Automated Genome-Wide Protein Domain Exploration

Exploiting the exponentially growing genomics and proteomics data requires high quality, automated analysis. Protein domain modeling is a key area of molecular biology as it unravels the mysteries of evolution, protein structures, and protein functions. A plethora of sequences exist in protein databases with incomplete domain knowledge. Hence this research explores automated bioinformatics tools for faster protein domain analysis. Automated tool chains described in this dissertation generate new protein domain models thus enabling more effective genome-wide protein domain analysis. To validate the new tool chains, the Shewanella oneidensis and Escherichia coli genomes were processed, resulting in a new peptide domain database, detection of poor domain models, and identification of likely new domains. The automated tool chains will require months or years to model a small genome when executing on a single workstation. Therefore the dissertation investigates approaches with grid computing and parallel processing to significantly accelerate these bioinformatics tool chains

Genomic data analysis using grid-based computing

Microarray experiments generate a plethora of genomic data; therefore we need techniques and architectures to analyze this data more quickly. This thesis presents a solution for reducing the computation time of a highly computationally intensive data analysis part of a genomic application. The application used is the Stanford Microarray Database (SMD). SMD\u27s implementation, working, and analysis features are described. The reasons for choosing the computationally intensive problems of the SMD, and the background importance of these problems are presented. This thesis presents an effective parallel solution to the computational problem, including the difficulties faced with the parallelization of the problem and the results achieved. Finally, future research directions for achieving even greater speedups are presented

Graph Neural Networks for E-Learning Recommendation Systems

This paper presents a novel recommendation system for e-learning platforms. Recent years have seen the emergence of graph neural networks (GNNs) for learning representations over graph-structured data. Due to their promising performance in semi-supervised learning over graphs and in recommendation systems, we employ them in e-learning platforms for user profiling and content profiling. Affinity graphs between users and learning resources are constructed in this study, and GNNs are employed to generate recommendations over these affinity graphs. In the context of e-learning, our proposed approach outperforms multiple different content-based and collaborative filtering baselines

Scaling description of frictionless dense suspensions under inhomogeneous flow

Predicting the rheology of dense suspensions under inhomogeneous flow is crucial in many industrial and geophysical applications, yet the conventional `$\mu(J)$' framework is limited to homogeneous conditions in which the shear rate and solids fraction are spatially invariant. To address this shortcoming, we use particle-based simulations of frictionless dense suspensions to derive new constitutive laws that unify the rheological response under both homogeneous and inhomogeneous conditions. By defining a new dimensionless number associated with particle velocity fluctuations and combining it with the viscous number, the macroscopic friction and the solids fraction, we obtain scaling relations that collapse data from homogeneous and inhomogeneous simulations. The relations allow prediction of the steady state velocity, stress and volume fraction fields using only knowledge of the applied driving force.Comment: 5 pages; 3 figure

Scaling Description of Frictionless Dense Suspensions under Inhomogeneous Flow

Predicting the rheology of dense suspensions under inhomogeneous flow is crucial in many industrial and geophysical applications, yet the conventional “μ(J)” framework is limited to homogeneous conditions in which the shear rate and solids fraction are spatially invariant. To address this shortcoming, we use particle-based simulations of frictionless dense suspensions to derive new constitutive laws that unify the rheological response under both homogeneous and inhomogeneous conditions. By defining a new dimensionless number associated with particle velocity fluctuations and combining it with the viscous number, the macroscopic friction, and the solids fraction, we obtain scaling relations that collapse data from homogeneous and inhomogeneous simulations. The relations allow prediction of the steady state velocity, stress, and volume fraction fields using only knowledge of the applied driving force