Chih-Hsuan Wei

tmChem: a high performance approach for chemical named entity recognition and normalization

Chemical compounds and drugs are an important class of entities in biomedical research with great potential in a wide range of applications, including clinical medicine. Locating chemical named entities in the literature is a useful step in chemical text mining pipelines for identifying the chemical mentions, their properties, and their relationships as discussed in the literature.We introduce the tmChem system, a chemical named entity recognizer created by combining two independent machine learning models in an ensemble. We use the corpus released as part of the recent CHEMDNER task to develop and evaluate tmChem, achieving a micro-averaged f-measure of 0.8739 on the CEM subtask (mention-level evaluation) and 0.8745 f-measure on the CDI subtask (abstract-level evaluation). We also report a high-recall combination (0.9212 for CEM and 0.9224 for CDI). tmChem achieved the highest f-measure reported in the CHEMDNER task for the CEM subtask, and the high recall variant achieved the highest recall on both the CEM and CDI tasks.We report that tmChem is a state-of-the-art tool for chemical named entity recognition and that performance for chemical named entity recognition has now tied (or exceeded) the performance previously reported for genes and diseases. Future research should focus on tighter integration between the named entity recognition and normalization steps for improved performance.The source code and a trained model for both models of tmChem is available at: http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmChem. The results of running tmChem (Model 2) on PubMed are available in PubTator: http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/PubTator

Assessing the state of the art in biomedical relation extraction: overview of the BioCreative V chemical-disease relation (CDR) task.

Manually curating chemicals, diseases and their relationships is significantly important to biomedical research, but it is plagued by its high cost and the rapid growth of the biomedical literature. In recent years, there has been a growing interest in developing computational approaches for automatic chemical-disease relation (CDR) extraction. Despite these attempts, the lack of a comprehensive benchmarking dataset has limited the comparison of different techniques in order to assess and advance the current state-of-the-art. To this end, we organized a challenge task through BioCreative V to automatically extract CDRs from the literature. We designed two challenge tasks: disease named entity recognition (DNER) and chemical-induced disease (CID) relation extraction. To assist system development and assessment, we created a large annotated text corpus that consisted of human annotations of chemicals, diseases and their interactions from 1500 PubMed articles. 34 teams worldwide participated in the CDR task: 16 (DNER) and 18 (CID). The best systems achieved an F-score of 86.46% for the DNER task—a result that approaches the human inter-annotator agreement (0.8875)—and an F-score of 57.03% for the CID task, the highest results ever reported for such tasks. When combining team results via machine learning, the ensemble system was able to further improve over the best team results by achieving 88.89% and 62.80% in F-score for the DNER and CID task, respectively. Additionally, another novel aspect of our evaluation is to test each participating system’s ability to return real-time results: the average response time for each team’s DNER and CID web service systems were 5.6 and 9.3u2009s, respectively. Most teams used hybrid systems for their submissions based on machining learning. Given the level of participation and results, we found our task to be successful in engaging the text-mining research community, producing a large annotated corpus and improving the results of automatic disease recognition and CDR extraction. Database URL: http://www.biocreative.org/tasks/biocreative-v/track-3-cdr/

BioCreative V CDR task corpus: a resource for chemical disease relation extraction

Community-run, formal evaluations and manually annotated text corpora are critically important for advancing biomedical text-mining research. Recently in BioCreative V, a new challenge was organized for the tasks of disease named entity recognition (DNER) and chemical-induced disease (CID) relation extraction. Given the nature of both tasks, a test collection is required to contain both disease/chemical annotations and relation annotations in the same set of articles. Despite previous efforts in biomedical corpus construction, none was found to be sufficient for the task. Thus, we developed our own corpus called BC5CDR during the challenge by inviting a team of Medical Subject Headings (MeSH) indexers for disease/chemical entity annotation and Comparative Toxicogenomics Database (CTD) curators for CID relation annotation. To ensure high annotation quality and productivity, detailed annotation guidelines and automatic annotation tools were provided. The resulting BC5CDR corpus consists of 1500 PubMed articles with 4409 annotated chemicals, 5818 diseases and 3116 chemical-disease interactions. Each entity annotation includes both the mention text spans and normalized concept identifiers, using MeSH as the controlled vocabulary. To ensure accuracy, the entities were first captured independently by two annotators followed by a consensus annotation: The average inter-annotator agreement (IAA) scores were 87.49% and 96.05% for the disease and chemicals, respectively, in the test set according to the Jaccard similarity coefficient. Our corpus was successfully used for the BioCreative V challenge tasks and should serve as a valuable resource for the text-mining research community. Database URL: http://www.biocreative.org/tasks/biocreative-v/track-3-cdr/

