Bio-Informatics

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BIO-INFORMATICS 3174204
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Code: 3174204 Credits: 3 Semester 7 CS-AI/ML

Bio-Informatics

Get the full GTU study material, English & Gujarati textbook PDFs, syllabus curriculum, and solved previous year question papers.

Bio-Informatics Syllabus — Unit-wise Content

The official GTU curriculum for Bio-Informatics (subject code 3174204) as prescribed for CS-AI/ML · Semester 7 divides the course into 5 units, covering 42 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.

Download the official syllabus PDF (3174204)

Unit 1 Bio-Informatics

5 lecture hours

  • Need for Bioinformatics technologies
  • Why should we use bioinformatics
  • Application of bioinformatics
  • Overview of gnome

Unit 2 Database for Bio-Informatics

6 lecture hours

  • What is database? Sequence database
  • structural database
  • molecular visualization
  • specialized sequence database
  • Gnome mapping database
  • biological culture and stock collection database
  • Enzymes and metabolic pathway databases
  • information retrieval from biological database

Unit 3 DATAWAREHOUSING AND DATAMINING IN BIO-

7 lecture hours

  • INFORMATICS Bioinformatics data
  • Data warehousing architecture
  • data quality
  • Biomedical data analysis
  • DNA data analysis
  • Protein data analysis
  • Neural network architecture and applications in bioinformatics

Unit 4 MODELING FOR BIO-INFORMATICS

9 lecture hours

  • Hidden markov modeling for biological data analysis
  • Sequence identification
  • Sequence classification
  • multiple alignment generation
  • Comparative modeling
  • Protein modeling
  • genomic modeling
  • Probabilistic modeling
  • Bayesian networks
  • Boolean networks
  • Molecular modeling
  • Subject Code : 3174204 Subject Name : Bio-informatics https://syllabus.gtu.ac.in/Page 2

Unit 5 PATTERN MATCHING AND VISUALIZATION

9 lecture hours

  • Gene regulation
  • motif recognition
  • motif detection
  • strategies for motif detection
  • Fractal analysis
  • DNA walk models
  • one dimension
  • two dimension
  • higher dimension
  • Game representation of Biological sequences, DNA, Protein
  • Amino acid sequences

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