Christian Theil Have

Efficient tabling of structured data with enhanced hash-consing

Current tabling systems suffer from an increase in space complexity, time complexity or both when dealing with sequences due to the use of data structures for tabled subgoals and answers and the need to copy terms into and from the table area. This symptom can be seen in not only B-Prolog, which uses hash tables, but also systems that use tries such as XSB and YAP. In this paper, we apply hash-consing to tabling structured data in B-Prolog. While hash-consing can reduce the space consumption when sharing is effective, it does not change the time complexity. We enhance hash-consing with two techniques, called input sharing and hash code memoization, for reducing the time complexity by avoiding computing hash codes for certain terms. The improved system is able to eliminate the extra linear factor in the old system for processing sequences, thus significantly enhancing the scalability of applications such as language parsing and bio-sequence analysis applications. We confirm this improvement with experimental results.

Inference with constrained hidden markov models in prism

A Hidden Markov Model (HMM) is a common statistical model which is widely used for analysis of biological sequence data and other sequential phenomena. In the present paper we show how HMMs can be extended with side-constraints and present constraint solving techniques for efficient inference. Defining HMMs with side-constraints in Constraint Logic Programming have advantages in terms of more compact expression and pruning opportunities during inference. nWe present a PRISM-based framework for extending HMMs with side-constraints and show how well-known constraints such as cardinality and all different are integrated. We experimentally validate our approach on the biologically motivated problem of global pairwise alignment.

Bayesian Annotation Networks for Complex Sequence Analysis

Probabilistic models that associate annotations to sequential data are widely used in computational biology and a range of other applications. Models integrating with logic programs provide, furthermore, for sophistication and generality, at the cost of potentially very high computational complexity. A methodology is proposed for modularization of such models into sub-models, each representing a particular interpretation of the input data to be analysed. Their composition forms, in a natural way, a Bayesian network, and we show how standard methods for prediction and training can be adapted for such composite models in an iterative way, obtaining reasonable complexity results. Our methodology can be implemented using the probabilistic-logic PRISM system, developed by Sato et al, in a way that allows for practical applications.

Effects of using coding potential, sequence conservation and mRNA structure conservation for predicting pyrrolysine containing genes.

BackgroundPyrrolysine (the 22nd amino acid) is in certain organisms and under certain circumstances encoded by the amber stop codon, UAG. The circumstances driving pyrrolysine translation are not well understood. The involvement of a predicted mRNA structure in the region downstream UAG has been suggested, but the structure does not seem to be present in all pyrrolysine incorporating genes.ResultsWe propose a strategy to predict pyrrolysine encoding genes in genomes of archaea and bacteria. We cluster open reading frames interrupted by the amber codon based on sequence similarity. We rank these clusters according to several features that may influence pyrrolysine translation. The ranking effects of different features are assessed and we propose a weighted combination of these features which best explains the currently known pyrrolysine incorporating genes. We devote special attention to the effect of structural conservation and provide further substantiation to support that structural conservation may be influential - but is not a necessary factor. Finally, from the weighted ranking, we identify a number of potentially pyrrolysine incorporating genes.ConclusionsWe propose a method for prediction of pyrrolysine incorporating genes in genomes of bacteria and archaea leading to insights about the factors driving pyrrolysine translation and identification of new gene candidates. The method predicts known conserved genes with high recall and predicts several other promising candidates for experimental verification. The method is implemented as a computational pipeline which is available on request.

A Declarative Pipeline Language for Complex Data Analysis

We introduce BANpipe – a logic-based scripting language designed to model complex compositions of time consuming analyses. Its declarative semantics is described together with alternative operational semantics facilitating goal directed execution, parallel execution, change propagation and type checking. A portable implementation is provided, which supports expressing complex pipelines that may integrate different Prolog systems and provide automatic management of files.

Querying Sentiment Development over Time

A new language is introduced for describing hypotheses about fluctuations of measurable properties in streams of timestamped data, and as prime example, we consider trends of emotions in the constantly flowing stream of Twitter messages. The language, called EmoEpisodes, has a precise semantics that measures how well a hypothesis characterizes a given time interval; the semantics is parameterized so it can be adjusted to different views of the data. EmoEpisodes is extended to a query language with variables standing for unknown topics and emotions, and the query-answering mechanism will return instantiations for topics and emotions as well as time intervals that provide the largest deflections in this measurement. Experiments are performed on a selection of Twitter data to demonstrates the usefulness of the approach.

Efficient tabling of structured data using indexing and program transformation

Tabling of structured data is important to support dynamic programming in logic programs. Several existing tabling systems for Prolog do not efficiently deal with structured data, but duplicate part of the structured data in different instances of tabled goals. As a consequence, time and space complexity may often be significantly higher than the theoretically optimal. A simple program transformation is proposed which uses an indexing of structured data that eliminates this problem, and drastic improvements of time and space complexity can be demonstrated. The technique is demonstrated for dynamic programming examples expressed in Prolog and in PRISM.

Logic-Statistic Models with Constraints for Biological Sequence Analysis

This project aims to investigate biologically inspired, logic-statistic models with constraints. The complexity and expressiveness of models with different kinds of constraints will be examined and algorithms to efficiently cope with inference in and training of such models will be explored. The models will be evaluated with regards to their applicability to biological sequence analysis.