We address collaborative decision in the Multi-Agent Consistency-based online learning of relational action models. This framework considers a community of agents, each of them learning and rationally acting following their relational action model. It relies on the idea that when agents communicate, on a utility basis, the observed effect of past actions to other agents, this results in speeding up the online learning process of each agent in the community. In the present article, we discuss how collaboration in this framework can be extended to the individual decision level. More precisely, we first discuss how an agent's ability to predict the effect of some action in its current state is enhanced when it takes into account all the action models in the community. Secondly, we consider the situation in which an agent fails to produce a plan using its own action model, and show how it can interact with the other agents in the community in order to select an appropriate action to perform. Such a community aided action selection strategy will help the agent revise its action model and increase its ability to reach its current goal as well as future ones.

Multi Agent Relational Action Learning considers a community of agents, each rationally acting following some relational action model. The observed effect of past actions that led an agent to revise its action model can be communicated, upon request, to another agent, speeding up its own revision. We present a frame-work for such collaborative relational action model revision.

Reconstruction of large scale gene regulatory networks (GRNs in the following) is an important step for understanding the complex regulatory mechanisms within the cell. Many modeling approaches have been introduced to find the causal relationship between genes using expression data. However, they have been suffering from high dimensionality-large number of genes but a small number of samples, overfitting, heavy computation time and low interpretability. We have previously proposed an algorithm LICORN, which uses the discrete expression data to find cooperative regulation relationships that are out of the scope of most GRN inference methods. However, as many other methods, LICORN suffers from a large number of false positives. We propose here a hybrid inference method H-Licorn that combines Licorn with a numerical selection step, expressed as a linear regression problem, that effectively complements the discrete search of Licorn. We evaluate a bootstrapped version of H-LICORN on the in silico DREAM5 dataset and show that H-LICORN has significantly higher performance than LICORN, and is competitive or outperforms state of the art GRN inference algorithms, especially when operating on small data sets. We also applied H-LICORN on a real dataset of human bladder cancer and show that it performs better than other methods in finding candidate regulatory interactions. In particular, solely based on gene expression data, H-LICORN is able to identify experimentally validated regulator cooperative relationships involved in cancer.

Reconstruction of gene regulatory networks (GRNs) is an important step for understanding the complex regulatory mechanisms within the cell. Many modeling approaches have been introduced to find the causal relationship between genes using expression data. However, they have been suffering from high dimensionality - large number of genes but a small number of samples -, over fitting, and heavy computation time. In this work 1, we present a novel method, namely SETNET, to improve the stability and accuracy of GRN inference using ensemble techniques. For a given target gene, SETNET extract an ensemble of regulation networks from discretized expression data instead of a single one. Inferred networks are then assessed by ranking individual regulation relationships using a regression based technique and continuous expression data. Evaluation on DREAM5 data demonstrates that SETNET is efficient, specially when operating on a small data set.

Modeling and analyzing networks is a major emerging topic in different research areas, such as computational biology, social science, document retrieval and social web applications.By connecting objects, it is possible to obtain an intuitive and global view of the relationships among components of a complex system.Nowadays, scientific communities have access to huge volume of network-structured data, such as social networks, gene/proteins/metabolic networks, sensor networks, and peer-to-peer networks.Often, data is collected at different time points allowing capturing a dynamic trend of the observed network.Consequently, the time component plays a key role in the comprehension of the evolutionary behavior of the studied network (evolution of the network structure and/or of flows within the system).Time can help to determine the real causal relationships within, for instance, gene activations, link creation, and information flow.Handling such data is a major challenge for current research in machine learning and data mining, and it has led to the development of recent innovative techniques that consider complex/multi-level networks, time-evolving graphs, heterogeneous information (nodes and links), and requires scalable algorithms that are able to manage large-scale complex networks.This special issue is the follow-up of the Dynamic Networks and Knowledge Discovery workshop (DyNaK) 1 that has been held in conjunction to ECML-PKDD 2011 at Barcelona on September 24th 2011.The workshop was motivated by the interest of providing a meeting point for scientists with different backgrounds who are interested in the study of large-scale dynamic complex networks.The workshop has attracted 18 submissions out of which 9 papers has been accepted.The workshop has gathered more than 30 participants and was also the host of three highly appreciated invited keynotes and one industrial talk.Building on the success of the DyNaK workshop, an open call for papers has been issued for this special issue, focusing on the major topic discussed in the workshop: analyzing, modeling and mining large-scale real network.15 high quality papers have been received; each of which has been reviewed by three reviewers.Only 7 contributions were finally selected.These contributions show the vitality of the field: a broad panel of techniques are applied to modeling the dynamics of complex systems, using a wide set of formalisms ranging from descriptive rules to Probabilistic Real-Time Automata.Application fields are also wide: vision, opinion diffusion in social network, business process modeling and text mining.In Internal link prediction: a new approach for predicting links in bipartite graphs, Allali et al. present an algorithm for predicting internal link in bipartite graph.They address the problem of predicting

