PMC text mining subset in BioC: about three million full-text articles and growing.

MOTIVATION: Interest in text mining full-text biomedical research articles is growing. To facilitate automated processing of nearly 3 million full-text articles (in PubMed Central® Open Access and Author Manuscript subsets) and to improve interoperability, we convert these articles to BioC, a community-driven simple data structure in either XML or JavaScript Object Notation format for conveniently sharing text and annotations. RESULTS: The resultant articles can be downloaded via both File Transfer Protocol for bulk access and a Web API for updates or a more focused collection. Since the availability of the Web API in 2017, our BioC collection has been widely used by the research community. AVAILABILITY AND IMPLEMENTATION: https://www.ncbi.nlm.nih.gov/research/bionlp/APIs/BioC-PMC/.

DNorm: disease name normalization with pairwise learning to rank.

MOTIVATION: Despite the central role of diseases in biomedical research, there have been much fewer attempts to automatically determine which diseases are mentioned in a text-the task of disease name normalization (DNorm)-compared with other normalization tasks in biomedical text mining research. METHODS: In this article we introduce the first machine learning approach for DNorm, using the NCBI disease corpus and the MEDIC vocabulary, which combines MeSH® and OMIM. Our method is a high-performing and mathematically principled framework for learning similarities between mentions and concept names directly from training data. The technique is based on pairwise learning to rank, which has not previously been applied to the normalization task but has proven successful in large optimization problems for information retrieval. RESULTS: We compare our method with several techniques based on lexical normalization and matching, MetaMap and Lucene. Our algorithm achieves 0.782 micro-averaged F-measure and 0.809 macro-averaged F-measure, an increase over the highest performing baseline method of 0.121 and 0.098, respectively. AVAILABILITY: The source code for DNorm is available at http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/DNorm, along with a web-based demonstration and links to the NCBI disease corpus. Results on PubMed abstracts are available in PubTator: http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/PubTator .

A context-blocks model for identifying clinical relationships in patient records.

BACKGROUND: Patient records contain valuable information regarding explanation of diagnosis, progression of disease, prescription and/or effectiveness of treatment, and more. Automatic recognition of clinically important concepts and the identification of relationships between those concepts in patient records are preliminary steps for many important applications in medical informatics, ranging from quality of care to hypothesis generation. METHODS: In this work we describe an approach that facilitates the automatic recognition of eight relationships defined between medical problems, treatments and tests. Unlike the traditional bag-of-words representation, in this work, we represent a relationship with a scheme of five distinct context-blocks determined by the position of concepts in the text. As a preliminary step to relationship recognition, and in order to provide an end-to-end system, we also addressed the automatic extraction of medical problems, treatments and tests. Our approach combined the outcome of a statistical model for concept recognition and simple natural language processing features in a conditional random fields model. A set of 826 patient records from the 4th i2b2 challenge was used for training and evaluating the system. RESULTS: Results show that our concept recognition system achieved an F-measure of 0.870 for exact span concept detection. Moreover the context-block representation of relationships was more successful (F-Measure = 0.775) at identifying relationships than bag-of-words (F-Measure = 0.402). Most importantly, the performance of the end-to-end system of relationship extraction using automatically extracted concepts (F-Measure = 0.704) was comparable to that obtained using manually annotated concepts (F-Measure = 0.711), and their difference was not statistically significant. CONCLUSIONS: We extracted important clinical relationships from text in an automated manner, starting with concept recognition, and ending with relationship identification. The advantage of the context-blocks representation scheme was the correct management of word position information, which may be critical in identifying certain relationships. Our results may serve as benchmark for comparison to other systems developed on i2b2 challenge data. Finally, our system may serve as a preliminary step for other discovery tasks in medical informatics.

Click-words: learning to predict document keywords from a user perspective.

