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Bioinformatics II Theoretical Bioinformatics and Machine Learning (PDF 394)

This book covers the following topics: Machine Learning in Bioinformatics, Theoretical Background of Machine Learning, Support Vector Machines, Error Minimization and Model Selection, Neural Networks, Bayes Techniques, Feature Selection, Hidden Markov Models.

Author(s): Sepp Hochreiter

400 Pages

This PDF thesis A Bioinformatics Study of Human Transcriptional Regulation covers the following topics related to Bioinformatics : Biological background, DNA and chromosomes, Transcription, Transcriptional regulation, Medical aspects of transcriptional regulation, High-throughput technologies, DNA microarrays, Massively parallel sequencing, Biological knowledge sources, Annotations, Experimental data, Aims, Methods, Computational methods, Statistics Randomization, Local search, Analysis of high-throughput data, Experiment design, Expression analysis, ChIP-chip data analysis, ChIP-seq data analysis, Data storage and management, Results and discussion, Summary of results, Computational results, Biological results, Medical results, Additional results, Discussion.

Author(s): Adam Ameur

54 Pages

Bioinformatics methods and applications for functional analysis of mass spectrometry based proteomics data

This book is intended to serve both as a textbook for short bioinformatics courses and as a base for a self teaching endeavor. It is divided in two parts: A. Bioinformatics Techniques and B. Case Studies. Each chapter of the first part addresses a specific problem in bioinformatics and consists of a theoretical part and of a detailed tutorial with practical applications of that theory using software freely available on the Internet.

Author(s): Chanchal Kumar

197 Pages

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Bioinformatics II Theoretical Bioinformatics and Machine Learning (PDF 394)

Bioinformatics II Theoretical Bioinformatics and Machine Learning (PDF 394)

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