Bioinformatics Algorithms: Techniques and Applications (Hardcover)

Ion Mandoiu, Alexander Zelikovsky



Presents algorithmic techniques for solving problems in bioinformatics, including applications that shed new light on molecular biology

This book introduces algorithmic techniques in bioinformatics, emphasizing their application to solving novel problems in post-genomic molecular biology. Beginning with a thought-provoking discussion on the role of algorithms in twenty-first-century bioinformatics education, Bioinformatics Algorithms covers:

  • General algorithmic techniques, including dynamic programming, graph-theoretical methods, hidden Markov models, the fast Fourier transform, seeding, and approximation algorithms

  • Algorithms and tools for genome and sequence analysis, including formal and approximate models for gene clusters, advanced algorithms for non-overlapping local alignments and genome tilings, multiplex PCR primer set selection, and sequence/network motif finding

  • Microarray design and analysis, including algorithms for microarray physical design, missing value imputation, and meta-analysis of gene expression data

  • Algorithmic issues arising in the analysis of genetic variation across human population, including computational inference of haplotypes from genotype data and disease association search in case/control epidemiologic studies

  • Algorithmic approaches in structural and systems biology, including topological and structural classification in biochemistry, and prediction of protein-protein and domain-domain interactions

Each chapter begins with a self-contained introduction to a computational problem; continues with a brief review of the existing literature on the subject and an in-depth description of recent algorithmic and methodological developments; and concludes with a brief experimental study and a discussion of open research challenges. This clear and approachable presentation makes the book appropriate for researchers, practitioners, and graduate students alike.



- 通用的算法技術,包括動態規劃、圖論方法、隱馬爾可夫模型、快速傅立葉變換、種子匹配和近似算法。
- 用於基因組和序列分析的算法和工具,包括基因簇的正式和近似模型、非重疊局部比對和基因組平鋪、多重PCR引物集選擇以及序列/網絡模式發現。
- 微陣列設計和分析,包括微陣列物理設計的算法、缺失值插補和基因表達數據的元分析。
- 在人類群體中分析基因變異時出現的算法問題,包括從基因型數據中計算推斷單倍型和在病例/對照流行病學研究中搜索疾病相關性的計算方法。
- 在結構生物學和系統生物學中的算法方法,包括生物化學中的拓撲和結構分類,以及蛋白質-蛋白質和域-域相互作用的預測。