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Bioinformatics by Nakaya Helder
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.
Author(s): Nakaya Helder I., PhD.
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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
Introduction to Bioinformatics Lecture
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.
Author(s): Esa Pitkanen
431 Pages
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
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 175p)
This note explains the relationship between biology, bioinformatics and computer science and shows the computational approaches used in bioinformatics.
Author(s): Marcel Turcotte
175 Pages
Introduction to Bioinformatics (online Lecture Materials)
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Author(s): NA
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Bioinformatics, Sequence and Structural Alignment
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BIOINFORMATICS lecture Notes Online
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Author(s): NA
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