Pattern discovery in protein interaction networks can reveal crucial biological knowledge on the inner workings of cellular machinery. Although far from complete, extracting meaningful patterns from proteomic networks is a nontrivial task due to their size-complexity. This paper proposes a computational framework to efficiently discover topologically-similar patterns from large proteomic networks using Particle Swarm Optimization (PSO). PSO is a robust and low-cost optimization technique that demonstrated to work effectively on the complex, mostly sparse proteomic networks. The resulting topologicallysimilar patterns of close proximity are utilized to systematically predict new high-confidence protein-protein interactions (PPIs). The proposed PSO-based PPI prediction method (3PI) managed to predict high-confidence PPIs, validated by more than one computational/experimental source, through a proposed PPI knowledge transfer process between topologically-similar interaction patterns of close proximity. In three case studies, over 50% of the predicted interactions for EFGR, ERBB2, ERBB3, GRB2 and UBC are overlapped with publically available interaction databases, ~80% of the predictions are found among the Top 1% results of another PPI prediction method and their genes are significantly co-expressed across different tissues. Moreover, the only single prediction example that did not overlap with any of our validation sources was recently experimentally supported by two PubMed publications.

This paper proposes a novel particle-swarm based approach for protein structure alignment and comparison. Applying heuristic search to discover similar protein substructure patterns can be easily trapped in certain regions of the sparse and challenging problem search space. Diversification, or restarting the heuristic search, is one of the common strategies used to escape local optima. Agile Particle Swarm Optimization (APSO) is a recent multi-start PSO that addresses the question of when to best restart swarm particles. This paper focuses on where and how to restart the swarm. Another challenge of applying a heuristic search to protein structures is that the fitness landscape does not necessarily guide to the optimal region. To address this issue, we propose the Targeted Agile PSO (TA-PSO) that uses a dynamic window-based search for automatic, variable-size pattern discovery in protein structures. The TA-PSO automatically builds a guiding list of potential patterns and uses it during the search process, which helps to find better solutions faster. The proposed TA-PSO showed up to 4 times improved performance that is 3.5 times faster and 6 times more robust/consistent compared with the traditional --non-targeted search

Recent advances of robotic/mechanical devices enable us to measure a subjectpsilas performance in an objective and precise manner. The main issue of using such devices is how to represent huge experimental data compactly in order to analyze and compare them with clinical data efficiently. In this paper, we choose a subset of features from real-time experimental data and build a classifier model to assess stroke patientspsila upper limb functionality. We compare our model with combinations of different classifiers and ensemble schemes, showing that it outperforms competitors. We also demonstrate that our results from experimental data are consistent with clinical information, and can capture changes of upper-limb functionality over time.

Assessment of sensory, motor and cognitive function of stroke subjects provide important information to guide patient rehabilitation. As many of the currently used measures are inherently subjective and use course rating scales, here we propose a hierarchical ensemble network that can automatically identify stroke patients and assess their upper limb functionality objectively, based on experimental task data. We compare our neural network ensemble model with ten combinations of different classifiers and ensemble schemes, showing that it significantly outperforms competitors. We also demonstrate that our measure scale is congruent with clinical information, responsive with changes of patients motor function, and reliable in terms of test-retest configuration.

Robotic/mechanic devices have become widely used for various medical assessments recently. While using these devices are beneficial in terms of accuracy and objectiveness, validation and consistency problem may occur when combining these data with traditional clinical information. Here we propose a visualization tool that can summarize the experimental data and compare them with the clinical data, in the stroke impairment assessment domain. This visual tool is based on a neural network ensemble that is trained to match the experimental data with Chedoke-McMaster scale, one of the major outcome measure for stroke impairment and recovery assessment. We compare our ensemble model with ten combinations of different classifiers and ensemble schemes, showing that it outperforms competitors. We also demonstrate that our visualization approach is consistent with clinical information, and reliable in a sense that output of our ensemble can be an estimator for the corresponding clinical data when Chedoke-McMaster scores are missing.

We show that visio‐spatial representations and reasoning can be used as a similarity metric for case‐based protein structure prediction. Our system retrieves pairs of α‐helices based on contact map similarity, then transfers and adapts the structure information to an unknown helix pair. We show that similar protein contact maps predict a similar three‐dimensional protein structure. The success of this method provides support for the notion that changing representations can enable similarity metrics in case‐based reasoning.

Commercial software release management is primarily concerned with the timely release of the next version of a product. There are a number of objectives in any new release of software, including: implementation of new features, repair of known defects, and maintenance of the overall quality of the software to facilitate future evolution. Deciding on the degree to which each of these objectives will be met is a chronic problem, with real-time consequences for managers responsible for software releases. To make effective decisions, management requires supporting product facts and data. This paper presents an approach based on data warehousing technologies for creating, maintaining, accessing and analyzing product data. The result is a tool that provides management with a uniform, integrated presentation of timely information to support decision making. The infrastructure has been evaluated in a large case study, which makes clear the advantages and shortcomings of the approach.

