Humans inevitably develop a sense of the relationships between objects, some of which are based on their appearance. Some pairs of objects might be seen as being alternatives to each other (such as two pairs of jeans), while others may be seen as being complementary (such as a pair of jeans and a matching shirt). This information guides many of the choices that people make, from buying clothes to their interactions with each other. We seek here to model this human sense of the relationships between objects based on their appearance. Our approach is not based on fine-grained modeling of user annotations but rather on capturing the largest dataset possible and developing a scalable method for uncovering human notions of the visual relationships within. We cast this as a network inference problem defined on graphs of related images, and provide a large-scale dataset for the training and evaluation of the same. The system we develop is capable of recommending which clothes and accessories will go well together (and which will not), amongst a host of other applications.

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Image-Based Recommendations on Styles and Substitutes

Most approaches in predicting protein function from protein-protein interaction data utilize the observation that a protein often share functions with proteins that interacts with it (its level-1 neighbours). However, proteins that interact with the same proteins (i.e. level-2 neighbours) may also have a greater likelihood of sharing similar physical or biochemical characteristics. We speculate that two separate forms of functional association accounts for such a phenomenon, and a protein is likely to share functions with its level-1 and/or level-2 neighbours. We are interested to find out how significant is functional association between level-2 neighbours and how they can be exploited for protein function prediction.

We made a statistical study on recent interaction data and observed that functional association between level-2 neighbours is clearly observable. A substantial number of proteins are observed to share functions with level-2 neighbours but not with level-1 neighbours. We develop an algorithm that predicts the functions of a protein in two steps: (1) assign a weight to each of its level-1 and level-2 neighbours by estimating its functional similarity with the protein using the local topology of the interaction network as well as the reliability of experimental sources; (2) scoring each function based on its weighted frequency in these neighbours. Using leave-one-out cross validation, we compare the performance of our method against that of several other existing approaches and show that our method performs well.

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Exploiting indirect neighbours and topological weight to predict protein function from protein-protein interactions

Agulhas rings provide the principal route for ocean waters to circulate from the Indo-Pacific to the Atlantic basin. Their influence on global ocean circulation is well known, but their role in plankton transport is largely unexplored. We show that, although the coarse taxonomic structure of plankton communities is continuous across the Agulhas choke point, South Atlantic plankton diversity is altered compared with Indian Ocean source populations. Modeling and in situ sampling of a young Agulhas ring indicate that strong vertical mixing drives complex nitrogen cycling, shaping community metabolism and biogeochemical signatures as the ring and associated plankton transit westward. The peculiar local environment inside Agulhas rings may provide a selective mechanism contributing to the limited dispersal of Indian Ocean plankton populations into the Atlantic.

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Environmental characteristics of Agulhas rings affect interocean plankton transport

Given a real world graph, how should we lay-out its edges? How can we compress it? These questions are closely related, and the typical approach so far is to find clique-like communities, like the ‘cavemen graph’, and compress them. We show that the block-diagonal mental image of the ‘cavemen graph’ is the wrong paradigm, in full agreement with earlier results that real world graphs have no good cuts. Instead, we propose to envision graphs as a collection of hubs connecting spokes, with super-hubs connecting the hubs, and so on, recursively. Based on the idea, we propose the SlashBurn method to recursively split a graph into hubs and spokes connected only by the hubs. We also propose techniques to select the hubs and give an ordering to the spokes, in addition to the basic SlashBurn. We give theoretical analysis of the proposed hub selection methods. Our view point has several advantages: (a) it avoids the ‘no good cuts’ problem, (b) it gives better compression, and (c) it leads to faster execution times for matrix-vector operations, which are the back-bone of most graph processing tools. Through experiments, we show that SlashBurn consistently outperforms other methods for all data sets, resulting in better compression and faster running time. Moreover, we show that SlashBurn with the appropriate spokes ordering can further improve compression while hardly sacrificing the running time.

