bluuebunny/crossref_metadata_2025_split huggingface dataset:
Uncovering protein interaction in abstracts and text using a novel linear model and word proximity networks
Authors: Abi-Haidar Alaa , Kaur Jasleen , Maguitman Ana , Radivojac Predrag , Rechtsteiner Andreas , Verspoor Karin , Wang Zhiping , Rocha Luis M 
Published: September 2008

Abstract
Background:
We participated in three of the protein-protein interaction subtasks of the Second BioCreative Challenge: classification of abstracts relevant for protein-protein interaction (interaction article subtask [IAS]), discovery of protein pairs (interaction pair subtask [IPS]), and identification of text passages characterizing protein interaction (interaction sentences subtask [ISS]) in full-text documents. We approached the abstract classification task with a novel, lightweight linear model inspired by spam detection techniques, as well as an uncertainty-based integration scheme. We also used a support vector machine and singular value decomposition on the same features for comparison purposes. Our approach to the full-text subtasks (protein pair and passage identification) includes a feature expansion method based on word proximity networks.

Results:
Our approach to the abstract classification task (IAS) was among the top submissions for this task in terms of measures of performance used in the challenge evaluation (accuracy, F-score, and area under the receiver operating characteristic curve). We also report on a web tool that we produced using our approach: the Protein Interaction Abstract Relevance Evaluator (PIARE). Our approach to the full-text tasks resulted in one of the highest recall rates as well as mean reciprocal rank of correct passages.

Conclusion:
Our approach to abstract classification shows that a simple linear model, using relatively few features, can generalize and uncover the conceptual nature of protein-protein interactions from the bibliome. Because the novel approach is based on a rather lightweight linear model, it can easily be ported and applied to similar problems. In full-text problems, the expansion of word features with word proximity networks is shown to be useful, although the need for some improvements is discussed.
Mining physical protein-protein interactions from the literature
Authors: Huang Minlie , Ding Shilin , Wang Hongning , Zhu Xiaoyan 
Published: September 2008

Abstract
Background:
Deciphering physical protein-protein interactions is fundamental to elucidating both the functions of proteins and biological processes. The development of high-throughput experimental technologies such as the yeast two-hybrid screening has produced an explosion in data relating to interactions. Since manual curation is intensive in terms of time and cost, there is an urgent need for text-mining tools to facilitate the extraction of such information. The BioCreative (Critical Assessment of Information Extraction systems in Biology) challenge evaluation provided common standards and shared evaluation criteria to enable comparisons among different approaches.

Results:
During the benchmark evaluation of BioCreative 2006, all of our results ranked in the top three places. In the task of filtering articles irrelevant to physical protein interactions, our method contributes a precision of 75.07%, a recall of 81.07%, and an AUC (area under the receiver operating characteristic curve) of 0.847. In the task of identifying protein mentions and normalizing mentions to molecule identifiers, our method is competitive among runs submitted, with a precision of 34.83%, a recall of 24.10%, and an F1 score of28.5%. In extracting protein interaction pairs, our profile-based method was competitive on the SwissProt-only subset (precision = 36.95%, recall = 32.68%, and F1 score = 30.40%) and on the entire dataset (30.96%, 29.35%, and26.20%, respectively). From the biologist's point of view, however, these findings are far from satisfactory. The error analysis presented in this report provides insight into how performance could be improved: three-quarters of false negatives were due to protein normalization problems (532/698), and about one-quarter were due to problems with correctly extracting interactions for this system.

Conclusion:
We present a text-mining framework to extract physical protein-protein interactions from the literature. Three key issues are addressed, namely filtering irrelevant articles, identifying protein names and normalizing them to molecule identifiers, and extracting protein-protein interactions. Our system is among the top three performers in the benchmark evaluation of BioCreative 2006. The tool will be helpful for manual interaction curation and can greatly facilitate the process of extracting protein-protein interactions.
OntoGene in BioCreative II
Authors: Rinaldi Fabio , Kappeler Thomas , Kaljurand Kaarel , Schneider Gerold , Klenner Manfred , Clematide Simon , Hess Michael , von Allmen Jean-Marc , Parisot Pierre , Romacker Martin , Vachon Therese 
Published: September 2008

Abstract
Background:
Research scientists and companies working in the domains of biomedicine and genomics are increasingly faced with the problem of efficiently locating, within the vast body of published scientific findings, the critical pieces of information that are needed to direct current and future research investment.

