Annotating proteins by mining protein interaction networks

Dynamically searching for a domain for protein function prediction

The availability of large amounts of protein-protein interaction (PPI) data makes it feasible to use computational approaches to predict protein functions. The base of existing computational approaches is to exploit the known function information of annotated proteins in the PPI data to predict functions of un-annotated proteins. However, these approaches consider the prediction domain (i.e. the set of proteins from which the functions are predicted) as unchangeable during the prediction procedure. This may lead to valuable information being overwhelmed by the unavoidable noise information in the PPI data when predicting protein functions, and in turn, the prediction results will be distorted. In this paper, we propose a novel method to dynamically predict protein functions from the PPI data. Our method regards the function prediction as a dynamic process of finding a suitable prediction domain, from which representative functions of the domain are selected to predict functions of un-annotated proteins. Our method exploits the topological structural information of a PPI network and the semantic relationship between protein functions to measure the relationship between proteins, dynamically select a suitable prediction domain and predict functions. The evaluation on real PPI datasets demonstrated the effectiveness of our proposed method, and generated better prediction results.

Predicting protein function by multi-label correlated semi-supervised learning

Assigning biological functions to uncharacterized proteins is a fundamental problem in the postgenomic era. The increasing availability of large amounts of data on protein-protein interactions (PPIs) has led to the emergence of a considerable number of computational methods for determining protein function in the context of a network. These algorithms, however, treat each functional class in isolation and thereby often suffer from the difficulty of the scarcity of labeled data. In reality, different functional classes are naturally dependent on one another. We propose a new algorithm, Multi-label Correlated Semi-supervised Learning (MCSL), to incorporate the intrinsic correlations among functional classes into protein function prediction by leveraging the relationships provided by the PPI network and the functional class network. The guiding intuition is that the classification function should be sufficiently smooth on subgraphs where the respective topologies of these two networks are a good match. We encode this intuition as regularized learning with intraclass and interclass consistency, which can be understood as an extension of the graph-based learning with local and global consistency (LGC) method. Cross validation on the yeast proteome illustrates that MCSL consistently outperforms several state-of-the-art methods. Most notably, it effectively overcomes the problem associated with scarcity of label data. The supplementary files are freely available at http://sites.google.com/site/csaijiang/MCSL.

Vavien: an algorithm for prioritizing candidate disease genes based on topological similarity of proteins in interaction networks

Genome-wide linkage and association studies have demonstrated promise in identifying genetic factors that influence health and disease. An important challenge is to narrow down the set of candidate genes that are implicated by these analyses. Protein-protein interaction (PPI) networks are useful in extracting the functional relationships between known disease and candidate genes, based on the principle that products of genes implicated in similar diseases are likely to exhibit significant connectivity/proximity. Information flow?based methods are shown to be very effective in prioritizing candidate disease genes. In this article, we utilize the topology of PPI networks to infer functional information in the context of disease association. Our approach is based on the assumption that PPI networks are organized into recurrent schemes that underlie the mechanisms of cooperation among different proteins. We hypothesize that proteins associated with similar diseases would exhibit similar topological characteristics in PPI networks. Utilizing the location of a protein in the network with respect to other proteins (i.e., the "topological profile" of the proteins), we develop a novel measure to assess the topological similarity of proteins in a PPI network. We then use this measure to prioritize candidate disease genes based on the topological similarity of their products and the products of known disease genes. We test the resulting algorithm, Vavien, via systematic experimental studies using an integrated human PPI network and the Online Mendelian Inheritance in Man (OMIM) database. Vavien outperforms other network-based prioritization algorithms as shown in the results and is available at www.diseasegenes.org.

Algorithmic and analytical methods in network biology

During the genomic revolution, algorithmic and analytical methods for organizing, integrating, analyzing, and querying biological sequence data proved invaluable. Today, increasing availability of high-throughput data pertaining to functional states of biomolecules, as well as their interactions, enables genome-scale studies of the cell from a systems perspective. The past decade witnessed significant efforts on the development of computational infrastructure for large-scale modeling and analysis of biological systems, commonly using network models. Such efforts lead to novel insights into the complexity of living systems, through development of sophisticated abstractions, algorithms, and analytical techniques that address a broad range of problems, including the following: (1) inference and reconstruction of complex cellular networks; (2) identification of common and coherent patterns in cellular networks, with a view to understanding the organizing principles and building blocks of cellular signaling, regulation, and metabolism; and (3) characterization of cellular mechanisms that underlie the differences between living systems, in terms of evolutionary diversity, development and differentiation, and complex phenotypes, including human disease. These problems pose significant algorithmic and analytical challenges because of the inherent complexity of the systems being studied; limitations of data in terms of availability, scope, and scale; intractability of resulting computational problems; and limitations of reference models for reliable statistical inference. This article provides a broad overview of existing algorithmic and analytical approaches to these problems, highlights key biological insights provided by these approaches, and outlines emerging opportunities and challenges in computational systems biology.

Protein function prediction with high-throughput data

Protein function prediction is one of the main challenges in post-genomic era. The availability of large amounts of high-throughput data provides an alternative approach to handling this problem from the computational viewpoint. In this review, we provide a comprehensive description of the computational methods that are currently applicable to protein function prediction, especially from the perspective of machine learning. Machine learning techniques can generally be classified as supervised learning, semi-supervised learning and unsupervised learning. By classifying the existing computational methods for protein annotation into these three groups, we are able to present a comprehensive framework on protein annotation based on machine learning techniques. In addition to describing recently developed theoretical methodologies, we also cover representative databases and software tools that are widely utilized in the prediction of protein function.

Commonality of functional annotation: a method for prioritization of candidate genes from genome-wide linkage studies

Linkage studies of complex traits frequently yield multiple linkage regions covering hundreds of genes. Testing each candidate gene from every region is prohibitively expensive and computational methods that simplify this process would benefit genetic research. We present a new method based on commonality of functional annotation (CFA) that aids dissection of complex traits for which multiple causal genes act in a single pathway or process. CFA works by testing individual Gene Ontology (GO) terms for enrichment among candidate gene pools, performs multiple hypothesis testing adjustment using an estimate of independent tests based on correlation of GO terms, and then scores and ranks genes annotated with significantly-enriched terms based on the number of quantitative trait loci regions in which genes bearing those annotations appear. We evaluate CFA using simulated linkage data and show that CFA has good power despite being conservative. We apply CFA to published linkage studies investigating age-of-onset of Alzheimer's disease and body mass index and obtain previously known and new candidate genes. CFA provides a new tool for studies in which causal genes are expected to participate in a common pathway or process and can easily be extended to utilize annotation schemes in addition to the GO.