The aim of this course is twofold: to provide an overview of the most common statistical methods for molecular proteomics data analysis, and to provide the necessary information for solving the complex biological problems and achieving the satisfactory score of sustainable development goals (SDGs) index from the health sectors.

Introduction: Cell Structure and Function, Cell components. Chromosome, Chromosome structure and organization.  DNA, RNA, Gene and Central dogma and bioinformatics. Introduction to Bioinformatics. Importance/scope/Applications of Bioinformatics.  DNA sequencing. Shotgun sequencing, Long repeats, r-scane.

Protein Sequencing and Amino Acids: Amino acids and Amino Acids structure and functions. Codons.  Metabolic and Biochemical pathway analysis e.g. E.coli. pathways. Sequence alignment, Overview of methods of sequence alignment. Dynamic programming algorithm for sequence alignment, Multiple Sequence alignments. statistical methods for aiding alignment.

Protein Databases: Overview of the use and maintenance of different databases in common use in biology. Databases: GenBank, DDBJ, EMBL  NCBI, EFI,  UniGene, UniProt, Swiss-Prot, PDB. BLAST and FASTA analysis.

Phylogenetic Analysis of Protein Sequence:  Motivation and background on phylogenetics, Distance and clustering approach, Likelihood methods,  Parsimony,  RNA-based phylogenetics methods, Phylogenetic Tree Estimation.

Protein Classification, Structure and Prediction: Protein Structure Prediction: Methods for predicting the secondary and tertiary structure of proteins. Techniques: neural networks, SVMs, genetic algorithms and stochastic global optimization.

Medical Informatics: Introduction to Medical Informatics. Perspectives and goals of Medical Informatics. History, Taxonomy and standards of Medical Informatics, Organization of Medicine and Health Information, Paper-based Medical Report and Electronic Medical Report (EMR), Pervasive Healthcare.

Drug Discovery Informatics: Metabolome and Metabolomics. Systems biology, Approaches to drug and vaccine design using bioinformatics tools, Molecular docking using Autodock and/or other computer aided programs.

Network Analysis and Disease Prediction: Scope and applications of Network analysis in medical informatics. Bayesian Network (BN) Analysis and application, Artificial Neural Network (ANN) Analysis and application. Other relevant network. Disease Surveillance, Disease prediction models. Survival analysis. Risk classifications. CAPRA and D’Amico risk classifications. Nomogram development for disease prediction.

1)

Shortliffe E.Hand  J.J. Cimino (2006).  Biomedical Informatics: Computer Applications in Health Care and Biomedicine (Health Informatics). Springer-Verlag. .

2)

Husmeier, D., Dybowski, R., Roberts, S. (2005). Probabilistic Modeling in Bioinformatics and Medical Informatics, 2nd edition, Publisher: Springer.

3)

Warren J. Ewens, Gregory R. Grant (2004): Statistical Methods in Bioinformatics: An Introduction (Statistics for Biology and Health).  2nd edition.  Publisher: Springer.

4)

Carey, V. J., Huber, W., Irizarry, R. A., & Dudoit, S. (2005). Bioinformatics and computational biology solutions using R and Bioconductor (Vol. 746718470). R. Gentleman (Ed.). New York: Springer.

5)

Pevsner, J. (2009). Bioinformatics and functional genomics. John Wiley & Sons.

6)

Carey, V. J., Huber, W., Irizarry, R. A., & Dudoit, S. (2005). Bioinformatics and computational biology solutions using R and Bioconductor (Vol. 746718470). R. Gentleman (Ed.). New York: Springer.