Results:
In this report we describe approaches taken within the scope of the second BioCreative competition in order to solve two aspects of this problem: detection of novel protein interactions reported in scientific articles, and detection of the experimental method that was used to confirm the interaction. Our approach to the former problem is based on a high-recall protein annotation step, followed by two strict disambiguation steps. The remaining proteins are then combined according to a number of lexico-syntactic filters, which deliver high-precision results while maintaining reasonable recall. The detection of the experimental methods is tackled by a pattern matching approach, which has delivered the best results in the official BioCreative evaluation.

Conclusion:
Although the results of BioCreative clearly show that no tool is sufficiently reliable for fully automated annotations, a few of the proposed approaches (including our own) already perform at a competitive level. This makes them interesting either as standalone tools for preliminary document inspection, or as modules within an environment aimed at supporting the process of curation of biomedical literature.
Gene mention normalization and interaction extraction with context models and sentence motifs
Authors: Hakenberg Jörg , Plake Conrad , Royer Loic , Strobelt Hendrik , Leser Ulf , Schroeder Michael 
Published: September 2008

Abstract
Background:
The goal of text mining is to make the information conveyed in scientific publications accessible to structured search and automatic analysis. Two important subtasks of text mining are entity mention normalization - to identify biomedical objects in text - and extraction of qualified relationships between those objects. We describe a method for identifying genes and relationships between proteins.

Results:
We present solutions to gene mention normalization and extraction of protein-protein interactions. For the first task, we identify genes by using background knowledge on each gene, namely annotations related to function, location, disease, and so on. Our approach currently achieves an f-measure of 86.4% on the BioCreative II gene normalization data. For the extraction of protein-protein interactions, we pursue an approach that builds on classical sequence analysis: motifs derived from multiple sequence alignments. The method achieves an f-measure of 24.4% (micro-average) in the BioCreative II interaction pair subtask.

Conclusion:
For gene mention normalization, our approach outperforms strategies that utilize only the matching of genes names against dictionaries, without invoking further knowledge on each gene. Motifs derived from alignments of sentences are successful at identifying protein interactions in text; the approach we present in this report is fully automated and performs similarly to systems that require human intervention at one or more stages.

Availability:
Our methods for gene, protein, and species identification, and extraction of protein-protein are available as part of the BioCreative Meta Services (BCMS), see http://bcms.bioinfo.cnio.es/.
Overview of BioCreative II gene mention recognition
Authors: Smith Larry , Tanabe Lorraine K , Ando Rie Johnson nee , Kuo Cheng-Ju , Chung I-Fang , Hsu Chun-Nan , Lin Yu-Shi , Klinger Roman , Friedrich Christoph M , Ganchev Kuzman , Torii Manabu , Liu Hongfang , Haddow Barry , Struble Craig A , Povinelli Richard J , Vlachos Andreas , Baumgartner William A , Hunter Lawrence , Carpenter Bob , Tsai Richard Tzong-Han , Dai Hong-Jie , Liu Feng , Chen Yifei , Sun Chengjie , Katrenko Sophia , Adriaans Pieter , Blaschke Christian , Torres Rafael , Neves Mariana , Nakov Preslav , Divoli Anna , Maña-López Manuel , Mata Jacinto , Wilbur W John 
Published: September 2008