MOTIVATION: Recognizing words that are key to a document is important for ranking relevant scientific documents. Traditionally, important words in a document are either nominated subjectively by authors and indexers or selected objectively by some statistical measures. As an alternative, we propose to use documents' words popularity in user queries to identify click-words, a set of prominent words from the users' perspective. Although they often overlap, click-words differ significantly from other document keywords. RESULTS: We developed a machine learning approach to learn the unique characteristics of click-words. Each word was represented by a set of features that included different types of information, such as semantic type, part of speech tag, term frequency-inverse document frequency (TF-IDF) weight and location in the abstract. We identified the most important features and evaluated our model using 6 months of PubMed click-through logs. Our results suggest that, in addition to carrying high TF-IDF weight, click-words tend to be biomedical entities, to exist in article titles, and to occur repeatedly in article abstracts. Given the abstract and title of a document, we are able to accurately predict the words likely to appear in user queries that lead to document clicks.

Semi-automatic semantic annotation of PubMed queries: a study on quality, efficiency, satisfaction.

Information processing algorithms require significant amounts of annotated data for training and testing. The availability of such data is often hindered by the complexity and high cost of production. In this paper, we investigate the benefits of a state-of-the-art tool to help with the semantic annotation of a large set of biomedical queries. Seven annotators were recruited to annotate a set of 10,000 PubMed® queries with 16 biomedical and bibliographic categories. About half of the queries were annotated from scratch, while the other half were automatically pre-annotated and manually corrected. The impact of the automatic pre-annotations was assessed on several aspects of the task: time, number of actions, annotator satisfaction, inter-annotator agreement, quality and number of the resulting annotations. The analysis of annotation results showed that the number of required hand annotations is 28.9% less when using pre-annotated results from automatic tools. As a result, the overall annotation time was substantially lower when pre-annotations were used, while inter-annotator agreement was significantly higher. In addition, there was no statistically significant difference in the semantic distribution or number of annotations produced when pre-annotations were used. The annotated query corpus is freely available to the research community. This study shows that automatic pre-annotations are found helpful by most annotators. Our experience suggests using an automatic tool to assist large-scale manual annotation projects. This helps speed-up the annotation time and improve annotation consistency while maintaining high quality of the final annotations.

Overview of the BioCreative VI Precision Medicine Track: mining protein interactions and mutations for precision medicine.

The Precision Medicine Initiative is a multicenter effort aiming at formulating personalized treatments leveraging on individual patient data (clinical, genome sequence and functional genomic data) together with the information in large knowledge bases (KBs) that integrate genome annotation, disease association studies, electronic health records and other data types. The biomedical literature provides a rich foundation for populating these KBs, reporting genetic and molecular interactions that provide the scaffold for the cellular regulatory systems and detailing the influence of genetic variants in these interactions. The goal of BioCreative VI Precision Medicine Track was to extract this particular type of information and was organized in two tasks: (i) document triage task, focused on identifying scientific literature containing experimentally verified protein-protein interactions (PPIs) affected by genetic mutations and (ii) relation extraction task, focused on extracting the affected interactions (protein pairs). To assist system developers and task participants, a large-scale corpus of PubMed documents was manually annotated for this task. Ten teams worldwide contributed 22 distinct text-mining models for the document triage task, and six teams worldwide contributed 14 different text-mining systems for the relation extraction task. When comparing the text-mining system predictions with human annotations, for the triage task, the best F-score was 69.06%, the best precision was 62.89%, the best recall was 98.0% and the best average precision was 72.5%. For the relation extraction task, when taking homologous genes into account, the best F-score was 37.73%, the best precision was 46.5% and the best recall was 54.1%. Submitted systems explored a wide range of methods, from traditional rule-based, statistical and machine learning systems to state-of-the-art deep learning methods. Given the level of participation and the individual team results we find the precision medicine track to be successful in engaging the text-mining research community. In the meantime, the track produced a manually annotated corpus of 5509 PubMed documents developed by BioGRID curators and relevant for precision medicine. The data set is freely available to the community, and the specific interactions have been integrated into the BioGRID data set. In addition, this challenge provided the first results of automatically identifying PubMed articles that describe PPI affected by mutations, as well as extracting the affected relations from those articles. Still, much progress is needed for computer-assisted precision medicine text mining to become mainstream. Future work should focus on addressing the remaining technical challenges and incorporating the practical benefits of text-mining tools into real-world precision medicine information-related curation.

