Comparative experiments on learning information extractors for proteins and their interactions

By Razvan C. Bunescu, Ruifang Ge, Rohit J. Kate, Edward M. Marcotte, Raymond J. Mooney, Arun K. Ramani, Yuk Wah Wong. Artif. Intell. Medicine, 2005

469 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/356600aa55214f535f9d43020fe97bbaf6541182

How researchers see this paper

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

In Artificial Intelligence

In Molecular Biology

Citations in the corpus

Extracting interactions between proteins from the literature (2008, methodology): “A variety of machine-learning and statistical techniques based on the discovery of co-occurrence of protein names have been applied for protein–protein information extraction [79–86,8,87–91].”
Exploiting Shallow Linguistic Information for Relation Extraction from Biomedical Literature (2006, methodology): “All experiments are conducted using 10-fold cross validation on the same data splitting used in (Bunescu et al., 2005; Bunescu and Mooney, 2005b).”
Memorization vs. Generalization : Quantifying Data Leakage in NLP Performance Evaluation (2021, methodology): “We examine overlap in the following datasets: • AIMed - AIMed dataset ( Bunescu et al., 2005) dataset (Smith et al., 2008) for NER task • ChEMU - Chemical Reactions from Patents (He et al., 2020) for recognising names of chemicals, an NER task • BC3ACT - Biocreative III protein interaction classiﬁca”
Construction of an annotated corpus to support biomedical information extraction (2009, methodology): “We plan to convert our own corpus to this format, which has already been carried out for several biomedical corpora, e.g., [13,16,21,23].”
Evaluation of linguistic features useful in extraction of interactions from PubMed; Application to annotating known, high-throughput and predicted interactions in I2D (2009, methodology): “Contact: yniu@uhnres.utoronto.ca; juris@ai.utoronto.ca Supplementary information: Supplementary data are available at Bioinformatics online.”
BioWordVec, improving biomedical word embeddings with subword information and MeSH (2019, methodology): “The public PPI corpora were used for the PPI extraction, including AIMed 29 , BioInfer 30 , IEPA 31 , HPRD50 32 and LLL 33 .”
Deep learning for extracting protein-protein interactions from biomedical literature (2017, methodology): “model on two benchmarking PPI corpora, AIMed (Bunescu et al., 2005) and BioInfer (Pyysalo et al.”
Investigating a Generic Paraphrase-Based Approach for Relation Extraction (2006, methodology): “In order to compare (roughly) our performance with supervised methods applied to this dataset, as summarized in (Bunescu et al., 2005), we adopted their recall and precision measurement.”
Bridging semantics and syntax with graph algorithms - state-of-the-art of extracting biomedical relations (2017, methodology): “Shared resources for biomedical relation extraction Utility category Data sources Terminology/Ontology/Knowledge Base GO [161], UMLS [58], MeSH [162], Wordnet [118], Verbnet [163], Biothesaurus [122], PDB [101], Uniprot [121], SIDER [164], DrugBank [165], HIVDB [166], RegaDB [167], Entrez Gene [153]”
The relationship between Precision-Recall and ROC curves (2006, methodology): “, 1989), have been cited as an alternative to ROC curves for tasks with a large skew in the class distribution (Bockhorst & Craven, 2005; Bunescu et al., 2004; Davis et al., 2005; Goadrich et al., 2004; Kok & Domingos, 2005; Singla & Domingos, 2005).”
An Unsupervised Text Mining Method for Relation Extraction from Biomedical Literature (2014, methodology): “AImed corpus is manually developed by Bunescu et al. for protein–protein interaction and protein name recognition [25], which has been used for many protein interaction extraction systems [22] [26–30].”
Learning from Imbalanced Data (2009, result): “As a result, many of the current research work in the community use PR curves for performance evaluations and comparisons [124], [125], [126], [127].”