AbstractNineteen teams presented results for the Gene Mention Task at the BioCreative II Workshop. In this task participants designed systems to identify substrings in sentences corresponding to gene name mentions. A variety of different methods were used and the results varied with a highest achieved F1 score of 0.8721. Here we present brief descriptions of all the methods used and a statistical analysis of the results. We also demonstrate that, by combining the results from all submissions, an F score of 0.9066 is feasible, and furthermore that the best result makes use of the lowest scoring submissions.
Overview of BioCreative II gene normalization
Authors: Morgan Alexander A , Lu Zhiyong , Wang Xinglong , Cohen Aaron M , Fluck Juliane , Ruch Patrick , Divoli Anna , Fundel Katrin , Leaman Robert , Hakenberg Jörg , Sun Chengjie , Liu Heng-hui , Torres Rafael , Krauthammer Michael , Lau William W , Liu Hongfang , Hsu Chun-Nan , Schuemie Martijn , Cohen K Bretonnel , Hirschman Lynette 
Published: September 2008

Abstract
Background:
The goal of the gene normalization task is to link genes or gene products mentioned in the literature to biological databases. This is a key step in an accurate search of the biological literature. It is a challenging task, even for the human expert; genes are often described rather than referred to by gene symbol and, confusingly, one gene name may refer to different genes (often from different organisms). For BioCreative II, the task was to list the Entrez Gene identifiers for human genes or gene products mentioned in PubMed/MEDLINE abstracts. We selected abstracts associated with articles previously curated for human genes. We provided 281 expert-annotated abstracts containing 684 gene identifiers for training, and a blind test set of 262 documents containing 785 identifiers, with a gold standard created by expert annotators. Inter-annotator agreement was measured at over 90%.

Results:
Twenty groups submitted one to three runs each, for a total of 54 runs. Three systems achieved F-measures (balanced precision and recall) between 0.80 and 0.81. Combining the system outputs using simple voting schemes and classifiers obtained improved results; the best composite system achieved an F-measure of 0.92 with 10-fold cross-validation. A 'maximum recall' system based on the pooled responses of all participants gave a recall of 0.97 (with precision 0.23), identifying 763 out of 785 identifiers.

Conclusion:
Major advances for the BioCreative II gene normalization task include broader participation (20 versus 8 teams) and a pooled system performance comparable to human experts, at over 90% agreement. These results show promise as tools to link the literature with biological databases.
Overview of the protein-protein interaction annotation extraction task of BioCreative II
Authors: Krallinger Martin , Leitner Florian , Rodriguez-Penagos Carlos , Valencia Alfonso 
Published: September 2008

Abstract
Background:
The biomedical literature is the primary information source for manual protein-protein interaction annotations. Text-mining systems have been implemented to extract binary protein interactions from articles, but a comprehensive comparison between the different techniques as well as with manual curation was missing.

Results:
We designed a community challenge, the BioCreative II protein-protein interaction (PPI) task, based on the main steps of a manual protein interaction annotation workflow. It was structured into four distinct subtasks related to: (a) detection of protein interaction-relevant articles; (b) extraction and normalization of protein interaction pairs; (c) retrieval of the interaction detection methods used; and (d) retrieval of actual text passages that provide evidence for protein interactions. A total of 26 teams submitted runs for at least one of the proposed subtasks. In the interaction article detection subtask, the top scoring team reached an F-score of 0.78. In the interaction pair extraction and mapping to SwissProt, a precision of 0.37 (with recall of 0.33) was obtained. For associating articles with an experimental interaction detection method, an F-score of 0.65 was achieved. As for the retrieval of the PPI passages best summarizing a given protein interaction in full-text articles, 19% of the submissions returned by one of the runs corresponded to curator-selected sentences. Curators extracted only the passages that best summarized a given interaction, implying that many of the automatically extracted ones could contain interaction information but did not correspond to the most informative sentences.