The BioC-BioGRID corpus: full text articles annotated for curation of protein-protein and genetic interactions.

A great deal of information on the molecular genetics and biochemistry of model organisms has been reported in the scientific literature. However, this data is typically described in free text form and is not readily amenable to computational analyses. To this end, the BioGRID database systematically curates the biomedical literature for genetic and protein interaction data. This data is provided in a standardized computationally tractable format and includes structured annotation of experimental evidence. BioGRID curation necessarily involves substantial human effort by expert curators who must read each publication to extract the relevant information. Computational text-mining methods offer the potential to augment and accelerate manual curation. To facilitate the development of practical text-mining strategies, a new challenge was organized in BioCreative V for the BioC task, the collaborative Biocurator Assistant Task. This was a non-competitive, cooperative task in which the participants worked together to build BioC-compatible modules into an integrated pipeline to assist BioGRID curators. As an integral part of this task, a test collection of full text articles was developed that contained both biological entity annotations (gene/protein and organism/species) and molecular interaction annotations (protein-protein and genetic interactions (PPIs and GIs)). This collection, which we call the BioC-BioGRID corpus, was annotated by four BioGRID curators over three rounds of annotation and contains 120 full text articles curated in a dataset representing two major model organisms, namely budding yeast and human. The BioC-BioGRID corpus contains annotations for 6409 mentions of genes and their Entrez Gene IDs, 186 mentions of organism names and their NCBI Taxonomy IDs, 1867 mentions of PPIs and 701 annotations of PPI experimental evidence statements, 856 mentions of GIs and 399 annotations of GI evidence statements. The purpose, characteristics and possible future uses of the BioC-BioGRID corpus are detailed in this report.Database URL: http://bioc.sourceforge.net/BioC-BioGRID.html.

Crowdsourcing and curation: perspectives from biology and natural language processing.

Crowdsourcing is increasingly utilized for performing tasks in both natural language processing and biocuration. Although there have been many applications of crowdsourcing in these fields, there have been fewer high-level discussions of the methodology and its applicability to biocuration. This paper explores crowdsourcing for biocuration through several case studies that highlight different ways of leveraging 'the crowd'; these raise issues about the kind(s) of expertise needed, the motivations of participants, and questions related to feasibility, cost and quality. The paper is an outgrowth of a panel session held at BioCreative V (Seville, September 9-11, 2015). The session consisted of four short talks, followed by a discussion. In their talks, the panelists explored the role of expertise and the potential to improve crowd performance by training; the challenge of decomposing tasks to make them amenable to crowdsourcing; and the capture of biological data and metadata through community editing.Database URL: http://www.mitre.org/publications/technical-papers/crowdsourcing-and-curation-perspectives.

BioCreative V BioC track overview: collaborative biocurator assistant task for BioGRID.

BioC is a simple XML format for text, annotations and relations, and was developed to achieve interoperability for biomedical text processing. Following the success of BioC in BioCreative IV, the BioCreative V BioC track addressed a collaborative task to build an assistant system for BioGRID curation. In this paper, we describe the framework of the collaborative BioC task and discuss our findings based on the user survey. This track consisted of eight subtasks including gene/protein/organism named entity recognition, protein-protein/genetic interaction passage identification and annotation visualization. Using BioC as their data-sharing and communication medium, nine teams, world-wide, participated and contributed either new methods or improvements of existing tools to address different subtasks of the BioC track. Results from different teams were shared in BioC and made available to other teams as they addressed different subtasks of the track. In the end, all submitted runs were merged using a machine learning classifier to produce an optimized output. The biocurator assistant system was evaluated by four BioGRID curators in terms of practical usability. The curators' feedback was overall positive and highlighted the user-friendly design and the convenient gene/protein curation tool based on text mining.Database URL: http://www.biocreative.org/tasks/biocreative-v/track-1-bioc/.

