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Introduction to Intelligent Systems in Bioinformatics Course

Join Dr. Karen Page at UCL for an engaging course on Intelligent Systems in Bioinformatics. This course explores the intersection of computer science and biology, covering essential topics such as molecular biology, genomics, microarray technology, and computational biology methods. Lectures are held on Mondays and Thursdays, combining theoretical knowledge with practical applications. Coursework and a written exam will assess your understanding of bioinformatics techniques, with a focus on data analysis and pattern extraction from biological data. For enrollment and inquiries, contact Dr. Page via email.

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Introduction to Intelligent Systems in Bioinformatics Course

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  1. Intelligent systems in bioinformatics Introduction to the course

  2. Contact details Dr. Karen Page Computer Science - Room G50a Tel: 020 7679 3683 (internal: 33683) Email: k.page@cs.ucl.ac.uk http://www.cs.ucl.ac.uk/staff/K.Page

  3. Lecture format • Monday and Thursday afternoons (2-5pm) – Pearson Lecture Theatre (Mon.) & Rm 229 (Thurs.) • We will take one or two 10/15-minute breaks, so typically the lecture might be split: 50-10-50-10-50 or 80-15-75

  4. Coursework & Homework • Coursework: • 1 piece • 15% of total mark • towards end of course • Homework: • Each week (doesn’t contribute to course grade) • Attach cover sheet (http://www.cs.ucl.ac.uk/teaching/cwsheet.htm) • Give to JJ Giwa (G07) by 12pm on due date

  5. Exam • Written exam • 15th March • 85% of total mark

  6. Newsgroups/ Mailing list • All communication concerning this course will be done via the email list. • Please join by sending an email with Subject: join • to gi10-request@cs.ucl.ac.uk or local.cs.gi10 or 4c58-request@cs.ucl.ac.uk or local.cs.4c58

  7. Useful Books • Alberts et al- Molecular Biology of the Cell • Stryer- Biochemistry • Baldi and Brunak – Bioinformatics – a machine learning approach • Durbin, Eddy, Krogh and Mitchison – Biological sequence analysis • Kanehisa - Post genome informatics • Lesk- Introduction to bioinformatics • Orengo, Jones and Thornton - Bioinformatics

  8. The Course- motivation for biological material • Modern molecular biology and especially genomics has led to vast quantities of data: DNA/ protein sequence, gene expression. • This mainly consists of vast strings/ matrices of letters/ numbers, which in their raw form are not very interesting. • What’s needed now is synthesis of data and mining of data for patterns. • Intelligent systems techniques are very good for extracting useful patterns.

  9. Motivation • In order to extract useful information, it is necessary to understand biological principles involved. • In this course we will introduce some basic molecular biology/ genomics and look at ways in which computers can be used to analyse it (bioinformatics), with a particular focus on intelligent systems techniques.

  10. Course material content • I will give five three-hour blocks of lectures towards the start of the course. • Prof. David Jones will give the rest of the lectures. • Will now give a brief summary of the content of my lectures and a very brief one of his.

  11. Content • Block 1: Biology • Introduction to course • Basic molecular biology • Cells, DNA, RNA, proteins, central dogma • Sequencing • Block 2: Genomics • History of genomics • Introduction to bioinformatics • Gene prediction

  12. Content • Block 3: Microarrays • Microarray technology • Statistics • Analysis of microarray data • Block 5: Guest lectures (Systems biology and Gene networks) • Intelligent systems and software for systems biology (Dr. Peter Saffrey, UCL) • Bayesian networks (Dr. Lorenz Wernisch, Birkbeck) • Reverse engineering of gene networks from microarray data (Dr. Lorenz Wernisch)

  13. Content • Block 8: Gene networks and Computational biology • Continuation of analysis of microarray data • Signalling pathways • Reverse engineering of networks from microarray data • Evolutionary games and evolutionary algorithms (if time)

  14. Content • Below is a rough outline of what Prof. Jones will cover: Blocks 4,6,7,9 & 10: • Gene finding and basic sequence comparisons • Sequence comparisons; Hidden Markov Models; proteins • Databases; agent technology • Protein structure; structure classification; structure prediction • Protein structure prediction; drug discovery

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