Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Motivation: Protein–protein interaction (PPI) extraction from published biological articles has attracted much attention because of the importance of protein interactions in biological processes. Despite significant progress, mining PPIs from literatures still rely heavily on time-and resource-consuming manual annotations.#R##N##R##N#Results: In this study,  developed a novel methodology based on Bayesian networks (BNs) for extracting PPI triplets (a PPI triplet consists of two protein names and the corresponding interaction word) from unstructured text. The method achieved an overall accuracy of 87% on a cross-validation test using manually annotated dataset.  also shod, through extracting PPI triplets from a large number of PubMed abstracts, that our method was able to complement human annotations to extract large number of new PPIs from literature.#R##N##R##N#Availability: Programs/scripts  developed/used in the study are available at http://stat.fsu.edu/~jinfeng/datasets/Bio-SI-programs-Bayesian-chowdhary-zhang-liu.zip#R##N##R##N#Contact: jliu@stat.harvard.edu#R##N##R##N#Supplementary information:Supplementary data are available at Bioinformatics online.
['Rajesh Chowdhary', 'Jinfeng Zhang', 'Jun S. Liu']
I am writing a research paper on a idea Bayesian inference of protein–protein interactions from biological literature
[{"from": "human", "value": "I am writing a research paper on a idea Bayesian inference of protein\u2013protein interactions from biological literature"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Motivation: Protein\u2013protein interaction (PPI) extraction from published biological articles has attracted much attention because of the importance of protein interactions in biological processes. Despite significant progress, mining PPIs from literatures still rely heavily on time-and resource-consuming manual annotations.#R##N##R##N#Results: In this study,  developed a novel methodology based on Bayesian networks (BNs) for extracting PPI triplets (a PPI triplet consists of two protein names and the corresponding interaction word) from unstructured text. The method achieved an overall accuracy of 87% on a cross-validation test using manually annotated dataset.  also shod, through extracting PPI triplets from a large number of PubMed abstracts, that our method was able to complement human annotations to extract large number of new PPIs from literature.#R##N##R##N#Availability: Programs/scripts  developed/used in the study are available at http://stat.fsu.edu/~jinfeng/datasets/Bio-SI-programs-Bayesian-chowdhary-zhang-liu.zip#R##N##R##N#Contact: jliu@stat.harvard.edu#R##N##R##N#Supplementary information:Supplementary data are available at Bioinformatics online."}]