Computational intelligence approaches for bioinformatics problems / Mahir Mohammed Sharif Adam ; Supervised Aboul Ella Otifey Hassanien , Hesham Ahmed Hefny

Thesis (Ph.D.) - Cairo University - Institute of Statistical Studies and Research- Department of Computer and Information Science

Recently, Bioinformatics problems have come to represent a signi{uFB01}cant presence in the contem- porary scienti{uFB01}c research. Bioinformatics uses some techniques including statistics, mathematics, computer science, computational intelligence (CI)... etc to solve complex problems such as gene- gene interactions, protein-protein interaction, diseases discovery, and proteins/enzymes prediction and classi{uFB01}cation. Enzyme/protein classi{uFB01}cation represents a critical problem due to the vital role of enzyme in the life of organisms; its working as an accelerator (catalyst) of bio-reactions that happen to accomplish the vital processes such as metabolism, respiration, reproduction, etc. The number of enzymes discovered is very big, which means that the operation of predicting, classify- ing and determining their functions are extremely dif{uFB01}cult. CI is a branch of arti{uFB01}cial intelligence, where well-crafted algorithms are being developed to solve complex, computationally expensive problems that are believed to require intelligence. CI is one of the most promising tools today to attack the hard problems in Bioinformatics and human genet- ics. Enzyme classi{uFB01}cation (EC) represents one of the most important Bioinformatics issues, which aims to classify the proteins into families or other biological signi{uFB01}cant groups. This trend allows to classify known proteins, or new proteins to predict their families (or classes) it allows the struc- tural and functional properties of proteins to be inferred, giving a deeper understanding of how proteins function in making up the living cell. Also, EC is paving the way for the drug design process

Issued also as CD