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biomedical NLP

Ahmed Allam, Michael Krauthammer
Motivation: Text and genomic data are composed of sequential tokens, such as words and nucleotides that give rise to higher order syntactic constructs. In this work, we aim at providing a comprehensive Python library implementing conditional random fields (CRFs), a class of probabilistic graphical models, for robust prediction of these constructs from sequential data. Results: Python Sequence Labeling (PySeqLab) is an open source package for performing supervised learning in structured prediction tasks...
July 21, 2017: Bioinformatics
Louise Deléger, Leonardo Campillos, Anne-Laure Ligozat, Aurélie Névéol
BACKGROUND: Knowledge representation frameworks are essential to the understanding of complex biomedical processes, and to the analysis of biomedical texts that describe them. Combined with natural language processing (NLP), they have the potential to contribute to retrospective studies by unlocking important phenotyping information contained in the narrative content of electronic health records (EHRs). This work aims to develop an extensive information representation scheme for clinical information contained in EHR narratives, and to support secondary use of EHR narrative data to answer clinical questions...
September 11, 2017: Journal of Biomedical Semantics
Gizem Sogancioglu, Hakime Öztürk, Arzucan Özgür
Motivation: The amount of information available in textual format is rapidly increasing in the biomedical domain. Therefore, natural language processing (NLP) applications are becoming increasingly important to facilitate the retrieval and analysis of these data. Computing the semantic similarity between sentences is an important component in many NLP tasks including text retrieval and summarization. A number of approaches have been proposed for semantic sentence similarity estimation for generic English...
July 15, 2017: Bioinformatics
Joshua Valdez, Michael Rueschman, Matthew Kim, Susan Redline, Satya S Sahoo
Extraction of structured information from biomedical literature is a complex and challenging problem due to the complexity of biomedical domain and lack of appropriate natural language processing (NLP) techniques. High quality domain ontologies model both data and metadata information at a fine level of granularity, which can be effectively used to accurately extract structured information from biomedical text. Extraction of provenance metadata, which describes the history or source of information, from published articles is an important task to support scientific reproducibility...
October 2016: On Move Meaningful Internet Syst
Gurusamy Murugesan, Sabenabanu Abdulkadhar, Balu Bhasuran, Jeyakumar Natarajan
Tagging biomedical entities such as gene, protein, cell, and cell-line is the first step and an important pre-requisite in biomedical literature mining. In this paper, we describe our hybrid named entity tagging approach namely BCC-NER (bidirectional, contextual clues named entity tagger for gene/protein mention recognition). BCC-NER is deployed with three modules. The first module is for text processing which includes basic NLP pre-processing, feature extraction, and feature selection. The second module is for training and model building with bidirectional conditional random fields (CRF) to parse the text in both directions (forward and backward) and integrate the backward and forward trained models using margin-infused relaxed algorithm (MIRA)...
December 2017: EURASIP Journal on Bioinformatics & Systems Biology
Ignacio Rubio-López, Roberto Costumero, Héctor Ambit, Consuelo Gonzalo-Martín, Ernestina Menasalvas, Alejandro Rodríguez González
Electronic Health Records (EHRs) are now being massively used in hospitals what has motivated current developments of new methods to process clinical narratives (unstructured data) making it possible to perform context-based searches. Current approaches to process the unstructured texts in EHRs are based in applying text mining or natural language processing (NLP) techniques over the data. In particular Named Entity Recognition (NER) is of paramount importance to retrieve specific biomedical concepts from the text providing the semantic type of the concept retrieved...
2017: Studies in Health Technology and Informatics
Rui Wang, Michelle K Sing, Reginald K Avery, Bruno S Souza, Minkyu Kim, Bradley D Olsen
Polymer networks are widely used from commodity to biomedical materials. The space-spanning, net-like structure gives polymer networks their advantageous mechanical and dynamic properties, the most essential factor that governs their responses to external electrical, thermal, and chemical stimuli. Despite the ubiquity of applications and a century of active research on these materials, the way that chemistry and processing interact to yield the final structure and the material properties of polymer networks is not fully understood, which leads to a number of classical challenges in the physical chemistry of gels...
