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Articles 1 - 3 of 3
Full-Text Articles in Physical Sciences and Mathematics
Deepcon-Pre: Improved Protein Contact Map Prediction Using Inverse Covariance And Deep Residual Networks, Nachammai Palaniappan
Deepcon-Pre: Improved Protein Contact Map Prediction Using Inverse Covariance And Deep Residual Networks, Nachammai Palaniappan
Theses
As with most domains where machine learning methods are applied, correct feature engineering is critical when developing deep learning algorithms for solving the protein folding problem. Unlike the domains such as computer vision and natural language processing, feature engineering is not rigorously studied towards solving the protein folding problem. A recent research has highlighted that input features known as precision matrix are most informative for predicting inter-residue contact map, the key for building three-dimensional models. In this work, we study the significance of the precision matrix feature when very deep residual networks are trained. Using a standard dataset of 3456 …
Protein Inter-Residue Distance Prediction Using Residual And Capsule Networks, Andrew Dillon
Protein Inter-Residue Distance Prediction Using Residual And Capsule Networks, Andrew Dillon
Theses
The protein folding problem, also known as protein structure prediction, is the task of building three-dimensional protein models given their one-dimensional amino acid sequence. New methods that have been successfully used in the most recent CASP challenge have demonstrated that predicting a protein's inter-residue distances is key to solving this problem. Various deep learning algorithms including fully convolutional neural networks and residual networks have been developed to solve the distance prediction problem. In this work, we develop a hybrid method based on residual networks and capsule networks. We demonstrate that our method can predict distances more accurately than the algorithms …
Enhancing Scalability In Genetic Programming With Adaptable Constraints, Type Constraints And Automatically Defined Functions, George Gerules
Enhancing Scalability In Genetic Programming With Adaptable Constraints, Type Constraints And Automatically Defined Functions, George Gerules
Dissertations
Genetic Programming is a type of biological inspired machine learning. It is composed of a population of stochastic individuals. Those individuals can exchange portions of themselves with others in the population through the crossover operation that draws its inspiration from biology. Other biologically inspired operations include mutation and reproduction. The form an individual takes can be many things. It, however, is represented most of the time as a computer program. Constructing correct efficient programs can be notoriously difficult. Various grammar, typing, function constraint, or counting mechanisms can guide creation and evolution of those individuals. These mechanisms can reduce search space …