The Experts below are selected from a list of 17298 Experts worldwide ranked by ideXlab platform
Majella E Lane - One of the best experts on this subject based on the ideXlab platform.
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rotigotine the first new Chemical Entity for transdermal drug delivery
European Journal of Pharmaceutics and Biopharmaceutics, 2014Co-Authors: Donald Mcafee, Jonathan Hadgraft, Majella E LaneAbstract:Abstract Rotigotine is the first, and to date, the only new Chemical Entity to be formulated for transdermal delivery. Although first approved for the management of Parkinson’s disease in Europe in 2007 and Restless Leg Syndrome in 2008, the story of rotigotine began more than twenty years earlier. In this review we outline the historical development of this molecule and its route to licensed medicine status. It has very favourable physicoChemical properties for transdermal delivery but it took a significant period to develop from concept to market. The stability problems which led to the temporary withdrawal of the patch are examined and the major clinical studies demonstrating efficacy and safety are outlined. Alternative new therapeutic modalities are also considered.
Buzhou Tang - One of the best experts on this subject based on the ideXlab platform.
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a comparison of conditional random fields and structured support vector machines for Chemical Entity recognition in biomedical literature
Journal of Cheminformatics, 2015Co-Authors: Buzhou Tang, Yudong Feng, Xiaolong Wang, Yonghui Wu, Yaoyun Zhang, Min Jiang, Jingqi Wang, Hua XuAbstract:Background Chemical compounds and drugs (together called Chemical entities) embedded in scientific articles are crucial for many information extraction tasks in the biomedical domain. However, only a very limited number of Chemical Entity recognition systems are publically available, probably due to the lack of large manually annotated corpora. To accelerate the development of Chemical Entity recognition systems, the Spanish National Cancer Research Center (CNIO) and The University of Navarra organized a challenge on Chemical and Drug Named Entity Recognition (CHEMDNER). The CHEMDNER challenge contains two individual subtasks: 1) Chemical Entity Mention recognition (CEM); and 2) Chemical Document Indexing (CDI). Our study proposes machine learning-based systems for the CEM task.
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A comparison of conditional random fields and structured support vector machines for Chemical Entity recognition in biomedical literature.
Journal of cheminformatics, 2015Co-Authors: Buzhou Tang, Yudong Feng, Xiaolong Wang, Yonghui Wu, Yaoyun Zhang, Min Jiang, Jingqi Wang, Hua XuAbstract:Chemical compounds and drugs (together called Chemical entities) embedded in scientific articles are crucial for many information extraction tasks in the biomedical domain. However, only a very limited number of Chemical Entity recognition systems are publically available, probably due to the lack of large manually annotated corpora. To accelerate the development of Chemical Entity recognition systems, the Spanish National Cancer Research Center (CNIO) and The University of Navarra organized a challenge on Chemical and Drug Named Entity Recognition (CHEMDNER). The CHEMDNER challenge contains two individual subtasks: 1) Chemical Entity Mention recognition (CEM); and 2) Chemical Document Indexing (CDI). Our study proposes machine learning-based systems for the CEM task. The 2013 CHEMDNER challenge organizers provided a manually annotated 10,000 UTF8-encoded PubMed abstracts according to a predefined annotation guideline: a training set of 3,500 abstracts, a development set of 3,500 abstracts and a test set of 3,000 abstracts. We developed machine learning-based systems, based on conditional random fields (CRF) and structured support vector machines (SSVM) respectively, for the CEM task for this data set. The effects of three types of word representation (WR) features, generated by Brown clustering, random indexing and skip-gram, on both two machine learning-based systems were also investigated. The performance of our system was evaluated on the test set using scripts provided by the CHEMDNER challenge organizers. Primary evaluation measures were micro Precision, Recall, and F-measure. Our best system was among the top ranked systems with an official micro F-measure of 85.05%. Fixing a bug caused by inconsistent features marginally improved the performance (micro F-measure of 85.20%) of the system. The SSVM-based CEM systems outperformed the CRF-based CEM systems when using the same features. Each type of the WR feature was beneficial to the CEM task. Both the CRF-based and SSVM-based systems using the all three types of WR features showed better performance than the systems using only one type of the WR feature.
Francisco M Couto - One of the best experts on this subject based on the ideXlab platform.
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ICBO - Identifying Chemical Entities based on ChEBI.
