Andrew Secker

AISEC: an artificial immune system for e-mail classification

With the increase in information on the Internet, the strive to find more effective tools for distinguishing between interesting and non-interesting material is increasing. Drawing analogies from the biological immune system, this paper presents an immune-inspired algorithm called AISEC that is capable of continuously classifying electronic mail as interesting and non-interesting without the need for re-training. Comparisons are drawn with a naive Bayesian classifier and it is shown that the proposed system performs as well as the naive Bayesian system and has a great potential for augmentation.

On the hierarchical classification of G protein-coupled receptors

MOTIVATION G protein-coupled receptors (GPCRs) play an important role in many physiological systems by transducing an extracellular signal into an intracellular response. Over 50% of all marketed drugs are targeted towards a GPCR. There is considerable interest in developing an algorithm that could effectively predict the function of a GPCR from its primary sequence. Such an algorithm is useful not only in identifying novel GPCR sequences but in characterizing the interrelationships between known GPCRs. RESULTS An alignment-free approach to GPCR classification has been developed using techniques drawn from data mining and proteochemometrics. A dataset of over 8000 sequences was constructed to train the algorithm. This represents one of the largest GPCR datasets currently available. A predictive algorithm was developed based upon the simplest reasonable numerical representation of the proteins physicochemical properties. A selective top-down approach was developed, which used a hierarchical classifier to assign sequences to subdivisions within the GPCR hierarchy. The predictive performance of the algorithm was assessed against several standard data mining classifiers and further validated against Support Vector Machine-based GPCR prediction servers. The selective top-down approach achieves significantly higher accuracy than standard data mining methods in almost all cases.

Optimizing amino acid groupings for GPCR classification

MOTIVATION There is much interest in reducing the complexity inherent in the representation of the 20 standard amino acids within bioinformatics algorithms by developing a so-called reduced alphabet. Although there is no universally applicable residue grouping, there are numerous physiochemical criteria upon which one can base groupings. Local descriptors are a form of alignment-free analysis, the efficiency of which is dependent upon the correct selection of amino acid groupings. RESULTS Within the context of G-protein coupled receptor (GPCR) classification, an optimization algorithm was developed, which was able to identify the most efficient grouping when used to generate local descriptors. The algorithm was inspired by the relatively new computational intelligence paradigm of artificial immune systems. A number of amino acid groupings produced by this algorithm were evaluated with respect to their ability to generate local descriptors capable of providing an accurate classification algorithm for GPCRs.

A Danger Theory Inspired Approach to Web Mining

Within immunology, new theories are constantly being proposed that challenge current ways of thinking. These include new theories regarding how the immune system responds to pathogenic material. This conceptual paper takes one relatively new such theory: the Danger theory, and explores the relevance of this theory to the application domain of web mining. Central to the idea of Danger theory is that of a context dependant response to invading pathogens. This paper argues that this context dependency could be utilised as powerful metaphor for applications in web mining. An illustrative example adaptive mailbox filter is presented that exploits properties of the immune system, including the Danger theory. This is essentially a dynamical classification task: a task that this paper argues is well suited to the field of artificial immune systems, particularly when drawing inspiration from the Danger theory.

GPCRTree: online hierarchical classification of GPCR function

BackgroundG protein-coupled receptors (GPCRs) play important physiological roles transducing extracellular signals into intracellular responses. Approximately 50% of all marketed drugs target a GPCR. There remains considerable interest in effectively predicting the function of a GPCR from its primary sequence.FindingsUsing techniques drawn from data mining and proteochemometrics, an alignment-free approach to GPCR classification has been devised. It uses a simple representation of a proteins physical properties. GPCRTree, a publicly-available internet server, implements an algorithm that classifies GPCRs at the class, sub-family and sub-subfamily level.ConclusionA selective top-down classifier was developed which assigns sequences within a GPCR hierarchy. Compared to other publicly available GPCR prediction servers, GPCRTree is considerably more accurate at every level of classification. The server has been available online since March 2008 at URL: http://igrid-ext.cryst.bbk.ac.uk/gpcrtree/.

Hierarchical classification of G-Protein-Coupled Receptors with data-driven selection of attributes and classifiers

We address the important bioinformatics problem of predicting protein function from a proteins primary sequence. We consider the functional classification of G-Protein-Coupled Receptors (GPCRs), whose functions are specified in a class hierarchy. We tackle this task using a novel top-down hierarchical classification system where, for each node in the class hierarchy, the predictor attributes to be used in that node and the classifier to be applied to the selected attributes are chosen in a data-driven manner. Compared with a previous hierarchical classification system selecting classifiers only, our new system significantly reduced processing time without significantly sacrificing predictive accuracy.

AISIID: An artificial immune system for interesting information discovery on the web

There exist numerous systems for mining the web in search of relevant information but few exist for the discovery of interesting information. The discovery of interesting information is an advance on basic text mining in that it aims to identify text that is novel, unexpected or surprising to a user, whilst still being relevant. This article investigates the use of artificial immune systems (AIS) applied to discovery of interesting information. AIS are thought to confer the adaptability and learning required for this task. Artificial immune system for interesting information discovery (AISIID) is described in some detail, then an evaluative study is undertaken involving the subjective evaluation of the results by users. AISIID is found to discover pages rated more interesting by users than a comparative system.

Alignment-Independent Techniques for Protein Classification

Predicting protein structure and function from amino acid sequences is a central aim of bioinformatics. Most bioinformatics analyses use sequence alignment as the basis by which to measure similarity. However, there is increasing evidence that many protein families are resistant to this straightforward method of comparison. Increasingly, a combination of machine-learning techniques and abstract representations of protein sequences is being used to classify proteins based upon the similarity of their physico-chemical properties rather than scoring sequence alignments. This is particularly effective in protein families that show greater structural conservation but appear to lack conserved sequences. Here we describe the inherent limitations of the alignment-dependent approaches to protein classification and present ‘alignment-free’ representations as a viable and realistic alternative to solve complex problems within bioinformatics.

An Artificial Immune System for Evolving Amino Acid Clusters Tailored to Protein Function Prediction

This paper addresses the classification task of data mining (a form of supervised learning) in the context of an important bioinformatics problem, namely the prediction of protein functions. This problem is cast as a hierarchical classification problem, where the protein functions to be predicted correspond to classes that are arranged in a hierarchical structure, in the form of a class tree. The main contribution of this paper is to propose a new Artificial Immune System that creates a new representation for proteins, in order to maximize the predictive accuracy of a hierarchical classification algorithm applied to the corresponding protein function prediction problem.

An Artificial Immune System for Clustering Amino Acids in the Context of Protein Function Classification

This paper addresses the classification task of data mining (a form of supervised learning) in the context of an important bioinformatics problem, namely the prediction of protein functions. This problem is cast as a hierarchical classification problem. The protein functions to be predicted correspond to classes that are arranged in a hierarchical structure (this takes the form of a class tree). The main contribution of this paper is to propose a new Artificial Immune System that creates a new representation for proteins, in order to maximize the predictive accuracy of a hierarchical classification algorithm applied to the corresponding protein function prediction problem.