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Parse biomedical

Dongdong Sun, Minghui Wang, Ao Li
Due to the importance of post-translational modifications (PTMs) in human health and diseases, PTMs are regularly reported in the biomedical literature. However, the continuing and rapid pace of expansion of this literature brings a huge challenge for researchers and database curators. Therefore, there is a pressing need to aid them in identifying relevant PTM information more efficiently by using a text mining system. So far, only a few web servers are available for mining information of a very limited number of PTMs, which are based on simple pattern matching or pre-defined rules...
October 2017: Journal of Bioinformatics and Computational Biology
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
Jason Cory Brunson, Xiaoyan Wang, Reinhard C Laubenbacher
BACKGROUND: Investigations into the factors behind coauthorship growth in biomedical research have mostly focused on specific disciplines or journals, and have rarely controlled for factors in combination or considered changes in their effects over time. Observers often attribute the growth to the increasing complexity or competition (or both) of research practices, but few attempts have been made to parse the contributions of these two likely causes. OBJECTIVES: We aimed to assess the effects of complexity and competition on the incidence and growth of coauthorship, using a sample of the biomedical literature spanning multiple journals and disciplines...
2017: PloS One
Allison Piovesan, Maria Caracausi, Francesca Antonaros, Maria Chiara Pelleri, Lorenza Vitale
We release GeneBase 1.1, a local tool with a graphical interface useful for parsing, structuring and indexing data from the National Center for Biotechnology Information (NCBI) Gene data bank. Compared to its predecessor GeneBase (1.0), GeneBase 1.1 now allows dynamic calculation and summarization in terms of median, mean, standard deviation and total for many quantitative parameters associated with genes, gene transcripts and gene features (exons, introns, coding sequences, untranslated regions). GeneBase 1...
2016: Database: the Journal of Biological Databases and Curation
Yu-O Yang
The purpose of this column is to explore the experience of being pregnant as talked about by women in Taiwan. In nursing and healthcare in general, there is a tendency to objectify the experience from a biomedical view, focusing on physiological changes and symptoms. A human science approach is here applied to help understand the themes that were evident in the comments of 23 pregnant Taiwanese women, about what being pregnant was like for them. The perspective used for the explanation was Parse's humanbecoming paradigm...
July 2016: Nursing Science Quarterly
Richard Andersson, Linnea Larsson, Kenneth Holmqvist, Martin Stridh, Marcus Nyström
Almost all eye-movement researchers use algorithms to parse raw data and detect distinct types of eye movement events, such as fixations, saccades, and pursuit, and then base their results on these. Surprisingly, these algorithms are rarely evaluated. We evaluated the classifications of ten eye-movement event detection algorithms, on data from an SMI HiSpeed 1250 system, and compared them to manual ratings of two human experts. The evaluation focused on fixations, saccades, and post-saccadic oscillations. The evaluation used both event duration parameters, and sample-by-sample comparisons to rank the algorithms...
May 18, 2016: Behavior Research Methods
Wei Zheng, Hongfei Lin, Zhehuan Zhao, Bo Xu, Yijia Zhang, Zhihao Yang, Jian Wang
The clinical recognition of drug-drug interactions (DDIs) is a crucial issue for both patient safety and health care cost control. Thus there is an urgent need that DDIs be extracted automatically from biomedical literature by text-mining techniques. Although the top-ranking DDIs systems explore various features of texts, these features can't yet adequately express long and complicated sentences. In this paper, we present an effective graph kernel which makes full use of different types of contexts to identify DDIs from biomedical literature...
June 2016: Journal of Biomedical Informatics
Andre F Marquand, Iead Rezek, Jan Buitelaar, Christian F Beckmann
BACKGROUND: Despite many successes, the case-control approach is problematic in biomedical science. It introduces an artificial symmetry whereby all clinical groups (e.g., patients and control subjects) are assumed to be well defined, when biologically they are often highly heterogeneous. By definition, it also precludes inference over the validity of the diagnostic labels. In response, the National Institute of Mental Health Research Domain Criteria proposes to map relationships between symptom dimensions and broad behavioral and biological domains, cutting across diagnostic categories...
October 1, 2016: Biological Psychiatry
Jari Björne, Tapio Salakoski
BACKGROUND: The Turku Event Extraction System (TEES) is a text mining program developed for the extraction of events, complex biomedical relationships, from scientific literature. Based on a graph-generation approach, the system detects events with the use of a rich feature set built via dependency parsing. The TEES system has achieved record performance in several of the shared tasks of its domain, and continues to be used in a variety of biomedical text mining tasks. RESULTS: The TEES system was quickly adapted to the BioNLP'13 Shared Task in order to provide a public baseline for derived systems...
2015: BMC Bioinformatics
R Islam, C Weir, G Del Fiol
BACKGROUND: Complexity in medicine needs to be reduced to simple components in a way that is comprehensible to researchers and clinicians. Few studies in the current literature propose a measurement model that addresses both task and patient complexity in medicine. OBJECTIVE: The objective of this paper is to develop an integrated approach to understand and measure clinical complexity by incorporating both task and patient complexity components focusing on the infectious disease domain...
2016: Methods of Information in Medicine
Lishuang Li, Shanshan Liu, Meiyue Qin, Yiwen Wang, Degen Huang
Extracting biomedical event from literatures has attracted much attention recently. By now, most of the state-of-the-art systems have been based on pipelines which suffer from cascading errors, and the words encoded by one-hot are unable to represent the semantic information. Joint inference with dual decomposition and novel word embeddings are adopted to address the two problems, respectively, in this work. Word embeddings are learnt from large scale unlabeled texts and integrated as an unsupervised feature into other rich features based on dependency parse graphs to detect triggers and arguments...