2020Co-Authors: Tiago Grego, Francisco R. Pinto, Francisco M CoutoAbstract:This software demonstration paper presents Identifying Chemical Entities (ICE), a platform composed by algorithms for Chemical Entity recognition, Entity resolution to a reference database, namely ChEBI, and validation using Chemical semantic similarity. It aims to provide the users with an improved display of Entity recognition results, exposing outliers which are possible recognition errors and displaying evidence that corroborates consistent Chemical entities in the Entity recognition and resolution process.
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improving Chemical Entity recognition through h index based semantic similarity
Journal of Cheminformatics, 2015Co-Authors: Andre Lamurias, Joao D Ferreira, Francisco M CoutoAbstract:Our approach to the BioCreative IV challenge of recognition and classification of drug names (CHEMDNER task) aimed at achieving high levels of precision by applying semantic similarity validation techniques to Chemical Entities of Biological Interest (ChEBI) mappings. Our assumption is that the Chemical entities mentioned in the same fragment of text should share some semantic relation. This validation method was further improved by adapting the semantic similarity measure to take into account the h-index of each ancestor. We applied this method in two measures, simUI and simGIC, and validated the results obtained for the competition, comparing each adapted measure to its original version. For the competition, we trained a Random Forest classifier that uses various scores provided by our system, including semantic similarity, which improved the F-measure obtained with the Conditional Random Fields classifiers by 4.6%. Using a notion of concept relevance based on the h-index measure, we were able to enhance our validation process so that for a fixed recall, we increased precision by excluding from the results a higher amount of false positives. We plotted precision and recall values for a range of validation thresholds using different similarity measures, obtaining higher precision values for the same recall with the measures based on the h-index. The semantic similarity measure we introduced was more efficient at validating text mining results from machine learning classifiers than other measures. We improved the results we obtained for the CHEMDNER task by maintaining high precision values while improving the recall and F-measure.
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enhancement of Chemical Entity identification in text using semantic similarity validation
PLOS ONE, 2013Co-Authors: Tiago Grego, Francisco M CoutoAbstract:: With the amount of Chemical data being produced and reported in the literature growing at a fast pace, it is increasingly important to efficiently retrieve this information. To tackle this issue text mining tools have been applied, but despite their good performance they still provide many errors that we believe can be filtered by using semantic similarity. Thus, this paper proposes a novel method that receives the results of Chemical Entity identification systems, such as Whatizit, and exploits the semantic relationships in ChEBI to measure the similarity between the entities found in the text. The method assigns a single validation score to each Entity based on its similarities with the other entities also identified in the text. Then, by using a given threshold, the method selects a set of validated entities and a set of outlier entities. We evaluated our method using the results of two state-of-the-art Chemical Entity identification tools, three semantic similarity measures and two text window sizes. The method was able to increase precision without filtering a significant number of correctly identified entities. This means that the method can effectively discriminate the correctly identified Chemical entities, while discarding a significant number of identification errors. For example, selecting a validation set with 75% of all identified entities, we were able to increase the precision by 28% for one of the Chemical Entity identification tools (Whatizit), maintaining in that subset 97% the correctly identified entities. Our method can be directly used as an add-on by any state-of-the-art Entity identification tool that provides mappings to a database, in order to improve their results. The proposed method is included in a freely accessible web tool at www.lasige.di.fc.ul.pt/webtools/ice/.
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Chemical Entity recognition and resolution to chebi
International Scholarly Research Notices, 2012Co-Authors: Tiago Grego, Catia Pesquita, Hugo P Bastos, Francisco M CoutoAbstract:Chemical entities are ubiquitous through the biomedical literature and the development of text-mining systems that can efficiently identify those entities are required. Due to the lack of available corpora and data resources, the community has focused its efforts in the development of gene and protein named Entity recognition systems, but with the release of ChEBI and the availability of an annotated corpus, this task can be addressed. We developed a machine-learning-based method for Chemical Entity recognition and a lexical-similarity-based method for Chemical Entity resolution and compared them with Whatizit, a popular-dictionary-based method. Our methods outperformed the dictionary-based method in all tasks, yielding an improvement in F-measure of 20% for the Entity recognition task, 2–5% for the Entity-resolution task, and 15% for combined Entity recognition and resolution tasks.
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IWANN (2) - Identification of Chemical Entities in Patent Documents
Distributed Computing Artificial Intelligence Bioinformatics Soft Computing and Ambient Assisted Living, 2009Co-Authors: Tiago Grego, Francisco M Couto, Dietrich Rebholz-schuhmannAbstract:Biomedical literature is an important source of information for Chemical compounds. However, different representations and nomenclatures for Chemical entities exist, which makes the reference of Chemical entities ambiguous. Many systems already exist for gene and protein Entity recognition, however very few exist for Chemical entities. The main reason for this is the lack of corpus to train named Entity recognition systems and perform evaluation. In this paper we present a Chemical Entity recognizer that uses a machine learning approach based on conditional random fields (CRF) and compare the performance with dictionary-based approaches using several terminological resources. For the training and evaluation, a gold standard of manually curated patent documents was used. While the dictionary-based systems perform well in partial identification of Chemical entities, the machine learning approach performs better (10% increase in F-score in comparison to the best dictionary-based system) when identifying complete entities.
