Computational and Advanced Statistical Methods in Biosciences (2208)

Doc.dr.sc. Ivana Škrlec

J. J. Strossmayer University of Osijek, Department of Mathematics

Doc.dr.sc. Olga Jovanović Glavas

Introduce students to modern computational and statistical methods in biosciences and their application to solve specific problems

1. Motivational problems in molecular biosciences
2. Statistical methods
a. Medline/PubMED databases: retrieval and descriptive statistics
b. Regression models:
(i) A simple model of logistical regression
(ii) Complex model of logistical regression
(iii) Example: Impact of TGF-b1 genetic polymorphism on kidney dysfunction after liver transplantation in children

3. The problem of selecting properties Feature selection problem)
and. Use statistical tests to select properties
b. Methods for selecting subsets of properties Feature Subset selection)
(i) Sequential backward selection Sequential Backward Selection)
(ii) Sequential pre-selection Sequential Forward Selection)
c. Methods of combinatorial optimization: k-Feature Set problem
d. Example: Detection of markers for prostate cancer (data: Kent Ridge Biomedical Data Set Repository, NCBI Data Set Record)

4. Computational methods
and. Graph modeling in genetics
b. DNA sequencing
c. Problem of the shortest supernition
d. Methods of sequence by hybridization: the problem of Hamilton and Euler's path
e. Protein and peptide sequencing
f. Identification of proteins by searching the database
g. Example: Database work: GenBank, RefSeq, TPA, SwissProt, PIR, PRF and PDB

1. Use statistical and computational methods in biosciences.
2. Choose adequate computer solutions for the retrieval and analysis of data in biosciences.
3. Interpret the results of the data analysis.
4. Solve specific scientific problems in biosciences using statistical and computational methods.

• Regular monitoring of classes
• Participation in teaching
• Preparation of seminar work

1. Sergios Theodoridis: Pattern Recognition, Fourth Edition. Academic Press, 2008.
2. Walter T. Ambrosius: Topics in Biostatistics (Methods in Molecular Biology), Humana Press, 2007.
3. Neil C. Jones & Pavel A. Pevzner: An Introduction to Bioinformatics Algorithms, MIT Press, 2004.

1. Arthur M. Lesk: Introduction to Bioinformatics, Oxford University Press, 2005.
2. Jonathan Pevsner: Bioinformatics and Functional Genomics (second edition), Wiley – Blackwell, 2009.
3. Oleg Okun: Feature Selection and Ensemble Methods for Bioinformatics: Algorithmic Classification and Implementations, IGI Global, 2011.
4. Peter Flach: Machine Learning: The Art and Science of Algorithms That Make Sense of Dana, Cambridge University Press, 2012.
5. Christopher M. Bishop: Pattern Recognition and Machine Learning, Springer, 2006.

The success of the course will be evaluated annually by the joint expert committee of the Ruđer Boskovic Institute, the University of Dubrovnik and the University of Josip Juraj Strossmayer in Osijek based on exam success and surveys.