Machine learning methods for prediction of disulphide bonding states of cysteine residues in proteins

The goal of this thesis work is to develop a computational method based on machine learning techniques for predicting disulfide-bonding states of cysteine residues in proteins, which is a sub-problem of a bigger and yet unsolved problem of protein structure prediction. Improvement in the prediction of disulfide bonding states of cysteine residues will help in putting a constraint in the three dimensional (3D) space of the respective protein structure, and thus will eventually help in the prediction of 3D structure of proteins. Results of this work will have direct implications in site-directed mutational studies of proteins, proteins engineering and the problem of protein folding. We have used a combination of Artificial Neural Network (ANN) and Hidden Markov Model (HMM), the so-called Hidden Neural Network (HNN) as a machine learning technique to develop our prediction method. By using different global and local features of proteins (specifically profiles, parity of cysteine residues, average cysteine conservation, correlated mutation, sub-cellular localization, and signal peptide) as inputs and considering Eukaryotes and Prokaryotes separately we have reached to a remarkable accuracy of 94% on cysteine basis for both Eukaryotic and Prokaryotic datasets, and an accuracy of 90% and 93% on protein basis for Eukaryotic dataset and Prokaryotic dataset respectively. These accuracies are best so far ever reached by any existing prediction methods, and thus our prediction method has outperformed all the previously developed approaches and therefore is more reliable. Most interesting part of this thesis work is the differences in the prediction performances of Eukaryotes and Prokaryotes at the basic level of input coding when ‘profile’ information was given as input to our prediction method. And one of the reasons for this we discover is the difference in the amino acid composition of the local environment of bonded and free cysteine residues in Eukaryotes and Prokaryotes. Eukaryotic bonded cysteine examples have a ‘symmetric-cysteine-rich’ environment, where as Prokaryotic bonded examples lack it.

Experimenting with the role of performance of hygiene and motivators on customer satisfaction: studying the temporal effect

Marketers continuously attempt to identify important attributes and innovate in order to understand how attribute performance could lead to customer satisfaction in the short term and in the long term. Understanding the impact of customer satisfaction may offer a competitive edge to companies. Researchers are discussing the importance of performance attributes in leading to satisfaction; however, there is no clear understanding of whether an attribute that leads to satisfaction at one time (e.g., short run) can cause it also in the long run, without excluding the possibility that it could lead to dissatisfaction and no satisfaction. The present research tries to understand anomalies related to asymmetric attribute performance and satisfaction over time with the help of Herzberg's (1967) Two-Factor Theory (TFT) and construal level theory (CLT). More precisely, there are main purposes of this dissertation. First, the present research tries to understand whether positive or negative hygiene attribute performance and motivator attribute factors exert different weights on overall customer satisfaction depending on the time elapsed from the service experience. Second, to test if positive or negative hygiene/motivator attribute performance affect to revisit intention and to word of mouth by considering mediating role of satisfaction. The results reveal that in the near past (NP) experience, the positive performance of hygiene concrete attributes creates a differential effect on overall satisfaction higher than the negative performance of hygiene concrete attributes. Results also confirmed mediating role of satisfaction in the relationship between attribute performance and revisit intention for near past condition but not for distant past. Likewise significant relationship was found for the mediating role of satisfaction in the relationship between attribute performance and word of mouth (WOM) for near past condition but not for distant past.