Conclusion:
The BioCreative II PPI task is the first attempt to compare the performance of text-mining tools specific for each of the basic steps of the PPI extraction pipeline. The challenges identified range from problems in full-text format conversion of articles to difficulties in detecting interactor protein pairs and then linking them to their database records. Some limitations were also encountered when using a single (and possibly incomplete) reference database for protein normalization or when limiting search for interactor proteins to co-occurrence within a single sentence, when a mention might span neighboring sentences. Finally, distinguishing between novel, experimentally verified interactions (annotation relevant) and previously known interactions adds additional complexity to these tasks.
MINT and IntAct contribute to the Second BioCreative challenge: serving the text-mining community with high quality molecular interaction data
Authors: Chatr-aryamontri Andrew , Kerrien Samuel , Khadake Jyoti , Orchard Sandra , Ceol Arnaud , Licata Luana , Castagnoli Luisa , Costa Stefano , Derow Cathy , Huntley Rachael , Aranda Bruno , Leroy Catherine , Thorneycroft Dave , Apweiler Rolf , Cesareni Gianni , Hermjakob Henning 
Published: September 2008

Abstract
Background
In the absence of consolidated pipelines to archive biological data electronically, information dispersed in the literature must be captured by manual annotation. Unfortunately, manual annotation is time consuming and the coverage of published interaction data is therefore far from complete. The use of text-mining tools to identify relevant publications and to assist in the initial information extraction could help to improve the efficiency of the curation process and, as a consequence, the database coverage of data available in the literature. The 2006 BioCreative competition was aimed at evaluating text-mining procedures in comparison with manual annotation of protein-protein interactions.

Results
To aid the BioCreative protein-protein interaction task, IntAct and MINT (Molecular INTeraction) provided both the training and the test datasets. Data from both databases are comparable because they were curated according to the same standards. During the manual curation process, the major cause of data loss in mining the articles for information was ambiguity in the mapping of the gene names to stable UniProtKB database identifiers. It was also observed that most of the information about interactions was contained only within the full-text of the publication; hence, text mining of protein-protein interaction data will require the analysis of the full-text of the articles and cannot be restricted to the abstract.

Conclusion
The development of text-mining tools to extract protein-protein interaction information may increase the literature coverage achieved by manual curation. To support the text-mining community, databases will highlight those sentences within the articles that describe the interactions. These will supply data-miners with a high quality dataset for algorithm development. Furthermore, the dictionary of terms created by the BioCreative competitors could enrich the synonym list of the PSI-MI (Proteomics Standards Initiative-Molecular Interactions) controlled vocabulary, which is used by both databases to annotate their data content.
Introducing meta-services for biomedical information extraction
Authors: Leitner Florian , Krallinger Martin , Rodriguez-Penagos Carlos , Hakenberg Jörg , Plake Conrad , Kuo Cheng-Ju , Hsu Chun-Nan , Tsai Richard Tzong-Han , Hung Hsi-Chuan , Lau William W , Johnson Calvin A , Sætre Rune , Yoshida Kazuhiro , Chen Yan Hua , Kim Sun , Shin Soo-Yong , Zhang Byoung-Tak , Baumgartner William A , Hunter Lawrence , Haddow Barry , Matthews Michael , Wang Xinglong , Ruch Patrick , Ehrler Frédéric , Özgür Arzucan , Erkan Güneş , Radev Dragomir R , Krauthammer Michael , Luong ThaiBinh , Hoffmann Robert , Sander Chris , Valencia Alfonso 
Published: September 2008

AbstractWe introduce the first meta-service for information extraction in molecular biology, the BioCreative MetaServer (BCMS; http://bcms.bioinfo.cnio.es/). This prototype platform is a joint effort of 13 research groups and provides automatically generated annotations for PubMed/Medline abstracts. Annotation types cover gene names, gene IDs, species, and protein-protein interactions. The annotations are distributed by the meta-server in both human and machine readable formats (HTML/XML). This service is intended to be used by biomedical researchers and database annotators, and in biomedical language processing. The platform allows direct comparison, unified access, and result aggregation of the annotations.
Text mining for biology - the way forward: opinions from leading scientists
Authors: Altman Russ B , Bergman Casey M , Blake Judith , Blaschke Christian , Cohen Aaron , Gannon Frank , Grivell Les , Hahn Udo , Hersh William , Hirschman Lynette , Jensen Lars Juhl , Krallinger Martin , Mons Barend , O'Donoghue Seán I , Peitsch Manuel C , Rebholz-Schuhmann Dietrich , Shatkay Hagit , Valencia Alfonso 
Published: September 2008