Model transformation in the context of Model-Driven Data Warehouse is ensured by human experts. It generates an exorbitant cost and requires high proficiency. We propose in this paper a machine learning approach to reduce the expert contribution in the transformation process. We propose to express the model transformation problem as an Inductive Logic Programming one and to use existing project traces to find the best business transformation rules. We used the Aleph ILP system to learn such rules. Obtained results show that found rules are close to expert ones. Within our application context, we need to deal with several dependent concepts. Taking into account work in Layered Learning, we propose a new methodology that automatically updates the background knowledge of the concepts to be learned. Experimental results support the conclusion that this approach is suitable to solve this kind of problem.

A general task in data mining consists in finding all rectangles of 1 in a boolean matrix in which the order of the rows and columns is not important. However, most algorithms which have been developed to solve this task are unable to be adapted to real data that may contain noise. The effect of the noise is to shatter relevant item sets into a set of small irrelevant item sets, yielding an explosion in the number of resulting item sets. Recent algorithms that have been proposed to address this problem suffer from various limitations such as the large number of results, the execution time which remains very high and the inability to discover overlapping patterns. In this work, we propose a new heuristic approach based on a graph algorithm for the efficient extraction of item set patterns in noisy binary contexts. This method is based on maximal flow/minimal cut algorithms to find dense sub graphs of 1 in the graph associated to the boolean data matrix. To evaluate our approach, various experiments have been performed on both synthetic data and real datasets from bioinformatic applications. We have compared our results on various synthetic datasets and a gene-expression data with various methods and demonstrate that i) our method is quite efficient ii) the patterns extracted by our algorithm have a better quality than the other methods.

Collaborative filtering has been extensively studied in the context of ratings prediction. However, industrial recommender systems often aim at predicting a few items of immediate interest to the user, typically products that (s)he is likely to buy in the near future. In a collaborative filtering setting, the prediction may be based on the user's purchase history rather than rating information, which may be unreliable or unavailable. In this paper, we present an experimental evaluation of various collaborative filtering algorithms on a real-world dataset of purchase history from customers in a store of a French home improvement and building supplies chain. These experiments are part of the development of a prototype recommender system for salespeople in the store. We show how different settings for training and applying the models, as well as the introduction of domain knowledge may dramatically influence both the absolute and the relative performances of the different algorithms. To the best of our knowledge, the influence of these parameters on the quality of the predictions of recommender systems has rarely been reported in the literature.

This work copes with the problem of link prediction in large-scale two-mode social networks. Two variations of the link prediction tasks are studied: predicting links in a bipartite graph and predicting links in a unimodal graph obtained by the projection of a bipartite graph over one of its node sets. For both tasks, we show in an empirical way, that taking into account the bipartite nature of the graph can enhance substantially the performances of prediction models we learn. This is achieved by introducing new variations of topological atttributes to measure the likelihood of two nodes to be connected. Our approach, for both tasks, consists in expressing the link prediction problem as a two class discrimination problem. Classical supervised machine learning approaches can then be applied in order to learn prediction models. Experimental validation of the proposed approach is carried out on two real data sets: a co-authoring network extracted from the DBLP bibliographical database and bipartite graph history of transactions on an on-line music e-commerce site.

