Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers

Building on recent progress at the intersection of combinatorial optimization and deep learning, we propose an end-to-end trainable architecture for deep graph matching that contains unmodified combinatorial solvers. Using the presence of heavily optimized combinatorial solvers together with some improvements in architecture design, we advance state-of-the-art on deep graph matching benchmarks for keypoint correspondence. In addition, we highlight the conceptual advantages of incorporating solvers into deep learning architectures, such as the possibility of post-processing with a strong multi-graph matching solver or the indifference to changes in the training setting. Finally, we propose two new challenging experimental setups. The code is available at https://github.com/martius-lab/blackbox-deep-graph-matchingComment: ECCV 2020 conference pape

Proceedings of the Salford Postgraduate Annual Research Conference (SPARC) 2011

These proceedings bring together a selection of papers from the 2011 Salford Postgraduate Annual Research Conference(SPARC). It includes papers from PhD students in the arts and social sciences, business, computing, science and engineering, education, environment, built environment and health sciences. Contributions from Salford researchers are published here alongside papers from students at the Universities of Anglia Ruskin, Birmingham City, Chester,De Montfort, Exeter, Leeds, Liverpool, Liverpool John Moores and Manchester

PROPER: global protein interaction network alignment through percolation matching

Background The alignment of protein-protein interaction (PPI) networks enables us to uncover the relationships between different species, which leads to a deeper understanding of biological systems. Network alignment can be used to transfer biological knowledge between species. Although different PI-network alignment algorithms were introduced during the last decade, developing an accurate and scalable algorithm that can find alignments with high biological and structural similarities among PPI networks is still challenging. Results In this paper, we introduce a new global network alignment algorithm for PPI networks called PROPER. Compared to other global network alignment methods, our algorithm shows higher accuracy and speed over real PPI datasets and synthetic networks. We show that the PROPER algorithm can detect large portions of conserved biological pathways between species. Also, using a simple parsimonious evolutionary model, we explain why PROPER performs well based on several different comparison criteria. Conclusions We highlight that PROPER has high potential in further applications such as detecting biological pathways, finding protein complexes and PPI prediction. The PROPER algorithm is available at http://proper.epfl.ch

An enhanced evolutionary algorithm for detecting complexes in protein interaction networks with heuristic biological operator

Detecting complexes in protein interaction networks is one of the most important topics of current computational biology research due to its prominent role in predicting functions of yet uncharacterized proteins and in diseases diagnosis. Evolutionary Algorithms (EAs) have been adopted recently to identify significant protein complexes. Conductance, expansion, normalized cut, modularity, and internal density are some well-known examples of complex detection models. In spite of the improvements and the robustness of predictive functions introduced by complex detection models based on EA and regardless of the general topological properties of protein interaction networks, inherent biological data of protein complexes has not, or rarely exploited and incorporated inside the methods as a specific heuristic operator. The aim of this operator is to guide the search process towards discovering hyper-connected and biologically related complexes by allowing a more effective exploration of the state space of possible solutions. Thus, the main contribution of this study is to develop a heuristic biological operator based on Gene Ontology (GO) annotations where it can serve as a local-common optimization approach. In the experiments, the performance of eight EA-based complex detection models has analyzed when applied on the yeast protein networks that are publicly available. The results give a clear argument for the positive effect of the proposed heuristic biological operator to considerably enhance the reliability of the current state-of-the-art optimization models