Data Mining and Machine Learning Techniques For Bioinformatics detailed syllabus for Biotechnology (BioTech) for 2021 regulation curriculum has been taken from the Anna Universities official website and presented for the BioTech students. For course code, course name, number of credits for a course and other scheme related information, do visit full semester subjects post given below.
For Biotechnology 7th Sem scheme and its subjects, do visit BioTech 7th Sem 2021 regulation scheme. For Professional Elective-VI scheme and its subjects refer to BioTech Professional Elective-VI syllabus scheme. The detailed syllabus of data mining and machine learning techniques for bioinformatics is as follows.
Course Objectives:
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Unit I
OVERVIEW OF MACHINE LEARNING TECHNIQUES 9
Supervised and unsupervised techniques. Empirical Risk Minimization, Structural Risk Minimization; Measuring the accuracy of learned hypotheses. Comparing learning algorithms: cross-validation, learning curves, and statistical hypothesis testing.
Unit II
MACHINE LEARNING TECHNIQUES 9
Classification: Decision tree, Bayesian, Rule based classification, ANN, SVM, HMM; Case based reasoning and Applications in Bioinformatics. Clustering: Partition Methods, Hierarchical methods, Density based methods, Grid based clustering, Model based clustering, clustering of high dimensional data, constraints based clustering, Analysis of MD trajectories, Protein Array data Analysis.
Unit III
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Unit IV
DATA PREPROCSSING AND VISUALIZATION 9
Overview of data preprocessing, Data cleaning, Data integration, Data reduction, Data transformation and discretization, Visualization- Visualizing a single attributes, Visualizing pair of attributes, Visualizing several attributes, Visualizing results of machine learning.
Unit V
APPLICATIONS OF DATA MINING 9
Application of Data Mining in Biodata analysis: DNA/protein sequence Analysis, Genome analysis, Protein Structure Analysis, Pathway analysis, microarray data analysis, annotation, gene ontology, gene mapping. Biological data mining tools: Entrez, Blast, sequence retrieval system (SRS).
Course Outcomes:
Upon completion of this course,
Students will be able to
- Know the basic notions and terminology used in Machine learning and Data mining.
- Understand fundamental principles of modern data analysis.
- Understand the applications of Machine learning and Data mining in biological data processing and visualization.
Text Books:
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Reference Books:
- Data Mining: Concepts and Techniques by Jiawei Han and MichelineKamber, 2000
- Data Mining Techniques, A. K. Pujari, UniversityPress, Hyderabad, 2006
- Data mining in bioinformatics by Wang et al, Springer-Verlag, 2005
For detailed syllabus of all the other subjects of Biotechnology 7th Sem, visit BioTech 7th Sem subject syllabuses for 2021 regulation.
For all Biotechnology results, visit Anna University BioTech all semester results direct link.