Hemicubane topological analogs of the oxygen-evolving complex of photosystem II mediating water-assisted propylene carbonate oxidation

A series of Ca-Mn clusters with the ligand 2-pyridinemethoxide (Py-CH2O) have been prepared with varying degrees of topological similarity to the biological oxygen-evolving complex. These clusters activate water as a...

Gas phase ion chemistry of titanium–oxofullerene with ligated solvents

Gas phase fragmentation events of fullerene-like titanium oxo-cluster anions were investigated in detail. The fragmentation channel of the ions was comparable to the fragmentation of C60 ions with systematic C2 losses which is a consequence of topological similarity.

Multiplex Embedding of Biological Networks Using Topological Similarity of Different Layers

Network embedding techniques, which provide low dimensional representations of the nodes in a network, have been commonly applied to many machine learning problems in computational biology. In most of these applications, multiple networks (e.g., different types of interactions/associations or semantically identical networks that come from different sources) are available. Multiplex network embedding aims to derive strength from these data sources by integrating multiple networks with a common set of nodes. Existing approaches to this problem treat all layers of the multiplex network equally while performing integration, ignoring the differences in the topology and sparsity patterns of different networks. Here, we formulate an optimization problem that accounts for inner-network smoothness, intra-network smoothness, and topological similarity of networks to compute diffusion states for each network. To quantify the topological similarity of pairs of networks, we use Gromov-Wasserteins discrepancy. Finally, we integrate the resulting diffusion states and apply dimensionality reduction (singular value decomposition after log-transformation) to compute node embeddings. Our experimental results in the context of drug repositioning and drug-target prediction show that the embeddings computed by the resulting algorithm, Hattusha, consistently improve predictive accuracy over algorithms that do not take into account the topological similarity of different networks.

Topological Similarity-Based Multi-Target Correlation Localization for Aerial-Ground Systems

In this paper, an algorithm for solving the multi-target correlation and co-location problem of aerial-ground heterogeneous system is investigated. Aiming at the multi-target correlation problem, the fusion algorithm of visual axis correlation method and improved topological similarity correlation method are adopted in view of large parallax and inconsistent scale between the aerial and ground perspectives. First, the visual axis was preprocessed by the threshold method, so that the sparse targets were initially associated. Then, the improved topological similarity method was used to further associate dense targets with the relative position characteristics between targets. The shortcoming of dense target similarity with small difference was optimized by the improved topological similarity method. For the problem of co-location, combined with the multi-target correlation algorithm in this paper, the triangulation positioning model was used to complete the co-location of multiple targets. In the experimental part, simulation experiments and flight experiments were designed to verify the effectiveness of the algorithm. Experimental results show that the proposed algorithm can effectively achieve multi-target correlation positioning, and that the positioning accuracy is obviously better than other positioning methods.

Link Prediction Based on the Derivation of Mapping Entropy

The algorithms based on topological similarity play an important role in link prediction. However, most of traditional algorithms based on the influences of nodes only consider the degrees of the endpoints which ignore the differences in contribution of neighbors. Through generous explorations, we propose the DME (derivation of mapping entropy) model concerning the mapping relationship between the node and its neighbors to access the influence of the node appropriately. Abundant experiments on nine real networks suggest that the model can improve precision in link prediction and perform better than traditional algorithms obviously with no increase in time complexity.

Can charge-reversal be considered as a strategy for attaining thermal stability in proteins?

Do charge reversal mutations (CRM) naturally occur in mesophilic-thermophilic/hyperthermophilic (M-T/HT) orthologous proteins? Do they contribute to thermal stability by altering charge-charge interactions? A careful investigation on 1550 M-T/HT orthologous protein pairs with remarkable structural and topological similarity extracts the role of buried and partially exposed CRMs in enhancing thermal stability. Our findings could assist in engineering thermo-stable variants of proteins.

MONACO: accurate biological network alignment through optimal neighborhood matching between focal nodes

Motivation Alignment of protein–protein interaction networks can be used for the unsupervised prediction of functional modules, such as protein complexes and signaling pathways, that are conserved across different species. To date, various algorithms have been proposed for biological network alignment, many of which attempt to incorporate topological similarity between the networks into the alignment process with the goal of constructing accurate and biologically meaningful alignments. Especially, random walk models have been shown to be effective for quantifying the global topological relatedness between nodes that belong to different networks by diffusing node-level similarity along the interaction edges. However, these schemes are not ideal for capturing the local topological similarity between nodes. Results In this article, we propose MONACO, a novel and versatile network alignment algorithm that finds highly accurate pairwise and multiple network alignments through the iterative optimal matching of ‘local’ neighborhoods around focal nodes. Extensive performance assessment based on real networks as well as synthetic networks, for which the ground truth is known, demonstrates that MONACO clearly and consistently outperforms all other state-of-the-art network alignment algorithms that we have tested, in terms of accuracy, coherence and topological quality of the aligned network regions. Furthermore, despite the sharply enhanced alignment accuracy, MONACO remains computationally efficient and it scales well with increasing size and number of networks. Availability and implementation Matlab implementation is freely available at https://github.com/bjyoontamu/MONACO. Supplementary information Supplementary data are available at Bioinformatics online.

Weighted synthetical influence of degree and H-index in link prediction of complex networks

Link prediction based on traditional models have attracted many interests recently. Among all models, the ones based on topological similarity have achieved great success. However, researchers pay more attention to links, but less to endpoint influence. After profound investigation, we find that the synthesis of degree and H-index plays an important role in modeling endpoint influence. So, in this paper, we propose link prediction models based on weighted synthetical influence, exploring the role of H-index and degree in endpoint influence measurement. Experiments on 12 real-world networks show that the proposed models can provide higher accuracy.