Finding abbreviations in biomedical literature: three BioC-compatible modules and four BioC-formatted corpora.

BioC is a recently created XML format to share text data and annotations, and an accompanying input/output library to promote interoperability of data and tools for natural language processing of biomedical text. This article reports the use of BioC to address a common challenge in processing biomedical text information-that of frequent entity name abbreviation. We selected three different abbreviation definition identification modules, and used the publicly available BioC code to convert these independent modules into BioC-compatible components that interact seamlessly with BioC-formatted data, and other BioC-compatible modules. In addition, we consider four manually annotated corpora of abbreviations in biomedical text: the Ab3P corpus of 1250 PubMed abstracts, the BIOADI corpus of 1201 PubMed abstracts, the old MEDSTRACT corpus of 199 PubMed(®) citations and the Schwartz and Hearst corpus of 1000 PubMed abstracts. Annotations in these corpora have been re-evaluated by four annotators and their consistency and quality levels have been improved. We converted them to BioC-format and described the representation of the annotations. These corpora are used to measure the three abbreviation-finding algorithms and the results are given. The BioC-compatible modules, when compared with their original form, have no difference in their efficiency, running time or any other comparable aspects. They can be conveniently used as a common pre-processing step for larger multi-layered text-mining endeavors. Database URL: Code and data are available for download at the BioC site: http://bioc.sourceforge.net.

Natural language processing pipelines to annotate BioC collections with an application to the NCBI disease corpus.

BioC is a new format and associated code libraries for sharing text and annotations. We have implemented BioC natural language preprocessing pipelines in two popular programming languages: C++ and Java. The current implementations interface with the well-known MedPost and Stanford natural language processing tool sets. The pipeline functionality includes sentence segmentation, tokenization, part-of-speech tagging, lemmatization and sentence parsing. These pipelines can be easily integrated along with other BioC programs into any BioC compliant text mining systems. As an application, we converted the NCBI disease corpus to BioC format, and the pipelines have successfully run on this corpus to demonstrate their functionality. Code and data can be downloaded from http://bioc.sourceforge.net. Database URL: http://bioc.sourceforge.net.

BioC implementations in Go, Perl, Python and Ruby.

As part of a communitywide effort for evaluating text mining and information extraction systems applied to the biomedical domain, BioC is focused on the goal of interoperability, currently a major barrier to wide-scale adoption of text mining tools. BioC is a simple XML format, specified by DTD, for exchanging data for biomedical natural language processing. With initial implementations in C++ and Java, BioC provides libraries of code for reading and writing BioC text documents and annotations. We extend BioC to Perl, Python, Go and Ruby. We used SWIG to extend the C++ implementation for Perl and one Python implementation. A second Python implementation and the Ruby implementation use native data structures and libraries. BioC is also implemented in the Google language Go. BioC modules are functional in all of these languages, which can facilitate text mining tasks. BioC implementations are freely available through the BioC site: http://bioc.sourceforge.net. Database URL: http://bioc.sourceforge.net/

Author Name Disambiguation for PubMed.