SlashBurn: Graph Compression and Mining beyond Caveman Communities

Motivation: Discovering and understanding patterns in networks of protein–protein interactions (PPIs) is a central problem in systems biology. Alignments between these networks aid functional understanding as they uncover important information, such as evolutionary conserved pathways, protein complexes and functional orthologs. A few methods have been proposed for global PPI network alignments, but because of NP-completeness of underlying sub-graph isomorphism problem, producing topologically and biologically accurate alignments remains a challenge.

Results: We introduce a novel global network alignment tool, Lagrangian GRAphlet-based ALigner (L-GRAAL), which directly optimizes both the protein and the interaction functional conservations, using a novel alignment search heuristic based on integer programming and Lagrangian relaxation. We compare L-GRAAL with the state-of-the-art network aligners on the largest available PPI networks from BioGRID and observe that L-GRAAL uncovers the largest common sub-graphs between the networks, as measured by edge-correctness and symmetric sub-structures scores, which allow transferring more functional information across networks. We assess the biological quality of the protein mappings using the semantic similarity of their Gene Ontology annotations and observe that L-GRAAL best uncovers functionally conserved proteins. Furthermore, we introduce for the first time a measure of the semantic similarity of the mapped interactions and show that L-GRAAL also uncovers best functionally conserved interactions. In addition, we illustrate on the PPI networks of baker's yeast and human the ability of L-GRAAL to predict new PPIs. Finally, L-GRAAL's results are the first to show that topological information is more important than sequence information for uncovering functionally conserved interactions.

Availability and implementation: L-GRAAL is coded in C++. Software is available at: http://bio-nets.doc.ic.ac.uk/L-GRAAL/.

Contact: ku.ca.lairepmi@ningod-dolam.n

Supplementary information: Supplementary data are available at Bioinformatics online.

L-GRAAL: Lagrangian graphlet-based network aligner.

The increasing availability and significance of large-scale protein-protein interaction ( ppi ) data has resulted in a flurry of research activity to comprehend the organization, processes, and functioning of cells by analyzing these data at network level. Network clustering , that analyzes the topological and functional properties of a ppi  network to identify clusters of interacting proteins, has gained significant popularity in the bioinformatics as well as data mining research communities. Many studies since the last decade have shown that clustering ppi  networks is an effective approach for identifying functional modules , revealing functions of unknown proteins, etc. In this paper, we examine this issue by classifying, discussing, and comparing a wide ranging approaches proposed by the bioinformatics community to cluster ppi  networks. A pervasive desire of this review is to emphasize the uniqueness of the network clustering problem in the context of ppi  networks and highlight why generic network clustering algorithms proposed by the data mining community cannot be directly adopted to address this problem effectively. We also review a closely related problem to ppi  network clustering, network summarization , which can enable us to make sense out of the information contained in large ppi  networks by generating multi-level functional summaries .

Clustering and Summarizing Protein-Protein Interaction Networks: A Survey

The availability of large-scale curated protein interaction datasets has given rise to the opportunity to investigate higher level organization and modularity within the protein interaction network (PPI) using graph theoretic analysis. Despite the recent progress, systems level analysis of PPIS remains a daunting task as it is challenging to make sense out of the deluge of high-dimensional interaction data. Specifically, techniques that automatically abstract and summarize PPIS at multiple resolutions to provide high level views of its functional landscape are still lacking. We present a novel data-driven and generic algorithm called FUSE (Fu nctional S ummary Ge nerator) that generates functional maps of a PPI at different levels of organization, from broad process-process level interactions to in-depth complex-complex level interactions, through a pro t maximization approach that exploits Minimum Description Length (MDL) principle to maximize information gain of the summary graph while satisfying the level of detail constraint. We evaluate the performance of FUSE on several real-world PPIS. We also compare FUSE to state-of-the-art graph clustering methods with GO term enrichment by constructing the biological process landscape of the PPIS. Using AD network as our case study, we further demonstrate the ability of FUSE to quickly summarize the network and identify many different processes and complexes that regulate it. Finally, we study the higher-order connectivity of the human PPI. By simultaneously evaluating interaction and annotation data, FUSE abstracts higher-order interaction maps by reducing the details of the underlying PPI to form a functional summary graph of interconnected functional clusters. Our results demonstrate its effectiveness and superiority over state-of-the-art graph clustering methods with GO term enrichment.

