A Bioinformatics Study of Human Transcriptional Regulation
A Bioinformatics Study of Human Transcriptional Regulation
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.
This page covers the
following topics related to Bioinformatics : Text Mining Gene Selection to
Understand Pathological Phenotype Using Biological Big Data, Single-Cell RNA
Sequencing Procedures and Data Analysis, Computational Methods for Detecting
Large-Scale Structural Rearrangements in Chromosomes, Machine Learning
Approaches for Biomarker Discovery Using Gene Expression Data, Bayesian
Inference of Gene Expression, Comprehensive Evaluation of Error-Correction
Methodologies for Genome Sequencing Data, Plant Transcriptome Assembly: Review
and Benchmarking,WeMine Aligned Pattern Clustering System for Biosequence
Pattern Analysis, Rational Design of Profile Hidden Markov Models for Viral
Classification and Discovery, Pattern Discovery and Disentanglement for Aligned
Pattern Cluster Analysis and Protein Binding Complexes Detection.
The topics of notes in this site are as
follows : Introduction to Bioinformatics, Sequence Alignment, Probabilistic
Sequence Models , Gene Expression Analysis, Protein Structure Prediction.
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.
This site hold 9 lectures on
various topics related to Micorarray Methodology and Analysis such as
Introduction to microarray technology, Image Processing for Pedestrians,
Differential Expression, Microarray Normalization, Clustering and
Classification, etc.
Author(s): M. Saleet Jafri, Program in Bioinformatics and
Computational Biology, George Mason University
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.
This note explains the
following topics: Modern approaches in Bioanalysis and Bioassays, Spectroscopic
techniques: UV-Visible spectroscopy, Fluorescence spectroscopy, IR spectroscopy,
CD spectroscopy, and Mass spectroscopy, Light Microscopy; Fluorescence
microscopy, Atomic force microscope, Electron microscope, Scanning electron
microscopy, Transmission Electron microsope, Application of microscope in
analyzing biological samples.