BC4GO: a full-text corpus for the BioCreative IV GO task

Gene function curation via Gene Ontology (GO) annotation is a common task among Model Organism Database groups. Owing to its manual nature, this task is considered one of the bottlenecks in literature curation. There have been many previous attempts at automatic identification of GO terms and supporting information from full text. However, few systems have delivered an accuracy that is comparable with humans. One recognized challenge in developing such systems is the lack of marked sentence-level evidence text that provides the basis for making GO annotations. We aim to create a corpus that includes the GO evidence text along with the three core elements of GO annotations: (i) a gene or gene product, (ii) a GO term and (iii) a GO evidence code. To ensure our results are consistent with real-life GO data, we recruited eight professional GO curators and asked them to follow their routine GO annotation protocols. Our annotators marked up more than 5000 text passages in 200 articles for 1356 distinct GO terms. For evidence sentence selection, the inter-annotator agreement (IAA) results are 9.3% (strict) and 42.7% (relaxed) in F1-measures. For GO term selection, the IAAs are 47% (strict) and 62.9% (hierarchical). Our corpus analysis further shows that abstracts contain ∼10% of relevant evidence sentences and 30% distinct GO terms, while the Results/Experiment section has nearly 60% relevant sentences and >70% GO terms. Further, of those evidence sentences found in abstracts, less than one-third contain enough experimental detail to fulfill the three core criteria of a GO annotation. This result demonstrates the need of using full-text articles for text mining GO annotations. Through its use at the BioCreative IV GO (BC4GO) task, we expect our corpus to become a valuable resource for the BioNLP research community. Database URL: http://www.biocreative.org/resources/corpora/bc-iv-go-task-corpus/.

On expert curation and scalability: UniProtKB/Swiss-Prot as a case study

Motivation Biological knowledgebases, such as UniProtKB/Swiss‐Prot, constitute an essential component of daily scientific research by offering distilled, summarized and computable knowledge extracted from the literature by expert curators. While knowledgebases play an increasingly important role in the scientific community, their ability to keep up with the growth of biomedical literature is under scrutiny. Using UniProtKB/Swiss‐Prot as a case study, we address this concern via multiple literature triage approaches. Results With the assistance of the PubTator text‐mining tool, we tagged more than 10 000 articles to assess the ratio of papers relevant for curation. We first show that curators read and evaluate many more papers than they curate, and that measuring the number of curated publications is insufficient to provide a complete picture as demonstrated by the fact that 8000‐10 000 papers are curated in UniProt each year while curators evaluate 50 000‐70 000 papers per year. We show that 90% of the papers in PubMed are out of the scope of UniProt, that a maximum of 2‐3% of the papers indexed in PubMed each year are relevant for UniProt curation, and that, despite appearances, expert curation in UniProt is scalable. Availability and implementation UniProt is freely available at http://www.uniprot.org/. Contact sylvain.poux@sib.swiss Supplementary information Supplementary data are available at Bioinformatics online.

SimConcept: a hybrid approach for simplifying composite named entities in biomedicine

Many text-mining studies have focused on the issue of named entity recognition and normalization, especially in the field of biomedical natural language processing. However, entity recognition is a complicated and difficult task in biomedical text. One particular challenge is to identify and resolve composite named entities, where a single span refers to more than one concept (e.g., BRCA1/2). Most bioconcept recognition and normalization studies have either ignored this issue, used simple ad-hoc rules, or only handled coordination ellipsis, which is only one of the many types of composite mentions studied in this work. No systematic methods for simplifying composite mentions have been previously reported, making a robust approach greatly needed. To this end, we propose a hybrid approach by integrating a machine learning model with a pattern identification strategy to identify the antecedent and conjuncts regions of a concept mention, and then reassemble the composite mention using those identified regions. Our method, which we have named SimConcept, is the first method to systematically handle most types of composite mentions. Our method achieves high performance in identifying and resolving composite mentions for three fundamental biological entities: genes (89.29% in F-measure), diseases (85.52% in F-measure) and chemicals (84.04% in F-measure). Furthermore, our results show that, using our SimConcept method can subsequently help improve the performance of gene and disease concept recognition and normalization.