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FUSE: a profit maximization approach for functional summarization of biological networks

Motivation Given the growth of large-scale protein-protein interaction (PPI) networks obtained across multiple species and conditions, network alignment is now an important research problem. Network alignment performs comparative analysis across multiple PPI networks to understand their connections and relationships. However, PPI data in high-throughput experiments still suffer from significant false-positive and false-negatives rates. Consequently, high-confidence network alignment across entire PPI networks is not possible. At best, local network alignment attempts to alleviate this problem by completely ignoring low-confidence mappings; global network alignment, on the other hand, pairs all proteins regardless. To this end, we propose an alternative strategy: instead of full alignment across the entire network or completely ignoring low-confidence regions, we aim to perform highly specific protein-to-protein alignments where data confidence is high, and fall back on broader functional region-to-region alignment where detailed protein-protein alignment cannot be ascertained. The basic idea is to provide an alignment of multiple granularities to allow biological predictions at varying specificity. Results DualAligner performs dual network alignment, in which both region-to-region alignment, where whole subgraph of one network is aligned to subgraph of another, and protein-to-protein alignment, where individual proteins in networks are aligned to one another, are performed to achieve higher accuracy network alignments. Dual network alignment is achieved in DualAligner via background information provided by a combination of Gene Ontology annotation information and protein interaction network data. We tested DualAligner on the global networks from IntAct and demonstrated the superiority of our approach compared with state-of-the-art network alignment methods. We studied the effects of parameters in DualAligner in controlling the quality of the alignment. We also performed a case study that illustrates the utility of our approach. Availability and implementation http://www.cais.ntu.edu.sg/∼assourav/DualAligner/.

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DualAligner: A Dual Alignment-based Strategy to Align Protein Interaction Networks

Most existing tag-based social image search engines present search results as a ranked list of images, which cannot be consumed by users in a natural and intuitive manner. In this paper, we present a novel concept-preserving image search results summarization algorithm named Prism. Prism exploits both visual features and tags of the search results to generate high quality summary, which not only breaks the results into visually and semantically coherent clusters but it also maximizes the coverage of the summary w.r.t the original search results. It first constructs a visual similarity graph where the nodes are images in the search results and the edges represent visual similarities between pairs of images. This graph is optimally decomposed and compressed into a set of concept-preserving subgraphs based on a set of summarization objectives. Images in a concept-preserving subgraph are visually and semantically cohesive and are described by a minimal set of tags or concepts. Lastly, one or more exemplar images from each subgraph is selected to form the exemplar summary of the result set. Through empirical study, we demonstrate the effectiveness of Prism against state-of-the-art image summarization and clustering algorithms.

PRISM: concept-preserving social image search results summarization

In this paper, we present a novel ordered XPATH evaluation in treeunaware RDBMS The novelties of our approach lies in the followings (a) We propose a novel XML storage scheme which comprises only leaf nodes, their corresponding data values, order encodings and their root-to-leaf paths (b) We propose an algorithm for mapping ordered XPATH queries into SQL queries over the storage scheme (c) We propose an optimization technique that enforces all mapped SQL queries to be evaluated in a "left-to-right" join order By employing these techniques, we show, through a comprehensive experiment, that our approach not only scales well but also performs better than some representative tree-unaware approaches on more than 65% of our benchmark queries with the highest observed gain factor being 1939 In addition, our approach reduces significantly the performance gap between tree-aware and tree-unaware approaches and even outperforms a state-of-the-art tree-aware approach for certain benchmark queries

Boon-Siew Seah

Papers

Clustering and Summarizing Protein-Protein Interaction Networks: A Survey

FUSE: a profit maximization approach for functional summarization of biological networks

DualAligner: A Dual Alignment-based Strategy to Align Protein Interaction Networks

PRISM: concept-preserving social image search results summarization

Efficient support for ordered xpath processing in tree-unaware commercial relational databases