Describing the orthology signal in a PPI network at a functional, complex level

In recent work, stable evolutionary signal induced by orthologous proteins has been observed in a Yeast protein-protein interaction (PPI) network. This finding suggests more connected subgraphs of a PPI network to be potential mediators of evolutionary information. Because protein complexes are also likely to be present in such subgraphs, it is interesting to characterize the bias of the orthology signal on the detection of putative protein complexes. To this aim, we propose a novel methodology for quantifying the functionality of the orthology signal in a PPI network at a protein complex level. The methodology performs a differential analysis between the functions of those complexes detected by clustering a PPI network using only proteins with orthologs in another given species, and the functions of complexes detected using the entire network or sub-networks generated by random sampling of proteins. We applied the proposed methodology to a Yeast PPI network using orthology information from a number of different organisms. The results indicated that the proposed method is capable to isolate functional categories that can be clearly attributed to the presence of an evolutionary (orthology) signal and quantify their distribution at a fine-grained protein level

Off-Policy Action Anticipation in Multi-Agent Reinforcement Learning

Learning anticipation in Multi-Agent Reinforcement Learning (MARL) is a reasoning paradigm where agents anticipate the learning steps of other agents to improve cooperation among themselves. As MARL uses gradient-based optimization, learning anticipation requires using Higher-Order Gradients (HOG), with so-called HOG methods. Existing HOG methods are based on policy parameter anticipation, i.e., agents anticipate the changes in policy parameters of other agents. Currently, however, these existing HOG methods have only been applied to differentiable games or games with small state spaces. In this work, we demonstrate that in the case of non-differentiable games with large state spaces, existing HOG methods do not perform well and are inefficient due to their inherent limitations related to policy parameter anticipation and multiple sampling stages. To overcome these problems, we propose Off-Policy Action Anticipation (OffPA2), a novel framework that approaches learning anticipation through action anticipation, i.e., agents anticipate the changes in actions of other agents, via off-policy sampling. We theoretically analyze our proposed OffPA2 and employ it to develop multiple HOG methods that are applicable to non-differentiable games with large state spaces. We conduct a large set of experiments and illustrate that our proposed HOG methods outperform the existing ones regarding efficiency and performance

A methodology for detecting the orthology signal in a PPI network at a functional complex level

Contains fulltext : 93730.pdf (preprint version ) (Open Access)13 p

Model-free inverse reinforcement learning with multi-intention, unlabeled, and overlapping demonstrations

In this paper, we define a novel inverse reinforcement learning (IRL) problem where the demonstrations are multi-intention, i.e., collected from multi-intention experts, unlabeled, i.e., without intention labels, and partially overlapping, i.e., shared between multiple intentions. In the presence of overlapping demonstrations, current IRL methods, developed to handle multi-intention and unlabeled demonstrations, cannot successfully learn the underlying reward functions. To solve this limitation, we propose a novel clustering-based approach to disentangle the observed demonstrations and experimentally validate its advantages. Traditional clustering-based approaches to multi-intention IRL, which are developed on the basis of model-based Reinforcement Learning (RL), formulate the problem using parametric density estimation. However, in high-dimensional environments and unknown system dynamics, i.e., model-free RL, the solution of parametric density estimation is only tractable up to the density normalization constant. To solve this, we formulate the problem as a mixture of logistic regressions to directly handle the unnormalized density. To research the challenges faced by overlapping demonstrations, we introduce the concepts of shared pair, which is a state-action pair that is shared in more than one intention, and separability, which resembles how well the multiple intentions can be separated in the joint state-action space. We provide theoretical analyses under the global optimality condition and the existence of shared pairs. Furthermore, we conduct extensive experiments on four simulated robotics tasks, extended to accept different intentions with specific levels of separability, and a synthetic driver task developed to directly control the separability. We evaluate the existing baselines on our defined problem and demonstrate, theoretically and experimentally, the advantages of our clustering-based solution, especially when the separability of the demonstrations decreases

Correction to: Model-free inverse reinforcement learning with multi-intention, unlabeled, and overlapping demonstrations

Correction to: Machine Learning https://doi.org/10.1007/s10994-022-06273-x There are two mistakes in the published article: 1. One of the references in the manuscript is incorrect. Here is the incorrect reference: “Bighashdel, A., Meletis, P., Jancura, P., & Dubbelman, G. (2021). In Proceeding of Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 206–221).” and here is the corrected reference: “Bighashdel, A., Meletis, P., Jancura, P., & Dubbelman, G. (2021). Deep Adaptive Multi-Intention Inverse Reinforcement Learning. In Proceedings of Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 206– 221).” 2. There is a missing character “i” in equation 3 of the published manuscript. Here is the incorrect equation:(Formula Presented.) and here is the corrected equation: (Formula Presented) The character “i” is missing in the term “ (a|s) ”. The corrected term is “ (a|s, i)”. The original article has been corrected

Dividing protein interaction networks by growing orthologous articulations. IR-BIO-002, Available at http://www.cs.vu.nl/˜elena/diva.pdf

Abstract. The increasing growth of data on protein-protein interaction (PPI) networks has boosted research on their comparative analysis. In particular, recent studies proposed models and algorithms for performing network alignment, the comparison of networks across species for discovering conserved modules. Common approaches for this task construct a merged representation of the considered networks, called alignment graph, and search the alignment graph for conserved networks of interest using greedy techniques. In this paper we propose a modular approach to this task. First, each network to be compared is divided into small subnets which are likely to contain conserved modules. To this aim, we develop an algorithm for dividing PPI networks that combines a graph theoretical property(articulation) with a biological one (orthology). Next, network alignment is performed on pairs of resulting subnets from different species. We tackle this task by means of a state-of-the-art alignment graph model for constructing alignment graphs, and an exact algorithm for searching in the alignment graph. Results of experiments show the ability of this approach to discover accurate conserved modules, and substantiate the importance of the notions of orthology and articulation for performing comparative network analysis in a modular fashion. Key words: Protein network dividing, modular network alignment.

DEEN:a simple and fast algorithm for network community detection

\u3cp\u3eThis paper introduces an algorithm for network community detection called DEEN (Delete Edges and Expand Nodes) consisting of two simple steps. First edges of the graph estimated to connect different clusters are detected and removed, next the resulting graph is used for generating communities by expanding seed nodes. DEEN uses as parameters the minimum and maximum allowed size of a cluster, and a resolution parameter whose value influences the number of removed edges. Application of DEEN to the budding yeast protein network for detecting functional protein complexes indicates its capability to identify clusters containing proteins with the same functional category, improving on MCL, a popular state-of-the-art method for functional protein complex detection. Moreover, application of DEEN to two popular benchmark networks results in the detection of accurate communities, substantiating the effectiveness of the proposed method in diverse domains.\u3c/p\u3