FunPred-1: Protein function prediction from a protein interaction network using neighborhood analysis

Saha, Sovan; Chatterjee, Piyali; Basu, Subhadip; Kundu, Mahantapas; Nasipuri, Mita

doi:10.2478/s11658-014-0221-5

Cited by 26 publications

(18 citation statements)

References 26 publications

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“…Protein clusters, thus formed, comprises of proteins belonging to any functional group. It results in accumulating larger number of functional groups as compared with only eight functional groups in our previous work (Saha et al, 2014). The novel computational method works in two stages: All the unique proteins are first clustered into M mutually exclusive clusters based on their node weight and edge weight in the overall PPIN.…”

Section: Methodsmentioning

confidence: 86%

“…It highlights the fact that most of the admissible results are successfully generated by FunPred 3.0. Table 4 shows a detailed performance comparison of other methodologies along with our proposed systems (like FunPred 1.1, FunPred 1.2 (Saha et al, 2014)), the neighborhood counting method (Schwikowski, Uetz & Fields, 2000), the Chi-square method (Hishigaki et al, 2001), a recent version of the neighbor relativity coefficient (NRC) (Moosavi, Rahgozar & Rahimi, 2013), FPred_Apriori (Prasad et al, 2017), Zhang methodology (Zhang et al, 2009), domain combination similarity (DCS) (Peng et al, 2014), domain combination similarity in context of protein complexes (DSCP) (Peng et al, 2014), protein overlap network (PON) (Liang et al, 2013), Deep_GO (Kulmanov et al, 2018) and the FS-weight based method (Chua, Sung & Wong, 2006)). All these data are collected from their respective works which are executed on the same organism, that is, yeast.…”

Section: Resultsmentioning

confidence: 99%

“…In one of our two earlier works, Funpred-1 (Saha et al, 2014), the selection of 10% of test proteins of the top eight functional groups from the dataset was done randomly. Top eight functional groups were selected on the basis of maximum number of occurrences and interactions of proteins in them.…”

Section: Methodsmentioning

confidence: 99%

“…Proteins with similar functions are more likely to interact. If the function of one protein is known then the functions of the binding un-annotated protein may either be experimentally assigned or computationally predicted (Chatterjee et al, 2011a, 2011b; Moosavi, Rahgozar & Rahimi, 2013; Prasad et al, 2017; Saha et al, 2012, 2014; Sriwastava, Basu & Maulik, 2015). Several computational techniques have been developed using either the protein sequence (Ng & Henikoff, 2003), protein structure (Lee, Redfern & Orengo, 2007; Mills, Beuning & Ondrechen, 2015), protein–protein interactions (Moosavi, Rahgozar & Rahimi, 2013; Schwikowski, Uetz & Fields, 2000; Vazquez et al, 2003; Xiong et al, 2013), or sequence motifs or signatures (Chatterjee et al, 2011a; Chen et al, 2007; Lichtarge, Bourne & Cohen, 1996).…”

Section: Introductionmentioning

confidence: 99%

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FunPred 3.0: improved protein function prediction using protein interaction network

Saha¹,

Chatterjee²,

Basu

et al. 2019

PeerJ

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Proteins are the most versatile macromolecules in living systems and perform crucial biological functions. In the advent of the post-genomic era, the next generation sequencing is done routinely at the population scale for a variety of species. The challenging problem is to massively determine the functions of proteins that are yet not characterized by detailed experimental studies. Identification of protein functions experimentally is a laborious and time-consuming task involving many resources. We therefore propose the automated protein function prediction methodology using in silico algorithms trained on carefully curated experimental datasets. We present the improved protein function prediction tool FunPred 3.0, an extended version of our previous methodology FunPred 2, which exploits neighborhood properties in protein–protein interaction network (PPIN) and physicochemical properties of amino acids. Our method is validated using the available functional annotations in the PPIN network of Saccharomyces cerevisiae in the latest Munich information center for protein (MIPS) dataset. The PPIN data of S. cerevisiae in MIPS dataset includes 4,554 unique proteins in 13,528 protein–protein interactions after the elimination of the self-replicating and the self-interacting protein pairs. Using the developed FunPred 3.0 tool, we are able to achieve the mean precision, the recall and the F-score values of 0.55, 0.82 and 0.66, respectively. FunPred 3.0 is then used to predict the functions of unpredicted protein pairs (incomplete and missing functional annotations) in MIPS dataset of S. cerevisiae. The method is also capable of predicting the subcellular localization of proteins along with its corresponding functions. The code and the complete prediction results are available freely at: https://github.com/SovanSaha/FunPred-3.0.git.

