Whole-proteome prediction of protein function via graph-theoretic analysis of interaction maps

Nabieva, Elena; Jim, Kam; Agarwal, Amit; Chazelle, Bernard; Singh, Mona

doi:10.1093/bioinformatics/bti1054

Cited by 453 publications

(390 citation statements)

References 42 publications

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“…Analogously, k-nearest neighborhood (kNN) methods consider only the labels of the k most similar neighbors [32]; in turn, shared similarity metrics, as those proposed in [28,18], can be introduced to generalize the notion of pairwise-similarity among nodes by taking into account the contribution of shared neighbors [14,9]. Other methodologies predict labels by propagating node labels to neighbors with an iterative process until convergence [70,69], or by evaluating the functional flows through the nodes of the graph [62,49]. Random Walks (RW) have also been applied to tune the probability to reach a given node through a probabilistic path starting from positive instances [59,4,30].…”

Section: Introductionmentioning

confidence: 99%

Learning node labels with multi-category Hopfield networks

Frasca

Bassis

Valentini

2015

Neural Comput & Applic

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In several real-world node-label prediction problems on graphs, in fields ranging from computational biology to World-Wide-Web analysis, nodes can be partitioned into categories different from the classes to be predicted, on the basis of their characteristics or their common properties. Such partitions may provide further information about node classification that classical machine learning algorithms do not take into account. We introduce a novel family of parametric Hopfield networks (m-Category Hopfield Networks) and a novel algorithm (Hopfield Multi-Category -HoMCat), designed to appropriately exploit the presence of propertybased partitions of nodes into multiple categories. Moreover, the proposed model adopts a cost-sensitive learning strategy to prevent the remarkable decay in performance usually observed when instance labels are unbalanced, that is when one class of labels is highly under-represented than the other one. We validate the proposed model on both synthetic and real-world data, in the context of multi-species function prediction, where the classes to be predicted are the Gene Ontology terms and the categories the different species in the multi-species protein network. We carried out an intensive experimental validation, which on the one hand compares HoMCat with several state-of-the-art graph-based algorithms, and on the other hand reveals that exploiting meaningful prior partitions of input data can substantially improve classification performances.

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Section: Introductionmentioning

confidence: 99%

Learning node labels with multi-category Hopfield networks

Frasca

Bassis

Valentini

2015

Neural Comput & Applic

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“…Methods based on the amount of functional flow through the nodes (Nabieva et al, 2005), on global graph consistency (Vazquez et al, 2003;Karaoz et al, 2004), on Markov and Gaussian Random Fields (Tsuda et al, 2005;Mostafavi et al, 2008), and recently on kernelized score functions have been applied to the predic-tion of gene functions.…”

Section: Introductionmentioning

confidence: 99%

A neural network algorithm for semi-supervised node label learning from unbalanced data

et al. 2013

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Given a weighted graph and a partial node labeling, the graph classification problem consists in predicting the labels of all the nodes. In several application domains, from gene to social network analysis, the labeling is unbalanced: for instance positive labels may be much less than negatives. In this paper we present COSNet (COst Sensitive neural Network), a neural algorithm for predicting node labels in graphs with unbalanced labels. COSNet is based on a 2-parameters family of Hopfield networks, and consists of two main steps: 1) the network parameters are learned through a costsensitive optimization procedure; 2) a suitable Hopfield network restricted to the unlabeled nodes is considered and simulated. The reached equilibrium point induces the classification of the unlabeled nodes. The restriction of the dynamics leads to a significant reduction in time complexity and allows the algorithm to nicely scale with large networks. An experimental analysis on real-world unbalanced data, in the context of the genome-wide prediction of gene functions, shows the effectiveness of the proposed approach.

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“…Toward this goal, highthroughput experimental techniques [e.g., yeast two-hybrid (1,2) and coimmunoprecipitation (3)] have been invented to discover protein-protein interactions (PPIs) . The data from these techniques, which are still being perfected, are being supplemented by high-confidence computational predictions and analyses of PPIs (4)(5)(6). A powerful way of representing and analyzing this vast corpus of data is the PPI network: A network where each node corresponds to a protein and an edge indicates a direct physical interaction between the proteins.…”

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confidence: 99%

Global alignment of multiple protein interaction networks with application to functional orthology detection

Singh¹,

Berger

2008

Proc. Natl. Acad. Sci. U.S.A.

567

574

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Protein-protein interactions (PPIs) and their networks play a central role in all biological processes. Akin to the complete sequencing of genomes and their comparative analysis, complete descriptions of interactomes and their comparative analysis is fundamental to a deeper understanding of biological processes. A first step in such an analysis is to align two or more PPI networks. Here, we introduce an algorithm, IsoRank, for global alignment of multiple PPI networks. The guiding intuition here is that a protein in one PPI network is a good match for a protein in another network if their respective sequences and neighborhood topologies are a good match. We encode this intuition as an eigenvalue problem in a manner analogous to Google's PageRank method. Using IsoRank, we compute a global alignment of the Saccharomyces cerevisiae, Drosophila melanogaster, Caenorhabditis elegans, Mus musculus, and Homo sapiens PPI networks. We demonstrate that incorporating PPI data in ortholog prediction results in improvements over existing sequence-only approaches and over predictions from local alignments of the yeast and fly networks. Previous methods have been effective at identifying conserved, localized network patterns across pairs of networks. This work takes the further step of performing a global alignment of multiple PPI networks. It simultaneously uses sequence similarity and network data and, unlike previous approaches, explicitly models the tradeoff inherent in combining them. We expect IsoRank-with its simultaneous handling of node similarity and network similarity-to be applicable across many scientific domains.biological networks ͉ graph isomorphism ͉ network alignment ͉ protein-protein interactions ͉ functional coherence A fundamental goal of biology is to understand the cell as a system of interacting components. In particular, the discovery and understanding of interactions between proteins has received significant attention in recent years. Toward this goal, highthroughput experimental techniques [e.g., yeast two-hybrid (1, 2) and coimmunoprecipitation (3)] have been invented to discover protein-protein interactions (PPIs) . The data from these techniques, which are still being perfected, are being supplemented by high-confidence computational predictions and analyses of PPIs (4-6). A powerful way of representing and analyzing this vast corpus of data is the PPI network: A network where each node corresponds to a protein and an edge indicates a direct physical interaction between the proteins.As the size of PPI datasets for various species rapidly increases, comparative analysis of PPI networks across species is proving to be a valuable tool. Such analysis is similar in spirit to traditional sequence-based comparative genomic analyses; it also promises commensurate insights. As a phylogenetic tool, it offers a functionoriented perspective that complements traditional sequence-based methods. Comparative network analysis also enables us to identify conserved functional components across species (7) and perform...

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Whole-proteome prediction of protein function via graph-theoretic analysis of interaction maps

Abstract: http://compbio.cs.princeton.edu/function

Cited by 453 publications

References 42 publications

Learning node labels with multi-category Hopfield networks

Learning node labels with multi-category Hopfield networks

A neural network algorithm for semi-supervised node label learning from unbalanced data

Global alignment of multiple protein interaction networks with application to functional orthology detection

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