Deep learning neural network approaches for one-dimensional structure prediction from protein sequences

Abstract

CIL). Their technical support was of great assistance. I thank Mr. Sing Yau in particular for assisting in matters such as card access and problems with my network account, amongst many other things.Finally, I thank my family members and friends for their kind words of encouragement and motivation throughout my studies.The findings of the thesis can be extended in several ways. Firstly, for the use of PSSM based encoding, the proposed models may be improved by incorporating other sequence and structure-based properties of interest, such as the amino acid composition, accessible surface areas of residues and so forth. Secondly, in the case of MP structure prediction, the current three or four state residue classification system could be extended to cover more features of interest such as kinks and re-entrant helices, that remain un-explored. Lastly, the heuristics based procedure to obtain the compact model can be extended in a systematic way by applying automated sample selection strategies, such that the best learning model given any training set is automatically selected.