have to be overcome. Practical issues such as handling noisy and incomplete data, processing compute-intensive tasks, and integrating various data sources, are new challenges faced by biologists in the post-genome era. This book will cover the fundamentals of state-of-the-art data mining techniques which have been designed to handle such challenging data analysis problems, and demonstrate with real applications how biologists and clinical scientists can employ data mining to enable them to make meaningful observations and discoveries from a wide array of heterogeneous data from molecular biology to pharmaceutical and clinical domains.

Table of Contents

Front Cover.
Half Title Page.
Science, Engineering, and Biology Informatics.
Title Page.
Copyright Page.
1: Sequence Analysis.
2: Mining the Sequence Databases for Homology Detection: Application to Recognition of Functions of Trypanosoma Brucei Brucei Proteins and Drug Targets.
3: Identification of Genes and Their Regulatory Regions Based on Multiple Physical and Structural Properties of a DNA Sequence.
4: Mining Genomic Sequence Data for Related Sequences Using Pairwise Statistical Significance.
5: Biological Network Mining.
6: Indexing for Similarity Queries on Biological Networks.
7: Theory and Method of Completion for a Boolean Regulatory Network Using Observed Data.
8: Mining Frequent Subgraph Patterns for Classifying Biological Data.
9: On the Integration of Prior Knowledge in the Inference of Regulatory Networks.
10: Classification, Trend Analysis and 3D Medical Images.
11: Classification and Its Application to Drug-Target Interaction Prediction.
12: Characterization and Prediction of Human Protein-Protein Interactions.
13: Trend Analysis.
14: Data Acquisition and Preprocessing on Three Dimensional Medical Images.
15: Text Mining and Its Biomedical Applications.
16: Text Mining in Biomedicine and Healthcare.
17: Learning to Rank Biomedical Documents with Only Positive and Unlabeled Examples: A Case Study.
18: Automated Mining of Disease-Specific Protein Interaction Networks Based on Biomedical Literature.