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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
Foundations of Bioinformatics and Systems Biology
The contents in this site includes the following topics : The complete course is comprised of three modules covering Foundations of Bioinformatics, Statistics in Bioinformatics, Systems Biology. These modules holds the following contents : Introduction to bioinformatics, Sequence Alignment and Database Searching, Structural Bioinformatics, Genome Informatics: High Throughput Sequencing Applications and Analytical Methods, Approaches to statistical estimation and testing, Statistical estimation and hypothesis testing, Analyses involving associations, Pearson correlation, t-test, and log odds ratios, Linear regression, Regression models, Introduction to graphical methods for multivariate data analysis, Clustering and principal component analysis, Introduction to systems biology, Epigenome data mining to understand disease predisposition, Computational clinical decision support systems, Application of systems biology to translational medicine.
Author(s): University of Michigan
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 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
Online Lectures on Bioinformatics
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Author(s): NA
NA Pages
Basic bioinformatics concepts, databases and tools
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Author(s): NA
NA Pages
Currently this section contains no detailed description for the page, will update this page soon.
Author(s): NA
NA Pages
Introduction to Bioinformatics (online Lecture Materials)
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Author(s): NA
NA Pages
MATHEMATICS and BIOLOGY THE INTERFACE CHALLENGES and OPPORTUNITIES
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Author(s): NA
NA Pages
Introduction to Programming for Bioinformatics in Python
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Author(s): NA
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