Improving chemical disease relation extraction with rich features and weakly labeled data

BackgroundDue to the importance of identifying relations between chemicals and diseases for new drug discovery and improving chemical safety, there has been a growing interest in developing automatic relation extraction systems for capturing these relations from the rich and rapid-growing biomedical literature. In this work we aim to build on current advances in named entity recognition and a recent BioCreative effort to further improve the state of the art in biomedical relation extraction, in particular for the chemical-induced disease (CID) relations.ResultsWe propose a rich-feature approach with Support Vector Machine to aid in the extraction of CIDs from PubMed articles. Our feature vector includes novel statistical features, linguistic knowledge, and domain resources. We also incorporate the output of a rule-based system as features, thus combining the advantages of rule- and machine learning-based systems. Furthermore, we augment our approach with automatically generated labeled text from an existing knowledge base to improve performance without additional cost for corpus construction. To evaluate our system, we perform experiments on the human-annotated BioCreative V benchmarking dataset and compare with previous results. When trained using only BioCreative V training and development sets, our system achieves an F-score of 57.51xa0%, which already compares favorably to previous methods. Our system performance was further improved to 61.01xa0% in F-score when augmented with additional automatically generated weakly labeled data.ConclusionsOur text-mining approach demonstrates state-of-the-art performance in disease-chemical relation extraction. More importantly, this work exemplifies the use of (freely available) curated document-level annotations in existing biomedical databases, which are largely overlooked in text-mining system development.

tmVar 2.0: integrating genomic variant information from literature with dbSNP and ClinVar for precision medicine

MotivationnDespite significant efforts in expert curation, clinical relevance about most of the 154 million dbSNP reference variants (RS) remains unknown. However, a wealth of knowledge about the variant biological function/disease impact is buried in unstructured literature data. Previous studies have attempted to harvest and unlock such information with text-mining techniques but are of limited use because their mutation extraction results are not standardized or integrated with curated data.nnnResultsnWe propose an automatic method to extract and normalize variant mentions to unique identifiers (dbSNP RSIDs). Our method, in benchmarking results, demonstrates a high F-measure of ∼90% and compared favorably to the state of the art. Next, we applied our approach to the entire PubMed and validated the results by verifying that each extracted variant-gene pair matched the dbSNP annotation based on mapped genomic position, and by analyzing variants curated in ClinVar. We then determined which text-mined variants and genes constituted novel discoveries. Our analysis reveals 41u2009889 RS numbers (associated with 9151 genes) not found in ClinVar. Moreover, we obtained a rich set worth further review: 12u2009462 rare variants (MAFu2009≤u20090.01) in 3849 genes which are presumed to be deleterious and not frequently found in the general population. To our knowledge, this is the first large-scale study to analyze and integrate text-mined variant data with curated knowledge in existing databases. Our results suggest that databases can be significantly enriched by text mining and that the combined information can greatly assist human efforts in evaluating/prioritizing variants in genomic research.nnnAvailability and implementationnThe tmVar 2.0 source code and corpus are freely available at https://www.ncbi.nlm.nih.gov/research/bionlp/Tools/tmvar/.nnnContactnzhiyong.lu@nih.gov.

Beyond accuracy: creating interoperable and scalable text-mining web services

UNLABELLEDnThe biomedical literature is a knowledge-rich resource and an important foundation for future research. With over 24 million articles in PubMed and an increasing growth rate, research in automated text processing is becoming increasingly important. We report here our recently developed web-based text mining services for biomedical concept recognition and normalization. Unlike most text-mining software tools, our web services integrate several state-of-the-art entity tagging systems (DNorm, GNormPlus, SR4GN, tmChem and tmVar) and offer a batch-processing mode able to process arbitrary text input (e.g. scholarly publications, patents and medical records) in multiple formats (e.g. BioC). We support multiple standards to make our service interoperable and allow simpler integration with other text-processing pipelines. To maximize scalability, we have preprocessed all PubMed articles, and use a computer cluster for processing large requests of arbitrary text.nnnAVAILABILITY AND IMPLEMENTATIONnOur text-mining web service is freely available at http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmTools/#curlnnnCONTACTn: Zhiyong.Lu@nih.gov.

Mining chemical patents with an ensemble of open systems

The significant amount of medicinal chemistry information contained in patents makes them an attractive target for text mining. In this manuscript, we describe systems for named entity recognition (NER) of chemicals and genes/proteins in patents, using the CEMP (for chemicals) and GPRO (for genes/proteins) corpora provided by the CHEMDNER task at BioCreative V. Our chemical NER system is an ensemble of five open systems, including both versions of tmChem, our previous work on chemical NER. Their output is combined using a machine learning classification approach. Our chemical NER system obtained 0.8752 precision and 0.9129 recall, for 0.8937 f-score on the CEMP task. Our gene/protein NER system is an extension of our previous work for gene and protein NER, GNormPlus. This system obtained a performance of 0.8143 precision and 0.8141 recall, for 0.8137 f-score on the GPRO task. Both systems achieved the highest performance in their respective tasks at BioCreative V. We conclude that an ensemble of independently-created open systems is sufficiently diverse to significantly improve performance over any individual system, even when they use a similar approach. Database URL: http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmTools/.