Abstract Motivation: With the increasing number of gene expression databases, the need for more powerful analysis and visualization tools is growing. Many techniques have successfully been applied to unravel latent similarities among genes and/or experiments. Most of the current systems for microarray data analysis use statistical methods, hierarchical clustering, self-organizing maps, support vector machines, or k-means clustering to organize genes or experiments into ‘meaningful’ groups. Without prior explicit bias almost all of these clustering methods applied to gene expression data not only produce different results, but may also produce clusters with little or no biological relevance. Of these methods, agglomerative hierarchical clustering has been the most widely applied, although many limitations have been identified. Results: Starting with a systematic comparison of the underlying theories behind clustering approaches, we have devised a technique that combines tree-structured vector quantization and partitive k-means clustering (BTSVQ). This hybrid technique has revealed clinically relevant clusters in three large publicly available data sets. In contrast to existing systems, our approach is less sensitive to data preprocessing and data normalization. In addition, the clustering results produced by the technique have strong similarities to those of self-organizing maps (SOMs). We discuss the advantages and the mathematical reasoning behind our approach. Availability: The BTSVQ system is implemented in Matlab R12 using the SOM toolbox for the visualization and preprocessing of the data http://www.cis.hut.fi/projects/somtoolbox/ BTSVQ is available for non-commercial use http://www.uhnres.utoronto.ca/ta3/BTSVQ Contact: [email protected] Keywords: microarray data clustering and visulization; self-organizing maps, partitive k-means clustering; lung cancer.

Over the past ten years, a subfield of GIScience has been recognized that addresses the linkage between human thought regarding geographical space, and the mechanisms for implementing these concepts in computational models. This research area has developed an identity through a series of successful international conferences and the establishment of a journal. It has also been complemented through community activities such as international standardization efforts and GIS interoperability. Historically, much of the advancement in computational methods has occurred at or close to the implementation level, as exemplified by attention to the development of spatial access methods. Significant progress has been made at the levels of spatial data models and spatial query languages, although we note the lack of a comprehensive theoretical framework comparable to the relational data model in database management systems. The difficult problems that need future research efforts are at the highly abstract level of capturing semantics of geographical information. A cognitive motivation is most promising as it shapes the focus on the users' needs and points of view, rather than on efficiency as in the case of a bottom-up system design. We also identify the need for new research in fields, models of qualitative spatial information, temporal aspects, knowledge discovery, and the integration of GIS with database management systems.

This paper presents a novel approach to model–based diagnosis. The approach addresses the two main problems that have prevented model–based diagnostic techniques from being widely used: computational complexity of abduction and inadequacies of device models. A model for automated diagnosis is defined that combines (1) deduction to rule out hypotheses, (2) abduction to generate hypotheses, and (3) induction to recall past experiences and account for potential errors in the device models. A review of the three forms of inference is provided, as well as a detailed analysis of the relationship between case–based reasoning and induction. The proposed model for diagnosis is used to characterize diagnostic errors and relate them to different types of errors in the device models. Experimental results are then described and used to assert the practicality and the usefulness of the approach. The model presented in this paper yields a practical method for solving hard diagnostic problems at a reasonable computational cost and provides a theoretical basis for overcoming the problem of partially incorrect device models.

A fundamental goal of research in molecular biology is to understand protein structure. Protein crystallography is currently the most successful method for determining the three-dimensional (3D) conformation of a protein, yet it remains labor intensive and relies on an expert's ability to derive and evaluate a protein scene model. In this paper, the problem of protein structure determination is formulated as an exercise in scene analysis. A computational methodology is presented in which a 3D image of a protein is segmented into a graph of critical points. Bayesian and certainty factor approaches are described and used to analyze critical point graphs and identify meaningful substructures, such as alpha-helices and beta-sheets. Results of applying the methodologies to protein images at low and medium resolution are reported. The research is related to approaches to representation, segmentation and classification in vision, as well as to top-down approaches to protein structure prediction.

Classification involves associating instances with particular classes by maximizing intra-class similarities and minimizing inter-class similarities. The paper presents a novel approach to case-based classification. The algorithm is based on a notion of similarity assessment and was developed for supporting flexible retrieval of relevant information. Validity of the proposed approach is tested on real world domains, and the system's performance is compared to that of other machine learning algorithms.

An approach to knowledge discovery in complex molecular databases is described. The machine learning paradigm used is structured concept formation, in which object's described in terms of components and their interrelationships are clustered and organized in a knowledge base. Symbolic images are used to represent classes of structured objects. A discovered molecular knowledge base is successfully used in the interpretation of a high resolution electron density map.>

A formal framework called Security Logic ( SL ) is developed for specifying and reasoning about security policies and for verifying that system designs adhere to such policies. Included in this modal logic framework are definitions of knowledge, permission, and obligation . Permission is used to specify secrecy policies and obligation to specify integrity policies. The combination of policies is addressed and examples based on policies from the current literature are given.