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Section: Methodsmentioning

confidence: 86%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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FunPred 3.0: improved protein function prediction using protein interaction network

Saha¹,

Chatterjee²,

Basu

et al. 2019

PeerJ

Self Cite

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“…To discover more candidate genes involved in SPN, we conducted a search in the GRN of SPN based on the “guilt-by-association” rule [ 29 ] which has been widely used to predict gene functions in many biological networks [ 55 , 56 ]. The rule regarded the neighbors of a given gene as to have similar biological functions.…”

Section: Resultsmentioning

confidence: 99%

Identification of potential biomarkers to differentially diagnose solid pseudopapillary tumors and pancreatic malignancies via a gene regulatory network

Jiao

et al. 2015

J Transl Med

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BackgroundSolid pseudopapillary neoplasms (SPN) are pancreatic tumors with low malignant potential and good prognosis. However, differential diagnosis between SPN and pancreatic malignancies including pancreatic neuroendocrine tumor (PanNET) and ductal adenocarcinoma (PDAC) is difficult. This study tried to identify candidate biomarkers for the distinction between SPN and the two malignant pancreatic tumors by examining the gene regulatory network of SPN.MethodsThe gene regulatory network for SPN was constructed by a co-expression model. Genes that have been reported to be correlated with SPN were used as the clues to hunt more SPN-related genes in the network according to a shortest path approach. By means of the K-nearest neighbor algorithm (KNN) classifier evaluated by the jackknife test, sets of genes to distinguish SPN and malignant pancreatic tumors were determined.ResultsWe took a new strategy to identify candidate biomarkers for differentiating SPN from the two malignant pancreatic tumors PanNET and PDAC by analyzing shortest paths among SPN-related genes in the gene regulatory network. 43 new SPN-relevant genes were discovered, among which, we found hsa-miR-194 and hsa-miR-7 along with 7 transcription factors (TFs) such as SOX11, SMAD3 and SOX4 etc. could correctly differentiate SPN from PanNET, while hsa-miR-204 and 4 TFs such as SOX9, TCF7 and PPARD etc. were demonstrated as the potential markers for SPN versus PDAC. 14 genes were demonstrated to serve as the candidate biomarkers for distinguishing SPN from PanNET and PDAC when considering them as malignant pancreatic tumors together.ConclusionThis study provides new candidate genes related to SPN and the potential biomarkers to differentiate SPN from PanNET and PDAC, which may help to diagnose patients with SPN in clinical setting. Furthermore, candidate biomarkers such as SOX11 and hsa-miR-204 which could cause cell proliferation but inhibit invasion or metastasis may be of importance in understanding the molecular mechanism of pancreatic oncogenesis and could be possible therapeutic targets for malignant pancreatic tumors.Electronic supplementary materialThe online version of this article (doi:10.1186/s12967-015-0718-3) contains supplementary material, which is available to authorized users.

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Gene Ontology Based Function Prediction of Human Protein Using Protein Sequence and Neighborhood Property of PPI Network

Saha¹,

Chatterjee

Basu

et al. 2017

Advances in Intelligent Systems and Computing

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FunPred-1: Protein function prediction from a protein interaction network using neighborhood analysis

Cited by 26 publications

References 26 publications

FunPred 3.0: improved protein function prediction using protein interaction network

FunPred 3.0: improved protein function prediction using protein interaction network

Identification of potential biomarkers to differentially diagnose solid pseudopapillary tumors and pancreatic malignancies via a gene regulatory network

Gene Ontology Based Function Prediction of Human Protein Using Protein Sequence and Neighborhood Property of PPI Network

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