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Computational Biology Biostatistics
The topics of notes in this site are as follows : Introduction to Bioinformatics, Sequence Alignment, Probabilistic Sequence Models , Gene Expression Analysis, Protein Structure Prediction.
Author(s): Burr Settles
NA Pages
A Bioinformatics Study of Human Transcriptional Regulation
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 Introduction by Mark Gerstein
This note covers the following topics: Molecular Biology, Molecular Biology Information - DNA, Protein Sequence, Macromolecular Structure and Protein Structure Details, Gene Expression Datasets, New Paradigm for Scientific Computing, General Types of Informatics in Bioinformatics, Genome Sequence, Protein Sequence, Major Application: Designing Drugs, Finding Homologues, Genome Characterization.
Author(s): Mark Gerstein
NA Pages
Bioinformatics Sequence Analysis and Phylogenetics Lecture Notes (PDF 190P)
This book covers the following topics: biological basics needed in bioinformatics, Pairwise Alignment, Multiple Alignment, Phylogenetics, DNA, RNA, Transcription, Introns, Exons, and Splicing, Amino Acids.
Author(s): Sepp Hochreiter
180 Pages
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
Algorithms in Bioinformatics (PDF 82p)
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Author(s): NA
NA Pages
Learn about Bioinformatics and Computational Tools for Biology
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Author(s): NA
NA Pages
Algorithms in Bioinformatics (PDF 87P)
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NA Pages
Introduction to Programming for Bioinformatics in Python
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NA Pages
BIOINFORMATICS lecture Notes Online
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NA Pages