The network security model originally developed for the SNet secure system is generally applicable to all secure networks. Although it was recognized that allowing message loss in the model permits a covert channel, this operational model was accepted on an intuitive basis as enforcing some concept of secrecy. The paper presents a formal argument that the model, augmented with a no-loss requirement, does indeed satisfy a formal abstract secrecy policy. This secrecy policy, called INF, has previously been defined in terms of security logic. The proof contains two steps. First, the network model augmented with a no-loss requirement (and called NM) is shown to satisfy the non-deducibility (ND) condition. ND satisfies INF, therefore, NM satisfies INF.>

Nial is a programming language designed around a mathematical treatment of data as nested arrays. A goal of the research described is to integrate within Nial a functional style of programming based on the theory of arrays with the declarative capabilities of a logic programming environment. This is partially accomplished by storing logic clauses as arrays which can be manipulated using logic clauses. Arrays as terms are considered as part of the syntax of the clauses. The approach to logic programming is based on providing a flexible environment for experimenting with full clausal or Horn clause logic. A variety of predefined control strategies and the capability for user-defined control strategies have been provided. The expressive capabilities of combining logic and functional programming styles provides a suitable language for many application areas. The philosophy and design behind a combined logic/database model used to prototype a knowledge-based systems application are described.>

A formal framework called security logic (SL) is developed for specifying and reasoning about security policies, and for verifying that system designs adhere to such policies. Included in this framework is a definition of knowledge based on modal logic so that properties can be time-related, a definition of permission, and a definition of obligation. Permission is used to specify secrecy policies, and obligation is used to specify integrity policies. A security policy is given as a set of policy constraints on the SL model. The combination of policies is addressed. Examples based on policies from the current literature are given.>

It is suggested that the notion of obligation found in modal logic can be used as the fundamental notion in formally specifying integrity. Integrity as represented by Clark-Wilson-type models is discussed. It is argued that the essential part of their model, for the purpose of formal specification, is an expression of required connectivity in a graph representing integrity subjects. A review of knowledge logic is given, followed by a discussion of a logic of security that contains operators for knowledge and obligation. A formal semantic definition of integrity based on operator nets is given.>

A methodology is presented for transforming a functional specification written in Lucid to an equivalent specification that captures its real-time properties. The enhanced specification consists of a set of equations that can be solved for several properties, including execution time and external requirements, or may simply be checked for the existence of a solution. Lucid has a set of meaning-preserving transformations, and a proof system corresponding to a behavioral semantics has been constructed. Both of these tools can be used to reason about properties of the specification. The specification is executable and can be used as a prototype for the system being specified. It is possible to express architectural constraints within the same formal framework. Thus this type of specification can be used to guide the development of new real-time systems.>

A method for reasoning about knowledge in multilevel secure distributed systems is introduced. This method, based on a behavioral semantics for operator nets, can be used to specify a variety of security properties such as nondisclosure, integrity, and authority systems. The major attributes of the method are the intuitive nature of the specifications and the expressibility of the model, which allows statements about temporal properties and deductive capabilities of processes.>

A security model for the SNet multilevel secure distributed system, bawd on a behavioral semantics for operator nets and expressed in Lucid, is described. This model subsumes a previously published model of the network within SNet and includes authorized downgrading as well as the security policies enforced by trusted hosts connected to the network. The previous model is based on seven rather ad hoc constraints without presenting a coherent argument regarding security. The model described in this paper provides a more general abstract model than is provided by those' seven constraints.

This paper describes current work on the design and specification of a multilevel secure distributed system called SNet. It discusses security models in general, the various problems of information flows in SNet, and the abstract and concrete security model components for SNet. It also introduces Lucid as a language for specifying distributed systems. The model components are expressed in Lucid; these Lucid partial specifications are shown to be correct with respect to the formal model, and the two model components are shown to be consistent. The complete functional specification of SNet in Lucid, its implementation in Concurrent Euclid, and the verification of the implementation with respect to the Lucid specification are not discussed.

Most of the recent research on programming languages for education has been centered around the language Logo. In this paper we introduce another candidate language for learning environments, Nial, the nested interactive array language. Nial is a general‐purpose programming language based on a formal theory of mathematics called array theory. This paper introduces Nial as a language for learning programming and developing and using computer‐aided instruction tools. A comparison with Logo is provided to evaluate these two languages in terms of their strengths and weaknesses as programming environments for novice programmers. We also demonstrate that a programming environment can be both simple to leam at the novice level and extendible to a powerful and sophisticated language.