Motivation The identification of disease-gene associations is a task of fundamental importance in human health research. A typical approach consists in first encoding large gene/protein relational datasets as networks due to the natural and intuitive property of graphs for representing objects' relationships and then utilizing graph-based techniques to prioritize genes for successive low-throughput validation assays. Since different types of interactions between genes yield distinct gene networks, there is the need to integrate different heterogeneous sources to improve the reliability of prioritization systems. Results We propose an approach based on three phases: first, we merge all sources in a single network, then we partition the integrated network according to edge density introducing a notion of edge type to distinguish the parts and finally, we employ a novel node kernel suitable for graphs with typed edges. We show how the node kernel can generate a large number of discriminative features that can be efficiently processed by linear regularized machine learning classifiers. We report state-of-the-art results on 12 disease-gene associations and on a time-stamped benchmark containing 42 newly discovered associations. Availability and implementation Source code: https://github.com/dinhinfotech/DiGI.git. Supplementary information Supplementary data are available at Bioinformatics online.

Heterogeneous networks integration for disease-gene prioritization with node kernels.

Many of the current scientific advances in the life sciences have their origin in the intensive use of data for knowledge discovery. In no area this is so clear as in bioinformatics, led by technological breakthroughs in data acquisition technologies. It has been argued that bioinformatics could quickly become the field of research generating the largest data repositories, beating other data-intensive areas such as high-energy physics or astroinformatics. Over the last decade, deep learning has become a disruptive advance in machine learning, giving new live to the long-standing connectionist paradigm in artificial intelligence. Deep learning methods are ideally suited to large-scale data and, therefore, they should be ideally suited to knowledge discovery in bioinformatics and biomedicine at large. In this brief paper, we review key aspects of the application of deep learning in bioinformatics and medicine, drawing from the themes covered by the contributions to an ESANN 2018 special session devoted to this topic.

Bioinformatics and Medicine in the Era of Deep Learning

The notion of node similarity is key in many graph processing techniques and it is especially important in diffusion graph kernels. However, when the graph structure is affected by noise in the form of missing links, similarities are distorted proportionally to the sparsity of the graph and to the fraction of missing links. Here, we introduce the notion of link enrichment, that is, performing link prediction in order to improve the performance of diffusion-based kernels. We empirically show a robust and large effect for the combination of a number of link prediction and a number of diffusion kernel techniques on several gene-disease association problems.

Link Enrichment for Diffusion-Based Graph Node Kernels

We introduce a new class of graph neural networks (GNNs), by combining several concepts that were so far studied independently - graph kernels, attention-based networks with structural priors and more recently, efficient Transformers architectures applying small memory footprint implicit attention methods via low rank decomposition techniques. The goal of the paper is twofold. Proposed by us Graph Kernel Attention Transformers (or GKATs) are much more expressive than SOTA GNNs as capable of modeling longer-range dependencies within a single layer. Consequently, they can use more shallow architecture design. Furthermore, GKAT attention layers scale linearly rather than quadratically in the number of nodes of the input graphs, even when those graphs are dense, requiring less compute than their regular graph attention counterparts. They achieve it by applying new classes of graph kernels admitting random feature map decomposition via random walks on graphs. As a byproduct of the introduced techniques, we obtain a new class of learnable graph sketches, called graphots, compactly encoding topological graph properties as well as nodes' features. We conducted exhaustive empirical comparison of our method with nine different GNN classes on tasks ranging from motif detection through social network classification to bioinformatics challenges, showing consistent gains coming from GKATs.

Graph Kernel Attention Transformers

When dealing with machine learning on graphs, one of the most successfully approaches is the one of kernel methods. Depending if one is interested in predicting properties of graphs (e.g. graph classification) or to predict properties of nodes in a single graph (e.g. graph node classification), different kernel functions should be adopted. In the last few years, several kernels for graphs have been defined in literature that extract local features from the input graphs, obtaining both efficiency and state-of-the-art predictive performances. Recently, some work has been done in this direction also regarding graph node kernels, but the majority of the graph node kernels available in literature consider only global information, that can be not optimal for many tasks. In this paper, we propose a procedure that allows to transform a local graph kernel in a kernel for nodes in a single, huge graph. We apply a specific instantiation to the task of disease gene prioritization from the bioinformatics domain, improving the state of the art in many diseases.

DEEP: Decomposition Feature Enhancement Procedure for Graphs

Gene-disease associations are inferred on the basis of similarities between the proteins encoded by genes. Biological relationships used to define similarities range from interacting proteins, proteins that participate in pathways and protein expression profiles. Though graph kernel methods have become a prominent approach for association prediction, most solutions are based on a notion of information diffusion that does not capture the specificity of different network parts. Here we propose a graph kernel method that explicitly models the configuration of each gene’s context. An empirical evaluation on several biological databases shows that our proposal is competitive w.r.t. state-of-the-art kernel approaches.

The Conjunctive Disjunctive Node Kernel

A crucial computational task for relational and network data is the “link prediction problem” which allows for example to discover unknown interactions between proteins to explain the mechanism of a disease in biological networks, or to suggest novel products for a customer in a e-commerce recommendation system. Most link prediction approaches however do not effectively exploit the contextual information available in the neighborhood of each edge. Here we propose to cast the problem as a binary classification task over the union of the pair of subgraphs located at the endpoints of each edge. We model the classification task using a support vector machine endowed with an efficient graph kernel and achieve state-of-the-art results on several benchmark datasets.

Joint Neighborhood Subgraphs Link Prediction

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Papers

Link Enrichment for Diffusion-Based Graph Node Kernels

The Conjunctive Disjunctive Node Kernel

DEEP: Decomposition Feature Enhancement Procedure for Graphs

Joint Neighborhood Subgraphs Link Prediction