July 2016: IEEE/ACM Transactions on Computational Biology and Bioinformatics
İlknur Karadeniz, Arzucan Özgür
BACKGROUND: Information regarding bacteria biotopes is important for several research areas including health sciences, microbiology, and food processing and preservation. One of the challenges for scientists in these domains is the huge amount of information buried in the text of electronic resources. Developing methods to automatically extract bacteria habitat relations from the text of these electronic resources is crucial for facilitating research in these areas. METHODS: We introduce a linguistically motivated rule-based approach for recognizing and normalizing names of bacteria habitats in biomedical text by using an ontology...
2015: BMC Bioinformatics
Christina James-Zorn, Virgillio G Ponferrada, Kevin A Burns, Joshua D Fortriede, Vaneet S Lotay, Yu Liu, J Brad Karpinka, Kamran Karimi, Aaron M Zorn, Peter D Vize
Xenbase, the Xenopus model organism database (, is a cloud-based, web-accessible resource that integrates the diverse genomic and biological data from Xenopus research. Xenopus frogs are one of the major vertebrate animal models used for biomedical research, and Xenbase is the central repository for the enormous amount of data generated using this model tetrapod. The goal of Xenbase is to accelerate discovery by enabling investigators to make novel connections between molecular pathways in Xenopus and human disease...
August 2015: Genesis: the Journal of Genetics and Development
Vít Nováček, Gully A P C Burns
Background. Unlike full reading, 'skim-reading' involves the process of looking quickly over information in an attempt to cover more material whilst still being able to retain a superficial view of the underlying content. Within this work, we specifically emulate this natural human activity by providing a dynamic graph-based view of entities automatically extracted from text. For the extraction, we use shallow parsing, co-occurrence analysis and semantic similarity computation techniques. Our main motivation is to assist biomedical researchers and clinicians in coping with increasingly large amounts of potentially relevant articles that are being published ongoingly in life sciences...
2014: PeerJ
Changqin Quan, Meng Wang, Fuji Ren
The wealth of interaction information provided in biomedical articles motivated the implementation of text mining approaches to automatically extract biomedical relations. This paper presents an unsupervised method based on pattern clustering and sentence parsing to deal with biomedical relation extraction. Pattern clustering algorithm is based on Polynomial Kernel method, which identifies interaction words from unlabeled data; these interaction words are then used in relation extraction between entity pairs...
2014: PloS One
Amr Ahmed, Andrew Arnold, Luis Pedro Coelho, Joshua Kangas, Abdul-Saboor Sheikh, Eric Xing, William Cohen, Robert F Murphy
The SLIF project combines text-mining and image processing to extract structured information from biomedical literature. SLIF extracts images and their captions from published papers. The captions are automatically parsed for relevant biological entities (protein and cell type names), while the images are classified according to their type (e.g., micrograph or gel). Fluorescence microscopy images are further processed and classified according to the depicted subcellular localization. The results of this process can be queried online using either a user-friendly web-interface or an XML-based web-service...
July 1, 2010: Web Semantics: Science, Services and Agents on the World Wide Web
Rong Xu, QuanQiu Wang
Systems approaches to studying drug-side-effect (drug-SE) associations are emerging as an active research area for drug target discovery, drug repositioning, and drug toxicity prediction. However, currently available drug-SE association databases are far from being complete. Herein, in an effort to increase the data completeness of current drug-SE relationship resources, we present an automatic learning approach to accurately extract drug-SE pairs from the vast amount of published biomedical literature, a rich knowledge source of side effect information for commercial, experimental, and even failed drugs...
October 2014: Journal of Biomedical Informatics
Richard Tzong-Han Tsai, Po-Ting Lai
BACKGROUND: Biomedical semantic role labeling (BioSRL) is a natural language processing technique that identifies the semantic roles of the words or phrases in sentences describing biological processes and expresses them as predicate-argument structures (PAS's). Currently, a major problem of BioSRL is that most systems label every node in a full parse tree independently; however, some nodes always exhibit dependency. In general SRL, collective approaches based on the Markov logic network (MLN) model have been successful in dealing with this problem...
2014: BMC Bioinformatics
Xiaohui Yuan, Dongyu Ang
With the availability of full-text documents in many online databases, the paradigm of biomedical literature mining and document understanding has shifted to analysis of both text and figures to derive implicit messages that are unforeseen with text mining only. To enable automatic, massive processing, a key step is to extract and parse figures embedded in papers. In this paper, we present a novel model-driven, hierarchical method to classify and extract panels from figures in scientific papers. Our method consists of two integrated components: figure (or panel) classification and panel segmentation...
2014: International Journal of Data Mining and Bioinformatics
Tobias Kuhn, Mate Levente Nagy, Thaibinh Luong, Michael Krauthammer
Authors of biomedical publications use gel images to report experimental results such as protein-protein interactions or protein expressions under different conditions. Gel images offer a concise way to communicate such findings, not all of which need to be explicitly discussed in the article text. This fact together with the abundance of gel images and their shared common patterns makes them prime candidates for automated image mining and parsing. We introduce an approach for the detection of gel images, and present a workflow to analyze them...
2014: Journal of Biomedical Semantics
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