Log analysis shows that PubMed users frequently use author names in queries for retrieving scientific literature. However, author name ambiguity may lead to irrelevant retrieval results. To improve the PubMed user experience with author name queries, we designed an author name disambiguation system consisting of similarity estimation and agglomerative clustering. A machine-learning method was employed to score the features for disambiguating a pair of papers with ambiguous names. These features enable the computation of pairwise similarity scores to estimate the probability of a pair of papers belonging to the same author, which drives an agglomerative clustering algorithm regulated by 2 factors: name compatibility and probability level. With transitivity violation correction, high precision author clustering is achieved by focusing on minimizing false-positive pairing. Disambiguation performance is evaluated with manual verification of random samples of pairs from clustering results. When compared with a state-of-the-art system, our evaluation shows that among all the pairs the lumping error rate drops from 10.1% to 2.2% for our system, while the splitting error rises from 1.8% to 7.7%. This results in an overall error rate of 9.9%, compared with 11.9% for the state-of-the-art method. Other evaluations based on gold standard data also show the increase in accuracy of our clustering. We attribute the performance improvement to the machine-learning method driven by a large-scale training set and the clustering algorithm regulated by a name compatibility scheme preferring precision. With integration of the author name disambiguation system into the PubMed search engine, the overall click-through-rate of PubMed users on author name query results improved from 34.9% to 36.9%.

BioC: a minimalist approach to interoperability for biomedical text processing.

A vast amount of scientific information is encoded in natural language text, and the quantity of such text has become so great that it is no longer economically feasible to have a human as the first step in the search process. Natural language processing and text mining tools have become essential to facilitate the search for and extraction of information from text. This has led to vigorous research efforts to create useful tools and to create humanly labeled text corpora, which can be used to improve such tools. To encourage combining these efforts into larger, more powerful and more capable systems, a common interchange format to represent, store and exchange the data in a simple manner between different language processing systems and text mining tools is highly desirable. Here we propose a simple extensible mark-up language format to share text documents and annotations. The proposed annotation approach allows a large number of different annotations to be represented including sentences, tokens, parts of speech, named entities such as genes or diseases and relationships between named entities. In addition, we provide simple code to hold this data, read it from and write it back to extensible mark-up language files and perform some sample processing. We also describe completed as well as ongoing work to apply the approach in several directions. Code and data are available at http://bioc.sourceforge.net/. Database URL: http://bioc.sourceforge.net/

An evolutionary algorithm approach for feature generation from sequence data and its application to DNA splice site prediction.

Associating functional information with biological sequences remains a challenge for machine learning methods. The performance of these methods often depends on deriving predictive features from the sequences sought to be classified. Feature generation is a difficult problem, as the connection between the sequence features and the sought property is not known a priori. It is often the task of domain experts or exhaustive feature enumeration techniques to generate a few features whose predictive power is then tested in the context of classification. This paper proposes an evolutionary algorithm to effectively explore a large feature space and generate predictive features from sequence data. The effectiveness of the algorithm is demonstrated on an important component of the gene-finding problem, DNA splice site prediction. This application is chosen due to the complexity of the features needed to obtain high classification accuracy and precision. Our results test the effectiveness of the obtained features in the context of classification by Support Vector Machines and show significant improvement in accuracy and precision over state-of-the-art approaches.

Understanding PubMed user search behavior through log analysis.

This article reports on a detailed investigation of PubMed users' needs and behavior as a step toward improving biomedical information retrieval. PubMed is providing free service to researchers with access to more than 19 million citations for biomedical articles from MEDLINE and life science journals. It is accessed by millions of users each day. Efficient search tools are crucial for biomedical researchers to keep abreast of the biomedical literature relating to their own research. This study provides insight into PubMed users' needs and their behavior. This investigation was conducted through the analysis of one month of log data, consisting of more than 23 million user sessions and more than 58 million user queries. Multiple aspects of users' interactions with PubMed are characterized in detail with evidence from these logs. Despite having many features in common with general Web searches, biomedical information searches have unique characteristics that are made evident in this study. PubMed users are more persistent in seeking information and they reformulate queries often. The three most frequent types of search are search by author name, search by gene/protein, and search by disease. Use of abbreviation in queries is very frequent. Factors such as result set size influence users' decisions. Analysis of characteristics such as these plays a critical role in identifying users' information needs and their search habits. In turn, such an analysis also provides useful insight for improving biomedical information retrieval.Database URL:http://www.ncbi.nlm.nih.gov/PubMed.