Hua Xu - One of the best experts on this subject based on the ideXlab platform.
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a comparison of conditional random fields and structured support vector machines for Chemical Entity recognition in biomedical literature
Journal of Cheminformatics, 2015Co-Authors: Buzhou Tang, Yudong Feng, Xiaolong Wang, Yonghui Wu, Yaoyun Zhang, Min Jiang, Jingqi Wang, Hua XuAbstract:Background Chemical compounds and drugs (together called Chemical entities) embedded in scientific articles are crucial for many information extraction tasks in the biomedical domain. However, only a very limited number of Chemical Entity recognition systems are publically available, probably due to the lack of large manually annotated corpora. To accelerate the development of Chemical Entity recognition systems, the Spanish National Cancer Research Center (CNIO) and The University of Navarra organized a challenge on Chemical and Drug Named Entity Recognition (CHEMDNER). The CHEMDNER challenge contains two individual subtasks: 1) Chemical Entity Mention recognition (CEM); and 2) Chemical Document Indexing (CDI). Our study proposes machine learning-based systems for the CEM task.
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A comparison of conditional random fields and structured support vector machines for Chemical Entity recognition in biomedical literature.
Journal of cheminformatics, 2015Co-Authors: Buzhou Tang, Yudong Feng, Xiaolong Wang, Yonghui Wu, Yaoyun Zhang, Min Jiang, Jingqi Wang, Hua XuAbstract:Chemical compounds and drugs (together called Chemical entities) embedded in scientific articles are crucial for many information extraction tasks in the biomedical domain. However, only a very limited number of Chemical Entity recognition systems are publically available, probably due to the lack of large manually annotated corpora. To accelerate the development of Chemical Entity recognition systems, the Spanish National Cancer Research Center (CNIO) and The University of Navarra organized a challenge on Chemical and Drug Named Entity Recognition (CHEMDNER). The CHEMDNER challenge contains two individual subtasks: 1) Chemical Entity Mention recognition (CEM); and 2) Chemical Document Indexing (CDI). Our study proposes machine learning-based systems for the CEM task. The 2013 CHEMDNER challenge organizers provided a manually annotated 10,000 UTF8-encoded PubMed abstracts according to a predefined annotation guideline: a training set of 3,500 abstracts, a development set of 3,500 abstracts and a test set of 3,000 abstracts. We developed machine learning-based systems, based on conditional random fields (CRF) and structured support vector machines (SSVM) respectively, for the CEM task for this data set. The effects of three types of word representation (WR) features, generated by Brown clustering, random indexing and skip-gram, on both two machine learning-based systems were also investigated. The performance of our system was evaluated on the test set using scripts provided by the CHEMDNER challenge organizers. Primary evaluation measures were micro Precision, Recall, and F-measure. Our best system was among the top ranked systems with an official micro F-measure of 85.05%. Fixing a bug caused by inconsistent features marginally improved the performance (micro F-measure of 85.20%) of the system. The SSVM-based CEM systems outperformed the CRF-based CEM systems when using the same features. Each type of the WR feature was beneficial to the CEM task. Both the CRF-based and SSVM-based systems using the all three types of WR features showed better performance than the systems using only one type of the WR feature.
Donald Mcafee - One of the best experts on this subject based on the ideXlab platform.
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rotigotine the first new Chemical Entity for transdermal drug delivery
European Journal of Pharmaceutics and Biopharmaceutics, 2014Co-Authors: Donald Mcafee, Jonathan Hadgraft, Majella E LaneAbstract:Abstract Rotigotine is the first, and to date, the only new Chemical Entity to be formulated for transdermal delivery. Although first approved for the management of Parkinson’s disease in Europe in 2007 and Restless Leg Syndrome in 2008, the story of rotigotine began more than twenty years earlier. In this review we outline the historical development of this molecule and its route to licensed medicine status. It has very favourable physicoChemical properties for transdermal delivery but it took a significant period to develop from concept to market. The stability problems which led to the temporary withdrawal of the patch are examined and the major clinical studies demonstrating efficacy and safety are outlined. Alternative new therapeutic modalities are also considered.