In the Relational Reinforcement learning framework, we propose an algorithm that learns an action model allowing to predict the resulting state of each action in any given situation. The system incrementally learns a set of first order rules: each time an example contradicting the current model (a counter-example) is encountered, the model is revised to preserve coherence and completeness, by using data-driven generalization and specialization mechanisms. The system is proved to converge by storing counter-examples only, and experiments on RRL benchmarks demonstrate its good performance w.r.t state of the art RRL systems.

In this work we tackle the problem of link prediction in co-authoring network. We apply a topological dyadic supervised machine learning approach for that purpose. A co-authoring network is actually obtained by the projection of a two-mode graph (an authoring graph linking authors to publications they have signed) over the authors set. We show that link prediction performances can be substantially enhanced by analyzing not only the co-authoring network, but also the dual graph obtained by projecting the original two-mode network over the set of publications.

MOTIVATION: One of the most challenging tasks in the post-genomic era is the reconstruction of transcriptional regulation networks. The goal is to identify, for each gene expressed in a particular cellular context, the regulators affecting its transcription, and the co-ordination of several regulators in specific types of regulation. DNA microarrays can be used to investigate relationships between regulators and their target genes, through simultaneous observations of their RNA levels. RESULTS: We propose a data mining system for inferring transcriptional regulation relationships from RNA expression values. This system is particularly suitable for the detection of cooperative transcriptional regulation. We model regulatory relationships as labelled two-layer gene regulatory networks, and describe a method for the efficient learning of these bipartite networks from discretized expression data sets. We also evaluate the statistical significance of such inferred networks and validate our methods on two public yeast expression data sets. AVAILABILITY: http://www.lri.fr/~elati/licorn.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Microarray-based CGH (Comparative Genomic Hybridization), transcriptome arrays and other large-scale genomic technologies are now routinely used to generate a vast amount of genomic profiles. Exploratory analysis of this data is crucial in helping to understand the data and to help form biological hypotheses. This step requires visualization of the data in a meaningful way to visualize the results and to perform first level analyses. RESULTS: We have developed a graphical user interface for visualization and first level analysis of molecular profiles. It is currently in use at the Institut Curie for cancer research projects involving CGH arrays, transcriptome arrays, SNP (single nucleotide polymorphism) arrays, loss of heterozygosity results (LOH), and Chromatin ImmunoPrecipitation arrays (ChIP chips). The interface offers the possibility of studying these different types of information in a consistent way. Several views are proposed, such as the classical CGH karyotype view or genome-wide multi-tumor comparison. Many functionalities for analyzing CGH data are provided by the interface, including looking for recurrent regions of alterations, confrontation to transcriptome data or clinical information, and clustering. Our tool consists of PHP scripts and of an applet written in Java. It can be run on public datasets at http://bioinfo.curie.fr/vamp AVAILABILITY: The VAMP software (Visualization and Analysis of array-CGH,transcriptome and other Molecular Profiles) is available upon request. It can be tested on public datasets at http://bioinfo.curie.fr/vamp. The documentation is available at http://bioinfo.curie.fr/vamp/doc.

MOTIVATION: The identification of recurrent genomic alterations can provide insight into the initiation and progression of genetic diseases, such as cancer. Array-CGH can identify chromosomal regions that have been gained or lost, with a resolution of approximately 1 mb, for the cutting-edge techniques. The extraction of discrete profiles from raw array-CGH data has been studied extensively, but subsequent steps in the analysis require flexible, efficient algorithms, particularly if the number of available profiles exceeds a few tens or the number of array probes exceeds a few thousands. RESULTS: We propose two algorithms for computing minimal and minimal constrained regions of gain and loss from discretized CGH profiles. The second of these algorithms can handle additional constraints describing relevant regions of copy number change. We have validated these algorithms on two public array-CGH datasets. AVAILABILITY: From the authors, upon request. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.