Protein sequences are essential for encoding molecular structures and functions. Consequently, biologists invest substantial resources and time discovering functional patterns in proteins. Using high-throughput technologies, biologists are generating an increasing amount of data. Thus, the major challenge in biosequencing today is the ability to conduct data analysis in an effi cient and productive manner. Conserved amino acids in proteins reveal important functional domains within protein families. Conversely, less conserved amino acid variations within these protein sequence patterns reveal areas of evolutionary and functional divergence. Exploring protein families using existing methods such as multiple sequence alignment is computationally expensive, thus pattern search is used. However, at present, combinatorial methods of pattern search generate a large set of solutions, and probabilistic methods require richer representations. They require biological ground truth of the input sequences, such as gene name or taxonomic species, as class labels based on traditional classi fication practice to train a model for predicting unknown sequences. However, these algorithms are inherently biased by mislabelling and may not be able to reveal class characteristics in a detailed and succinct manner. A novel pattern representation called an Aligned Pattern Cluster (AP Cluster) as developed in this dissertation is compact yet rich. It captures conservations and variations of amino acids and covers more sequences with lower entropy and greatly reduces the number of patterns. AP Clusters contain statistically signi cant patterns with variations; their importance has been confi rmed by the following biological evidences: 1) Most of the discovered AP Clusters correspond to binding segments while their aligned columns correspond to binding sites as verifi ed by pFam, PROSITE, and the three-dimensional structure. 2) By compacting strong correlated functional information together, AP Clusters are able to reveal class characteristics for taxonomical classes, gene classes and other functional classes, or incorrect class labelling. 3) Co-occurrence of AP Clusters on the same homologous protein sequences are spatially close in the protein's three-dimensional structure. These results demonstrate the power and usefulness of AP Clusters. They bring in similar statistically signifi cance patterns with variation together and align them to reveal protein regional functionality, class characteristics, binding and interacting sites for the study of protein-protein and protein-drug interactions, for diff erentiation of cancer tumour types, targeted gene therapy as well as for drug target discovery.

Reinforcement Learning (RL) is a learning framework in which an agent learns a policy from continual interaction with the environment. A policy is a mapping from states to actions. The agent receives rewards as feedback on the actions performed. The objective of RL is to design autonomous agents to search for the policy that maximizes the expectation of the cumulative reward. When the environment is partially observable, the agent cannot determine the states with certainty. These states are called hidden in the literature. An agent that relies exclusively on the current observations will not always find the optimal policy. For example, a mobile robot needs to remember the number of doors went by in order to reach a specific door, down a corridor of identical doors. To overcome the problem of partial observability, an agent uses both current and past (memory) observations to construct an internal state representation, which is treated as an abstraction of the environment. This research focuses on how features of past events are extracted with variable granularity regarding the internal state construction. The project introduces a new method that applies Information Theory and decision tree technique to derive a tree structure, which represents the state and the policy. The relevance, of a candidate feature, is assessed by the Information Gain Ratio ranking with respect to the cumulative expected reward. Experiments carried out on three different RL tasks have shown that our variant of the U-Tree (McCallum, 1995) produces a more robust state representation and faster learning. This better performance can be explained by the fact that the Information Gain Ratio exhibits a lower variance in return prediction than the Kolmogorov-Smirnov statistical test used in the original U-Tree algorithm.