Graph Community Infomax

Graph representation learning aims at learning low-dimension representations for nodes in graphs, and has been proven very useful in several downstream tasks. In this article, we propose a new model, Graph Community Infomax (GCI), that can adversarial learn representations for nodes in attributed networks. Different from other adversarial network embedding models, which would assume that the data follow some prior distributions and generate fake examples, GCI utilizes the community information of networks, using nodes as positive(or real) examples and negative(or fake) examples at the same time. An autoencoder is applied to learn the embedding vectors for nodes and reconstruct the adjacency matrix, and a discriminator is used to maximize the mutual information between nodes and communities. Experiments on several real-world and synthetic networks have shown that GCI outperforms various network embedding methods on community detection tasks.

Discovering cancer driver genes and pathways using stochastic block model graph neural networks

The identification of genes and pathways responsible for the transformation of normal cellsinto malignant ones represents a pivotal step to understand the aetiology of cancer, to characterise progression and relapse, and to ultimately design targeted therapies. The advent of high-throughput omic technologies has enabled the discovery of a significant number of cancer driver genes, but recent genomic studies have shown these to be only necessary but not sufficient to trigger tumorigenesis. Since most biological processes are the results of the interaction of multiple genes, it is then conceivable that tumorigenesis is likely the result of the action of networks of cancer driver and non-driver genes. Here we take advantage of recent advances in graph neural networks, combined with well established statistical models of network structure, to build a new model, called Stochastic Block Model Graph Neural Network (SBM-GNN), which predicts cancer driver genes and cancer mediating pathways directly from high-throughput omic experiments. Experimental analysis of synthetic datasets showed that our model can correctly predict genes associated with cancer and recover relevant pathways, while outperforming other state-of-the-art methods. Finally, we used SBM-GNN to perform a pan-cancer analysis, where we found genes and pathways directly involved with the hallmarks of cancer controlling genome stability, apoptosis, immune response, and metabolism.

Exact transcript quantification over splice graphs

Background The probability of sequencing a set of RNA-seq reads can be directly modeled using the abundances of splice junctions in splice graphs instead of the abundances of a list of transcripts. We call this model graph quantification, which was first proposed by Bernard et al. (Bioinformatics 30:2447–55, 2014). The model can be viewed as a generalization of transcript expression quantification where every full path in the splice graph is a possible transcript. However, the previous graph quantification model assumes the length of single-end reads or paired-end fragments is fixed. Results We provide an improvement of this model to handle variable-length reads or fragments and incorporate bias correction. We prove that our model is equivalent to running a transcript quantifier with exactly the set of all compatible transcripts. The key to our method is constructing an extension of the splice graph based on Aho-Corasick automata. The proof of equivalence is based on a novel reparameterization of the read generation model of a state-of-art transcript quantification method. Conclusion We propose a new approach for graph quantification, which is useful for modeling scenarios where reference transcriptome is incomplete or not available and can be further used in transcriptome assembly or alternative splicing analysis.

Category-Theoretic Formulation of Model-Based Systems Architecting: The Concept-Model-Graph-View-Concept Transformation Cycle

We introduce the Concept-Model-Graph-View-Concept (CMGVC) transformation cycle. The CMGVC cycle facilitates coherent architecture analysis, reasoning, insight, and decision-making based on conceptual models that are transformed into a common, robust graph data structure (GDS). The GDS is then transformed into multiple views on the model, which inform stakeholders in various ways. This GDS-based approach decouples the view from the model and constitutes a powerful enhancement of model-based systems engineering (MBSE). CMGVC applies the rigorous foundations of Category Theory, a mathematical framework of representations and transformations. The CMGVC architecture is superior to direct transformations and language-coupled common representations. We demonstrate the CMGVC cycle to transform a conceptual system architecture model built with the Object Process Modeling Language (OPM) into dual graphs and a decision support matrix (DSM) that stimulates system architecture insight.

Automatic text translation and world model graph

Построение модели мира производится автоматически на основе обработки массива пар текстов на входном и выходном языках. В основе лежит представление ситуации в виде подграфа общей модели мира на входном языке. По этому подграфу выбирается подграф на выходном языке. Этот подграф преобразуется в выходной текст.

Development of a relevance index for the subjects in a study plan - Case study: Systems and Computer Engineering, Universidad Tecnológica de Pereira

This document presents the development of an index that aims to quantify, according to some criteria known in graph theory, how relevant a subject is, taking into account its location in the curriculum, its number of credits, its prerequisites and the subjects dependents. The first thing was to model the academic plan using a graph, which considers only two things: the assigned credits and the prerequisites that must be met before taking the subjects. After having this model, graph theory algorithms were applied that allow to measure the importance of a subject with respect to the location in its curricular mesh (Centrality) and allow to give a measure of the importance of the subjects based on academic credits, its prerequisites and subjects depending on it (Neighborhood). It is important to note that the analysis presented is not intended to indicate that one subject is more important than another for the student's professional development, but rather to analyze, in an estimative way, which subjects contribute more to the connectivity of the program and academic flow by this network only taking into account the information found in the curriculum.The result obtained is a composite index, which allows visualizing the relevance degree of the subjects in the study plan.