PIE: an online prediction system for protein–protein interactions from text
نویسندگان
چکیده
Protein-protein interaction (PPI) extraction has been an important research topic in bio-text mining area, since the PPI information is critical for understanding biological processes. However, there are very few open systems available on the Web and most of the systems focus on keyword searching based on predefined PPIs. PIE (Protein Interaction information Extraction system) is a configurable Web service to extract PPIs from literature, including user-provided papers as well as PubMed articles. After providing abstracts or papers, the prediction results are displayed in an easily readable form with essential, yet compact features. The PIE interface supports more features such as PDF file extraction, PubMed search tool and network communication, which are useful for biologists and bio-system developers. The PIE system utilizes natural language processing techniques and machine learning methodologies to predict PPI sentences, which results in high precision performance for Web users. PIE is freely available at http://bi.snu.ac.kr/pie/.
منابع مشابه
Prediction of Protein Sub-Mitochondria Locations Using Protein Interaction Networks
Background: Prediction of the protein localization is among the most important issues in the bioinformatics that is used for the prediction of the proteins in the cells and organelles such as mitochondria. In this study, several machine learning algorithms are applied for the prediction of the intracellular protein locations. These algorithms use the features extracted from pro...
متن کاملDiscovering Domains Mediating Protein Interactions
Background: Protein-protein interactions do not provide any direct information regarding the domains within the proteins that mediate the interactions. The majority of proteins are multi domain proteins and the interaction between them is often defined by the pairs of their domains. Most of the former studies focus only on interacting domain pairs. However they do not consider the in...
متن کاملProtein Secondary Structure Prediction: a Literature Review with Focus on Machine Learning Approaches
DNA sequence, containing all genetic traits is not a functional entity. Instead, it transfers to protein sequences by transcription and translation processes. This protein sequence takes on a 3D structure later, which is a functional unit and can manage biological interactions using the information encoded in DNA. Every life process one can figure is undertaken by proteins with specific functio...
متن کاملRumen Degradability and Model Prediction of Nutrient Supply to Ruminants from Different Processed Soybean Meals
DVE2010 system was used for model prediction of nutrient supply to ruminants from soy bean meal (SBM), extruded soy bean meal (SBE) and full-fat soybean meal (FSBM). Extruded soy bean meal had the highest truly absorbed rumen undegraded protein in the small intestine (ARUP) followed by SBM and FSBM. There was no significant difference between SBE and FSBM in the case of truly absorbed rumen syn...
متن کاملStudy of PKA binding sites in cAMP-signaling pathway using structural protein-protein interaction networks
Backgroud: Protein-protein interaction, plays a key role in signal transduction in signaling pathways. Different approaches are used for prediction of these interactions including experimental and computational approaches. In conventional node-edge protein-protein interaction networks, we can only see which proteins interact but ‘structural networks’ show us how these proteins inter...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره 36 شماره
صفحات -
تاریخ انتشار 2008