December 20, 2016: Accounts of Chemical Research
Yanshan Wang, Stephen Wu, Dingcheng Li, Saeed Mehrabi, Hongfang Liu
In the era of digitalization, information retrieval (IR), which retrieves and ranks documents from large collections according to users' search queries, has been popularly applied in the biomedical domain. Building patient cohorts using electronic health records (EHRs) and searching literature for topics of interest are some IR use cases. Meanwhile, natural language processing (NLP), such as tokenization or Part-Of-Speech (POS) tagging, has been developed for processing clinical documents or biomedical literature...
October 2016: Journal of Biomedical Informatics
Denis Griffis, Chaitanya Shivade, Eric Fosler-Lussier, Albert M Lai
Sentence boundary detection (SBD) is a critical preprocessing task for many natural language processing (NLP) applications. However, there has been little work on evaluating how well existing methods for SBD perform in the clinical domain. We evaluate five popular off-the-shelf NLP toolkits on the task of SBD in various kinds of text using a diverse set of corpora, including the GENIA corpus of biomedical abstracts, a corpus of clinical notes used in the 2010 i2b2 shared task, and two general-domain corpora (the British National Corpus and Switchboard)...
2016: AMIA Summits on Translational Science Proceedings
Kirk Roberts, Mary Regina Boland, Lisiane Pruinelli, Jina Dcruz, Andrew Berry, Mattias Georgsson, Rebecca Hazen, Raymond F Sarmiento, Uba Backonja, Kun-Hsing Yu, Yun Jiang, Patricia Flatley Brennan
The field of biomedical informatics experienced a productive 2015 in terms of research. In order to highlight the accomplishments of that research, elicit trends, and identify shortcomings at a macro level, a 19-person team conducted an extensive review of the literature in clinical and consumer informatics. The result of this process included a year-in-review presentation at the American Medical Informatics Association Annual Symposium and a written report (see supplemental data). Key findings are detailed in the report and summarized here...
April 1, 2017: Journal of the American Medical Informatics Association: JAMIA
Sean F Gilmore, Craig D Blanchette, Tiffany M Scharadin, Greg L Hura, Amy Rasley, Michele Corzett, Chong-Xian Pan, Nicholas O Fischer, Paul T Henderson
Nanolipoprotein particles (NLPs) consist of a discoidal phospholipid lipid bilayer confined by an apolipoprotein belt. NLPs are a promising platform for a variety of biomedical applications due to their biocompatibility, size, definable composition, and amphipathic characteristics. However, poor serum stability hampers the use of NLPs for in vivo applications such as drug formulation. In this study, NLP stability was enhanced upon the incorporation and subsequent UV-mediated intermolecular cross-linking of photoactive DiynePC phospholipids in the lipid bilayer, forming cross-linked nanoparticles (X-NLPs)...
August 17, 2016: ACS Applied Materials & Interfaces
Robin McEntire, Debbie Szalkowski, James Butler, Michelle S Kuo, Meiping Chang, Man Chang, Darren Freeman, Sarah McQuay, Jagruti Patel, Michael McGlashen, Wendy D Cornell, Jinghai James Xu
External content sources such as MEDLINE(®), National Institutes of Health (NIH) grants and conference websites provide access to the latest breaking biomedical information, which can inform pharmaceutical and biotechnology company pipeline decisions. The value of the sites for industry, however, is limited by the use of the public internet, the limited synonyms, the rarity of batch searching capability and the disconnected nature of the sites. Fortunately, many sites now offer their content for download and we have developed an automated internal workflow that uses text mining and tailored ontologies for programmatic search and knowledge extraction...
May 2016: Drug Discovery Today
Eugene Tseytlin, Kevin Mitchell, Elizabeth Legowski, Julia Corrigan, Girish Chavan, Rebecca S Jacobson
BACKGROUND: Natural language processing (NLP) applications are increasingly important in biomedical data analysis, knowledge engineering, and decision support. Concept recognition is an important component task for NLP pipelines, and can be either general-purpose or domain-specific. We describe a novel, flexible, and general-purpose concept recognition component for NLP pipelines, and compare its speed and accuracy against five commonly used alternatives on both a biological and clinical corpus...
