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

By E. Nabieva, Kam-Chuen Jim, Amit Agarwal, Bernard Chazelle, Mona Singh. Intelligent Systems in Molecular Biology, 2005

500 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b83e70ccde417b10dc51b4c8ccdb75789713a1e1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. Bonnie Berger
2. Jian Peng
3. Jinbo Xu
4. Mona Singh
5. Igor Ulitsky
6. Roded Sharan
7. Quaid D. Morris
8. Ron Shamir

Citations in the corpus

• Graph theory and networks in Biology. (2006, methodology): “Firstly, in [187], the functional flow algorithm was described.”
• Computational solutions for omics data (2013, methodology): “An early application used flow to propagate biological process annotations over a protein–protein interaction network 119 .”
• Disease candidate gene identification and prioritization using protein interaction networks (2009, background): “Network-based analyses have been developed with a number of goals [9], including protein function prediction [10], identification of functional modules [11], interaction prediction [12,13], identification of disease candidate genes [14,15] and drug targets [16,17], and the study of network structure”
• A Survey of Statistical Network Models (2009, methodology): “Statistical methods have been developed for analyzing many aspects of this large protein interaction network, including de-noising [32; 8], function prediction [227], and identification of binding motifs [23].”
• Using PPI network autocorrelation in hierarchical multi-label classification trees for gene function prediction (2013, background): “However, the above approaches [29,30,45-47] do not consider the hierarchy of categories.”
• GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function (2008, background): “While there are a large variety of label propagation algorithms (for example, those based on functional flow [24,25] or Markov random fields [26]), our interest in the Gaussian field algorithm stems from the fact that it has a welldefined solution, it is derived from a principled framework, and it h”
• Uncovering Biological Network Function via Graphlet Degree Signatures (2008, background): “Other approaches use the idea of shared neighbors (Samanta and Liang, 2003) or the network flow-based idea (Nabieva et al., 2005) to determine protein function.”
• Network-based prediction of protein function (2007, methodology): “Several measures have been suggested to evaluate the quality of a direct annotation method (see e.g., Deng et al , 2003; Nabieva et al , 2005).”
• Pairwise Global Alignment of Protein Interaction Networks by Matching Neighborhood Topology (2007, background): “Of course, more sophisticated methods to transfer annotation may perform even better at elucidating function of such proteins [20].”