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.
This note
covers the following topics: what is bioinformatics, Path to the bioinformatics, Scope of computational biology, The Central Dogma of
Molecular Biology, Genomics, Proteomics, Killer application, Cells, Signaling
Pathways Control gene activity, DNA the code of life and genetic information
chromosomes.
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 explains the
following topics: What is bioinformatics, Molecular biology primer, Biological
words, Sequence assembly, Sequence alignment, Fast sequence alignment using
FASTA and BLAST, Genome rearrangements, Motif finding, Phylogenetic trees and
Gene expression analysis.
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.
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.
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.