Classification and Analysis of Regulatory Pathways Using Graph Property, Biochemical and Physicochemical Property, and Functional Property

Given a regulatory pathway system consisting of a set of proteins, can we predict which pathway class it belongs to? Such a problem is closely related to the biological function of the pathway in cells and hence is quite fundamental and essential in systems biology and proteomics. This is also an extremely difficult and challenging problem due to its complexity. To address this problem, a novel approach was developed that can be used to predict query pathways among the following six functional categories: (i) “Metabolism”, (ii) “Genetic Information Processing”, (iii) “Environmental Information Processing”, (iv) “Cellular Processes”, (v) “Organismal Systems”, and (vi) “Human Diseases”. The prediction method was established trough the following procedures: (i) according to the general form of pseudo amino acid composition (PseAAC), each of the pathways concerned is formulated as a 5570-D (dimensional) vector; (ii) each of components in the 5570-D vector was derived by a series of feature extractions from the pathway system according to its graphic property, biochemical and physicochemical property, as well as functional property; (iii) the minimum redundancy maximum relevance (mRMR) method was adopted to operate the prediction. A cross-validation by the jackknife test on a benchmark dataset consisting of 146 regulatory pathways indicated that an overall success rate of 78.8% was achieved by our method in identifying query pathways among the above six classes, indicating the outcome is quite promising and encouraging. To the best of our knowledge, the current study represents the first effort in attempting to identity the type of a pathway system or its biological function. It is anticipated that our report may stimulate a series of follow-up investigations in this new and challenging area

Improving Manufacturing Data Quality with Data Fusion and Advanced Algorithms for Improved Total Data Quality Management

Data mining and predictive analytics in the sustainable-biomaterials industries is currently not feasible given the lack of organization and management of the database structures. The advent of artificial intelligence, data mining, robotics, etc., has become a standard for successful business endeavors and is known as the ‘Fourth Industrial Revolution’ or ‘Industry 4.0’ in Europe. Data quality improvement through real-time multi-layer data fusion across interconnected networks and statistical quality assessment may improve the usefulness of databases maintained by these industries. Relational databases with a high degree of quality may be the gateway for predictive modeling and enhanced business analytics. Data quality is a key issue in the sustainable bio-materials industry. Untreated data from multiple databases (e.g., sensor data and destructive test data) are generally not in the right structure to perform advanced analytics. Some inherent problems of data from sensors that are stored in data warehouses at millisecond intervals include missing values, duplicate records, sensor failure data (data out of feasible range), outliers, etc. These inherent problems of the untreated data represent information loss and mute predictive analytics. The goal of this data science focused research was to create a continuous real-time software algorithm for data cleaning that automatically aligns, fuses, and assesses data quality for missing fields and potential outliers. The program automatically reduces the variable size, imputes missing values, and predicts the destructive test data for every record in a database. Improved data quality was assessed using 10-fold cross-validation and the normalized root mean square error of prediction (NRMSEP) statistic. The impact of outliers and missing data were tested on a simulated dataset with 201 variations of outlier percentages ranging from 0-90% and missing data percentages ranging from 0-90%. The software program was also validated on a real dataset from the wood composites industry. One result of the research was that the number of sensors needed for accurate predictions are highly dependent on the correlation between independent variables and dependent variables. Overall, the data cleaning software program significantly decreased the NRMSEP ranging from 64% to 12% of quality control variables for key destructive test values (e.g., internal bond, water absorption and modulus of rupture)

Investigation into the role of sequence-driven-features and amino acid indices for the prediction of structural classes of proteins

The work undertaken within this thesis is towards the development of a representative set of sequence driven features for the prediction of structural classes of proteins. Proteins are biological molecules that make living things function, to determine the function of a protein the structure must be known because the structure dictates its physical capabilities. A protein is generally classified into one of the four main structural classes, namely all-α, all-β, α + β or α / β, which are based on the arrangements and gross content of the secondary structure elements. Current methods manually assign the structural classes to the protein by manual inspection, which is a slow process. In order to address the problem, this thesis is concerned with the development of automated prediction of structural classes of proteins and extraction of a small but robust set of sequence driven features by using the amino acid indices. The first main study undertook a comprehensive analysis of the largest collection of sequence driven features, which includes an existing set of 1479 descriptor values grouped by ten different feature groups. The results show that composition based feature groups are the most representative towards the four main structural classes, achieving a predictive accuracy of 63.87%. This finding led to the second main study, development of the generalised amino acid composition method (GAAC), where amino acid index values are used to weigh corresponding amino acids. GAAC method results in a higher accuracy of 68.02%. The third study was to refine the amino acid indices database, which resulted in the highest accuracy of 75.52%. The main contributions from this thesis are the development of four computationally extracted sequence driven feature-sets based on the underused amino acid indices. Two of these methods, GAAC and the hybrid method have shown improvement over the usage of traditional sequence driven features in the context of smaller and refined feature sizes and classification accuracy. The development of six non-redundant novel sets of the amino acid indices dataset, of which each are more representative than the original database. Finally, the construction of two large 25% and 40% homology datasets consisting over 5000 and 7000 protein samples, respectively. A public webserver has been developed located at http://www.generalised-protein-sequence-features.com, which allows biologists and bioinformaticians to extract GAAC sequence driven features from any inputted protein sequence