In this paper we present an algorithm to search for functional modules in a database storing the protein-protein interactions of a given organism. Proteinprotein interactions are modelled by an indirect graph where nodes represent proteins and edges represent interactions. The algorithm works on the adjacency matrix associated with such a graph, and the concept of quality of a sub-matrix is int...

Polyethylene glycol (PEG) is a non-ionic water soluble polymer that is used as an additive in protein solutions to induce protein crystallization. PEG in dilute solution concentrations and within a molecular weight range, induces an attractive protein-protein interaction. However the strength of this attractive proteinprotein interaction weakens with increasing PEG concentration and molecular w...

Recently, the structural controllability theory has been introduced to analyze the ProteinProtein Interaction (PPI) network. The indispensable nodes, which their removal increase the number of driver nodes to control the network, are found essential in PPI network. However, the PPI network is far from complete and there may exist many false-positive or false-negative interactions, which promote...

In systems biology, network motif is defined as a frequent and unique subgraph pattern in a network and has been applied in various biological and medical problems. However, identifying network motifs is intrinsically very costly, which restricts extensive and exhaustive experiments for real problems. Furthermore, most algorithms and tools provide limited information regarding network motifs, w...

In Ge et al. (2001) the authors consider an interesting question. They assemble gene expression data from a yeast cell-cycle experiment (Cho et al., 1998), literature proteinprotein interaction (PPI) data and yeast two-hybrid data. We have curated the data slightly to make it simpler to carry out the analyses reported in Ge et al. (2001). In particular we reduced the data to the 2885 genes that...

We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations. By stacking layers in which nodes are able to attend over their neighborhoods’ features, we enable (implicitly) specifying different weight...

Computational methods to predict proteinprotein interactions are becoming increasingly important. This is due to the fact that most of the interactions data have been identified by highthroughput technologies like the yeast two-hybrid system which are known to yield many false positives. In this paper we investigate the use of two protein sequence features, namely, domain structure and hydropho...

In living cells, proteins are dynamically connected through biochemical reactions, so its functional features are properly encoded into proteinprotein interaction networks (PINs). Up to present, many efforts have been devoted to exploring the basic feature of PINs. However, it is still a challenging problem to explore a universal property of PINs. Here we employed the complex networks theory to...

Prediction of protein complexes is a crucial problem in computational biology. The increasing amount of available genomic data can enhance the identification of protein complexes. Here we describe an approach for predicting protein complexes based on integration of protein-protein interaction (PPI) data and protein functional annotation data. The basic idea is that proteins in protein complexes...

Background: To understand protein function, it is important to study proteinprotein interaction networks. These networks can be represented in network diagrams called protein interaction maps that can lead to better understanding by visualization. We address the problem of drawing of protein interactions in Kohn's Molecular Interaction Map (MIM) notation. Even though there are some existing too...