2005 ConsolidHumanProteinProteinInteract

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Subjects Headings: Semantic Relation Extraction Algorithm, Protein-Protein Interaction, Log Odds Ratio, PPLRE Project

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Quotes

Abstract

Background
Extensive protein interaction maps are being constructed for yeast, worm, and fly to ask how the proteins organize into pathways and systems, but no such genome-wide interaction map yet exists for the set of human proteins. To prepare for studies in humans, we wished to establish tests for the accuracy of future interaction assays and to consolidate the known interactions among human proteins.
Results
We established two tests of the accuracy of human protein interaction datasets and measured the relative accuracy of the available data. We then developed and applied natural language processing and literature-mining algorithms to recover from Medline abstracts 6,580 interactions among 3,737 human proteins. A three-part algorithm was used: first, human protein names were identified in Medline abstracts using a discriminator based on conditional random fields, then interactions were identified by the co-occurrence of protein names across the set of Medline abstracts, filtering the interactions with a Bayesian classifier to enrich for legitimate physical interactions. These mined interactions were combined with existing interaction data to obtain a network of 31,609 interactions among 7,748 human proteins, accurate to the same degree as the existing datasets.
Conclusion
These interactions and the accuracy benchmarks will aid interpretation of current functional genomics data and provide a basis for determining the quality of future large-scale human protein interaction assays. Projecting from the approximately 15 interactions per protein in the best-sampled interaction set to the estimated 25,000 human genes implies more than 375,000 interactions in the complete human protein interaction network. This set therefore represents no more than 10% of the complete network.

Results

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Assembling existing public protein interaction data

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Benchmarking of protein information data

To measure the relative accuracy of each protein interaction dataset, we established two benchmarks of interaction accuracy, one based on shared protein function and the other based on previously known interactions. First, we constructed a benchmark in which we tested the extent to which interaction partners in a dataset shared annotation, a measure previously shown to correlate with the accuracy of functional genomics datasets [13,14,21]. We used the functional annotations listed in the Kyoto Encyclopedia of Genes and Genomes (KEGG) [30] and Gene Ontology (GO) [31] annotation databases. These databases provide specific pathway and biological process annotations for approximately 7,500 human genes, assigning human genes into 155 KEGG pathways (at the lowest level of KEGG) and 1,356 GO pathways (at level 8 of the GO biological process annotation). KEGG and GO annotations were combined into a single composite functional annotation set, which was then split into independent testing and training sets by randomly assigning annotated genes into the two categories (3,792 and 3,809 annotated genes respectively). For the second benchmark based on known physical interactions, we assembled the human protein interactions from Reactome and BIND, a set of 11,425 interactions between 1,710 proteins. Each benchmark therefore consists of a set of binary relations between proteins, either based on proteins sharing annotation or physically interacting. Generally speaking, we expect more accurate protein interaction datasets to be more enriched in these protein pairs. More specifically, we expect true physical interactions to score highly on both tests, while non-physical or indirect associations, such as genetic associations, should score highly on the functional, but not the physical interaction, test.

For both benchmarks, the scoring scheme for measuring interaction set accuracy is in the form of a log odds ratio of gene pairs either sharing annotations or physically interacting. To evaluate a dataset, we calculate a log likelihood ratio (LLR) as:

[math]\displaystyle{ LLR = ln (\frac{P(D \vert I)} {P(D \vert \sim I)}) }[/math]

where [math]\displaystyle{ P(D\vert I) }[/math] and [math]\displaystyle{ P(D\vert \sim I) }[/math] are the probability of observing the data (D) conditioned on the genes sharing benchmark associations (I) and not sharing benchmark associations (~I). By Bayes theorem, this equation can be rewritten as:

[math]\displaystyle{ LLR = ln (\frac{(P(I \vert D)/P(\sim I \vert D))}{P(I)/P(\sim I)}) }[/math]

where [math]\displaystyle{ P(I\vert D) }[/math] and [math]\displaystyle{ P(\sim I\vert D) }[/math] are the frequencies of interactions observed in the given dataset (D) between annotated genes sharing benchmark associations (I) and not sharing associations (~I), respectively, while P(I) and P(~I) represent the prior expectations (the total frequencies of all benchmark genes sharing the same associations and not sharing associations, respectively). This latter version of the equation is simpler to compute. A score of zero indicates interaction partners in the dataset being tested are no more likely than random to belong to the same pathway or to interact; higher scores indicate a more accurate dataset.

Discussion

Shortcomings and strengths of literature mining via the co-citation/Bayesian classifier approach

The co-citation approach [14,26,40] calculates the random probability of co-occurrence of two protein names. The assumption is that if the co-citation is statistically unlikely under the random model, then there is a true underlying reason for the proteins to be co-cited - that is, they are interacting at either the functional, pathway level, or are co-localized or physically interact. The method has both advantages and disadvantages. It does not extract all interactions, but only those with statistically significant co-citations.

Intuition: proteins co-occurring in a large number of abstracts tend to be interacting proteins. (from paper presentation)

Compute the probability of co-citation under a random model (hyper-geometric distribution). [math]\displaystyle{ P(k \vert N,n,m)=(n choose k)((N-n) choose (m-k))/(N choose m) }[/math] where, [math]\displaystyle{ N }[/math] <= total number of abstracts (750K). “n – abstracts citing the first protein; [math]\displaystyle{ m }[/math] <= abstracts citing the second protein; [math]\displaystyle{ k }[/math] <= abstracts citing both proteins." (from paper presentation)

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2005 ConsolidHumanProteinProteinInteractArun K. Ramani
Razvan C. Bunescu
Edward M Marcotte
Raymond J. Mooney		Consolidating the Set of Known Human Protein-Protein Interactions in Preparation for Large-Scale Mapping of the Human InteractomeGenome Biologyhttp://genomebiology.com/content/pdf/gb-2005-6-5-r40.pdf10.1186/gb-2005-6-5-r402005
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