2016: BMC Bioinformatics
Eugene Kolker, Imre Janko, Elizabeth Montague, Roger Higdon, Elizabeth Stewart, John Choiniere, Aaron Lai, Mary Eckert, William Broomall, Natali Kolker
Gene/disease associations are a critical part of exploring disease causes and ultimately cures, yet the publications that might provide such information are too numerous to be manually reviewed. We present a software utility, MOPED-Digger, that enables focused human assessment of literature by applying natural language processing (NLP) to search for customized lists of genes and diseases in titles and abstracts from biomedical publications. The results are ranked lists of gene/disease co-appearances and the publications that support them...
December 2015: Omics: a Journal of Integrative Biology
Pepi Sfakianaki, Lefteris Koumakis, Stelios Sfakianakis, Galatia Iatraki, Giorgos Zacharioudakis, Norbert Graf, Kostas Marias, Manolis Tsiknakis
BACKGROUND: A plethora of publicly available biomedical resources do currently exist and are constantly increasing at a fast rate. In parallel, specialized repositories are been developed, indexing numerous clinical and biomedical tools. The main drawback of such repositories is the difficulty in locating appropriate resources for a clinical or biomedical decision task, especially for non-Information Technology expert users. In parallel, although NLP research in the clinical domain has been active since the 1960s, progress in the development of NLP applications has been slow and lags behind progress in the general NLP domain...
September 30, 2015: BMC Medical Informatics and Decision Making
Albert Park, Andrea L Hartzler, Jina Huh, David W McDonald, Wanda Pratt
BACKGROUND: The prevalence and value of patient-generated health text are increasing, but processing such text remains problematic. Although existing biomedical natural language processing (NLP) tools are appealing, most were developed to process clinician- or researcher-generated text, such as clinical notes or journal articles. In addition to being constructed for different types of text, other challenges of using existing NLP include constantly changing technologies, source vocabularies, and characteristics of text...
2015: Journal of Medical Internet Research
Thierry Hamon, Fleur Mougin, Natalia Grabar
With the recent and intensive research in the biomedical area, the knowledge accumulated is disseminated through various knowledge bases. Links between these knowledge bases are needed in order to use them jointly. Linked Data, SPARQL language, and interfaces in Natural Language question-answering provide interesting solutions for querying such knowledge bases. We propose a method for translating natural language questions in SPARQL queries. We use Natural Language Processing tools, semantic resources, and the RDF triples description...
2015: Studies in Health Technology and Informatics
Kai Zheng, V G Vinod Vydiswaran, Yang Liu, Yue Wang, Amber Stubbs, Özlem Uzuner, Anupama E Gururaj, Samuel Bayer, John Aberdeen, Anna Rumshisky, Serguei Pakhomov, Hongfang Liu, Hua Xu
OBJECTIVE: In recognition of potential barriers that may inhibit the widespread adoption of biomedical software, the 2014 i2b2 Challenge introduced a special track, Track 3 - Software Usability Assessment, in order to develop a better understanding of the adoption issues that might be associated with the state-of-the-art clinical NLP systems. This paper reports the ease of adoption assessment methods we developed for this track, and the results of evaluating five clinical NLP system submissions...
December 2015: Journal of Biomedical Informatics
Ioannis Korkontzelos, Dimitrios Piliouras, Andrew W Dowsey, Sophia Ananiadou
OBJECTIVE: Drug named entity recognition (NER) is a critical step for complex biomedical NLP tasks such as the extraction of pharmacogenomic, pharmacodynamic and pharmacokinetic parameters. Large quantities of high quality training data are almost always a prerequisite for employing supervised machine-learning techniques to achieve high classification performance. However, the human labour needed to produce and maintain such resources is a significant limitation. In this study, we improve the performance of drug NER without relying exclusively on manual annotations...
October 2015: Artificial Intelligence in Medicine
Yang Chen, Li Li, Guo-Qiang Zhang, Rong Xu
MOTIVATION: Discerning genetic contributions to diseases not only enhances our understanding of disease mechanisms, but also leads to translational opportunities for drug discovery. Recent computational approaches incorporate disease phenotypic similarities to improve the prediction power of disease gene discovery. However, most current studies used only one data source of human disease phenotype. We present an innovative and generic strategy for combining multiple different data sources of human disease phenotype and predicting disease-associated genes from integrated phenotypic and genomic data...
June 15, 2015: Bioinformatics
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