Extending boolean regulatory network models with answer set programming

Because of their simplicity, boolean networks are a popular formalism to model gene regulatory networks. However, they have their limitations, including their inability to formally and unambiguously define network behaviour, and their lack of the possibility to model meta interactions, i.e., interactions that target other interactions. In this paper we develop an answer set programming (ASP) framework that supports threshold boolean network semantics and extends it with the capability to model meta interactions. The framework is easy to use but sufficiently flexible to express intricate interactions that go beyond threshold network semantics as we illustrate with an example of a Mammalian cell cycle network. Moreover, readily available answer set solvers can be used to find the steady states of the network

Fuzzy Ants Clustering for Web People Search

A search engine query for a person’s name often brings up web pages corresponding to several people who share the same name. The Web People Search (WePS) problem involves organizing such search results for an ambiguous name query in meaningful clusters, that group together all web pages corresponding to one single individual. A particularly challenging aspect of this task is that it is in general not known beforehand how many clusters to expect. In this paper we therefore propose the use of a Fuzzy Ants clustering algorithm that does not rely on prior knowledge of the number of clusters that need to be found in the data. An evaluation on benchmark data sets from SemEval’s WePS1 and WePS2 competitions shows that the resulting system is competitive with the agglomerative clustering Agnes algorithm. This is particularly interesting as the latter involves manual setting of a similarity threshold (or estimating the number of clusters in advance) while the former does not

Efficient Solving of Time-dependent Answer Set Programs

Answer set programs with time predicates are useful to model systems whose properties depend on time, like for example gene regulatory networks. A state of such a system at time point t then corresponds to the literals of an answer set that are grounded with time constant t. An important task when modelling time-dependent systems is to find steady states from which the system\u27s behaviour does not change anymore. This task is complicated by the fact that it is typically not known in advance at what time steps these steady states occur. A brute force approach of estimating a time upper bound tmax and grounding and solving the program w.r.t. that upper bound leads to a suboptimal solving time when the estimate is too low or too high. In this paper we propose a more efficient algorithm for solving Markovian programs, which are time-dependent programs for which the next state depends only on the previous state. Instead of solving these Markovian programs for a long time interval {0,...,tmax}, we successively find answer sets of parts of the grounded program. Our approach guarantees the discovery of all steady states and cycles while avoiding unnecessary extra work

Linguistic feature analysis for protein interaction extraction

<p>Abstract</p> <p>Background</p> <p>The rapid growth of the amount of publicly available reports on biomedical experimental results has recently caused a boost of text mining approaches for protein interaction extraction. Most approaches rely implicitly or explicitly on linguistic, i.e., lexical and syntactic, data extracted from text. However, only few attempts have been made to evaluate the contribution of the different feature types. In this work, we contribute to this evaluation by studying the relative importance of deep syntactic features, i.e., grammatical relations, shallow syntactic features (part-of-speech information) and lexical features. For this purpose, we use a recently proposed approach that uses support vector machines with structured kernels.</p> <p>Results</p> <p>Our results reveal that the contribution of the different feature types varies for the different data sets on which the experiments were conducted. The smaller the training corpus compared to the test data, the more important the role of grammatical relations becomes. Moreover, deep syntactic information based classifiers prove to be more robust on heterogeneous texts where no or only limited common vocabulary is shared.</p> <p>Conclusion</p> <p>Our findings suggest that grammatical relations play an important role in the interaction extraction task. Moreover, the net advantage of adding lexical and shallow syntactic features is small related to the number of added features. This implies that efficient classifiers can be built by using only a small fraction of the features that are typically being used in recent approaches.</p

DEEPER: a full parsing based approach to protein relation extraction

Lexical variance in biomedical texts poses a challenge to automatic protein relation mining. We therefore propose a new approach that relies only on more general language structures such as parsing and dependency information for the construction of feature vectors that can be used by standard machine learning algorithms in deciding whether a sentence describes a protein interaction or not. As our approach is not dependent on the use of specific interaction keywords, it is applicable to heterogeneous corpora. Evaluation on benchmark datasets shows that our method is competitive with existing state-of-the-art algorithms for the extraction of protein interactions

Modeling protein interaction networks with answer set programming

Abstract—In this paper we propose the use of answer set programming (ASP) to model protein interaction networks. We argue that this declarative formalism rivals the popular boolean networks in terms of ease of use, while at the same time being more expressive. As we demonstrate for the particular case of a fission yeast network, all information present in a boolean network, as well as relevant background assumptions, can be expressed explicitly in an answer set program. Moreover, readily available answer set solvers can then be used to find the stable states of the network. Keywords-biological networks, logic programming, systems biology, stable state I