AbstractThis article collects opinions from leading scientists about how text mining can provide better access to the biological literature, how the scientific community can help with this process, what the next steps are, and what role future BioCreative evaluations can play. The responses identify several broad themes, including the possibility of fusing literature and biological databases through text mining; the need for user interfaces tailored to different classes of users and supporting community-based annotation; the importance of scaling text mining technology and inserting it into larger workflows; and suggestions for additional challenge evaluations, new applications, and additional resources needed to make progress.
Concept recognition for extracting protein interaction relations from biomedical text
Authors: Baumgartner William A , Lu Zhiyong , Johnson Helen L , Caporaso J Gregory , Paquette Jesse , Lindemann Anna , White Elizabeth K , Medvedeva Olga , Cohen K Bretonnel , Hunter Lawrence 
Published: September 2008

Abstract
Background:
Reliable information extraction applications have been a long sought goal of the biomedical text mining community, a goal that if reached would provide valuable tools to benchside biologists in their increasingly difficult task of assimilating the knowledge contained in the biomedical literature. We present an integrated approach to concept recognition in biomedical text. Concept recognition provides key information that has been largely missing from previous biomedical information extraction efforts, namely direct links to well defined knowledge resources that explicitly cement the concept's semantics. The BioCreative II tasks discussed in this special issue have provided a unique opportunity to demonstrate the effectiveness of concept recognition in the field of biomedical language processing.

Results:
Through the modular construction of a protein interaction relation extraction system, we present several use cases of concept recognition in biomedical text, and relate these use cases to potential uses by the benchside biologist.

Conclusion:
Current information extraction technologies are approaching performance standards at which concept recognition can begin to deliver high quality data to the benchside biologist. Our system is available as part of the BioCreative Meta-Server project and on the internet http://bionlp.sourceforge.net.
David Wells
Authors: Hoopes Laura L. Mays 
Published: September 2008

Note from the Editor Educator Highlights for CBE—Life Sciences Education show how professors at different kinds of institutions educate students in life sciences with inspiration and panache. If you have a particularly creative teaching portfolio yourself, or if you wish to nominate an inspiring colleague to be profiled, please e-mail Laura Hoopes at lhoopes@pomona.edu.
Gregory Florant
Authors: Hoopes Laura L. Mays 
Published: September 2008

Note from the Editor Educator Highlights for CBE—Life Sciences Education show how professors at different kinds of institutions educate students in life sciences with inspiration and panache. If you have a particularly creative teaching portfolio yourself, or if you wish to nominate an inspiring colleague to be profiled, please e-mail Laura Hoopes at lhoopes@pomona.edu.
Applying Organisational Theory to Isolated, Confined and Extreme Settings
Authors: Sarris Aspa 
Published: September 2008

AbstractResearch on person–environment fit theory has largely developed within the context of people and organisations in urban settings. There has been little research of this kind within organisations in isolated and confined contexts. The purpose of this article was to examine the implications of person–environment fit theory within the context of the complementary fit and supplementary fit paradigm (Muchinsky & Monahan, 1987), for people working in isolated and remote organisational settings. Using a sample of Antarctic personnel, this study examined the extent to which psychological need fulfilment and subjective fit with Antarctic station culture were equally important in predicting individual attitudes, including satisfaction with being an expedition member, intent to return and willingness to recommend the Antarctic to others. Results showed that gender and subjective fit with Antarctic station culture predicted satisfaction with station membership. Results also showed that subjective fit predicted willingness to recommend the Antarctic as a good place to work, while psychological need fulfilment was not a predictor of any of the outcome variables examined. Results suggest that outcomes such as intention to return (or to stay) may be less relevant in such settings because of the unique and temporary nature of the experience and the work. Results also attest to the need for further development of organisational theories, including person–environment fit theory, using data collected from organisations in isolated, confined and extreme environmental contexts.