Compositional model repositories via dynamic constraint satisfaction with order-of-magnitude preferences

J. Keppens and Q. Shen. Compositional model repositories via dynamic constraint satisfaction with order-of-magnitude preferences. Journal of Artificial Intelligence Research, 21:499-550, 2004.

Preserving Piece-wise Linearity in Fuzzy Interpolation

Z. Huang and Q. Shen. Preserving Piece-wise Linearity in Fuzzy Interpolation. Proceedings of the 2005 UK Workshop on Computational Intelligence, pages 105-112.

Fault identification through the combination of symbolic conflict recognition and Markov Chain-aided belief revision

F. Smith and Q. Shen. Fault identification through the combination of symbolic conflict recognition and Markov Chain-aided belief revision. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 34(5):649-663, 2004.

Scale and move transformation-based fuzzy interpolative reasoning: A revisit

Z. Huang and Q. Shen. Scale and move transformation-based fuzzy interpolative reasoning: A revisit. Proceedings of the 13th International Conference on Fuzzy Systems, pages 623-628, 2004.

Causality enabled compositional modelling of Bayesian networks

J. Keppens and Q. Shen. Causality enabled compositional modelling of Bayesian networks. Proceedings of the 18th International Workshop on Qualitative Reasoning, pages 33-40, 2004.

Fuzzy Interpolation and Extrapolation: A Practical Approach

Z. Huang and Q. Shen. Fuzzy interpolative and extrapolative reasoning: a practical approach. IEEE Transactions on Fuzzy Systems, 16(1):13-28, 2008.

Using a logical model to predict the growth of yeast

Whelan, K. E. and King, R. D. Using a logical model to predict the growth of yeast. BMC Bioinformatics 2008, 9:97

A Phylogenetic Mixture Model for Detecting Pattern-Heterogeneity in Gene Sequence or Character-State Data

Mark Pagel, Andrew Meade (2004). A phylogenetic mixture model for detecting pattern-heterogeneity in gene sequence or character-state data. Systematic Biology, 53(4), 571-581. RAE2008

Perspectiva dos médicos dentistas acerca da metodologia clínica aplicada nos tratamentos de branqueamento dentário

Monografia apresentada à Universidade Fernando Pessoa para obtenção do grau de Licenciada em Medicina Dentária

Sobrecargas posturais

Projeto de Pós-Graduação/Dissertação apresentado à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Mestre em Medicina Dentária

Controlo do tratamento endodontico não-cirúrgico de dentes com diagnóstico de necrose pulpar associada ou não a lesão apical

Projeto de Pós-Graduação/Dissertação apresentado à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Mestre em Medicina Dentária

Integration of relational and hierarchical network information for protein function prediction

BACKGROUND:In the current climate of high-throughput computational biology, the inference of a protein's function from related measurements, such as protein-protein interaction relations, has become a canonical task. Most existing technologies pursue this task as a classification problem, on a term-by-term basis, for each term in a database, such as the Gene Ontology (GO) database, a popular rigorous vocabulary for biological functions. However, ontology structures are essentially hierarchies, with certain top to bottom annotation rules which protein function predictions should in principle follow. Currently, the most common approach to imposing these hierarchical constraints on network-based classifiers is through the use of transitive closure to predictions.RESULTS:We propose a probabilistic framework to integrate information in relational data, in the form of a protein-protein interaction network, and a hierarchically structured database of terms, in the form of the GO database, for the purpose of protein function prediction. At the heart of our framework is a factorization of local neighborhood information in the protein-protein interaction network across successive ancestral terms in the GO hierarchy. We introduce a classifier within this framework, with computationally efficient implementation, that produces GO-term predictions that naturally obey a hierarchical 'true-path' consistency from root to leaves, without the need for further post-processing.CONCLUSION:A cross-validation study, using data from the yeast Saccharomyces cerevisiae, shows our method offers substantial improvements over both standard 'guilt-by-association' (i.e., Nearest-Neighbor) and more refined Markov random field methods, whether in their original form or when post-processed to artificially impose 'true-path' consistency. Further analysis of the results indicates that these improvements are associated with increased predictive capabilities (i.e., increased positive predictive value), and that this increase is consistent uniformly with GO-term depth. Additional in silico validation on a collection of new annotations recently added to GO confirms the advantages suggested by the cross-validation study. Taken as a whole, our results show that a hierarchical approach to network-based protein function prediction, that exploits the ontological structure of protein annotation databases in a principled manner, can offer substantial advantages over the successive application of 'flat' network-based methods.

Adding Polymorphic Abstraction to ML (Detailed Abstract)

The ML programming language restricts type polymorphism to occur only in the "let-in" construct and requires every occurrence of a formal parameter of a function (a lambda abstraction) to have the same type. Milner in 1978 refers to this restriction (which was adopted to help ML achieve automatic type inference) as a serious limitation. We show that this restriction can be relaxed enough to allow universal polymorphic abstraction without losing automatic type inference. This extension is equivalent to the rank-2 fragment of system F. We precisely characterize the additional program phrases (lambda terms) that can be typed with this extension and we describe typing anomalies both before and after the extension. We discuss how macros may be used to gain some of the power of rank-3 types without losing automatic type inference. We also discuss user-interface problems in how to inform the programmer of the possible types a program phrase may have.

A Direct Algorithm for the Type Interference in the Rank 2 Fragment of the Second--Order λ-Calculus

We study the problem of type inference for a family of polymorphic type disciplines containing the power of Core-ML. This family comprises all levels of the stratification of the second-order lambda-calculus by "rank" of types. We show that typability is an undecidable problem at every rank k ≥ 3 of this stratification. While it was already known that typability is decidable at rank ≤ 2, no direct and easy-to-implement algorithm was available. To design such an algorithm, we develop a new notion of reduction and show how to use it to reduce the problem of typability at rank 2 to the problem of acyclic semi-unification. A by-product of our analysis is the publication of a simple solution procedure for acyclic semi-unification.