The Experts below are selected from a list of 258 Experts worldwide ranked by ideXlab platform
Thierry Hamon - One of the best experts on this subject based on the ideXlab platform.
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Extracting Food-Drug Interactions from Scientific Literature: Tackling Unspecified Relation
2019Co-Authors: Tsanta Randriatsitohaina, Thierry HamonAbstract:This paper tackles the problem of mining scientific literature to extract Food-Drug Interaction (FDI). This problem is viewed as a relation extraction task which can be solved with classification method. Since FDI need to be described in a very fine way with many relation types, we face the data sparseness and the lack of examples per type of relation. To address this issue, we propose an effective approach for grouping relations sharing similar representation into clusters and reducing the lack of examples. Since unspecified relations represent more than half the data, we propose to contrast supervised and unsupervised methods to identify the specific relation involved in these examples. The performance of our classification-based labeling approach is twice better than on initial dataset and the data imbalance is significantly reduced. Besides, how learning models combine relations can be interpreted to more effectively group relations.
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AIME - Extracting Food-Drug Interactions from Scientific Literature: Tackling Unspecified Relation
Artificial Intelligence in Medicine, 2019Co-Authors: Tsanta Randriatsitohaina, Thierry HamonAbstract:This paper tackles the problem of mining scientific literature to extract Food-Drug Interaction (FDI). This problem is viewed as a relation extraction task which can be solved with classification method. Since FDI need to be described in a very fine way with many relation types, we face the data sparseness and the lack of examples per type of relation. To address this issue, we propose an effective approach for grouping relations sharing similar representation into clusters and reducing the lack of examples. Since unspecified relations represent more than half the data, we propose to contrast supervised and unsupervised methods to identify the specific relation involved in these examples. The performance of our classification-based labeling approach is twice better than on initial dataset and the data imbalance is significantly reduced. Besides, how learning models combine relations can be interpreted to more effectively group relations.
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Extracting Food-Drug Interactions from scientific literature: relation clustering to address lack of data
2019Co-Authors: Tsanta Randriatsitohaina, Thierry HamonAbstract:Food-Drug Interaction (FDI) occurs when Food and Drug are taken simultaneously and cause unexpected effect. This paper tackles the problem of mining scientific literature in order to extract these Interactions. We consider this problem as a relation extraction task which can be solved with classification method. Since Food-Drug Interactions need a fine-grained description with many relation types, we face the data sparseness and the lack of examples per type of relation. To address this issue, we propose an effective approach for grouping relations sharing similar representation into clusters and reducing the lack of examples. Cluster labels are then used as labels of the dataset given to classifiers for the FDI type identification. Our approach, relying on the extraction of relevant features before, between, and after the entities associated by the relation, improves significantly the performance of the FDI classification. Finally, we contrast an intuitive grouping method based on the definition of the relation types and a unsupervised clustering based on the instances of each relation type.
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Automatic query selection for acquisition and discovery of Food-Drug Interactions
2018Co-Authors: Georgeta Bordea, Frantz Thiessard, Thierry Hamon, Fleur MouginAbstract:Food-Drug Interactions can profoundly impact desired and adverse effects of Drugs, with unexpected and often harmful consequences on the health and well-being of patients. A growing body of scientific publications report clinically relevant Food-Drug Interactions, but conventional search strategies based on handcrafted queries and indexing terms suffer from low recall. In this paper, we introduce a novel task called Food-Drug Interaction discovery that aims to automatically identify scientific publications that describe Food-Drug Interactions from a database of biomedical literature. We make use of an expert curated corpus of Food-Drug Interactions to analyse different methods for query selection and we propose a high-recall approach based on feature selection.
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CLEF - Automatic Query Selection for Acquisition and Discovery of Food-Drug Interactions
Lecture Notes in Computer Science, 2018Co-Authors: Georgeta Bordea, Frantz Thiessard, Thierry Hamon, Fleur MouginAbstract:Food-Drug Interactions can profoundly impact desired and adverse effects of Drugs, with unexpected and often harmful consequences on the health and well-being of patients. A growing body of scientific publications report clinically relevant Food-Drug Interactions, but conventional search strategies based on handcrafted queries and indexing terms suffer from low recall. In this paper, we introduce a novel task called Food-Drug Interaction discovery that aims to automatically identify scientific publications that describe Food-Drug Interactions from a database of biomedical literature. We make use of an expert curated corpus of Food-Drug Interactions to analyse different methods for query selection and we propose a high-recall approach based on feature selection.
Tsanta Randriatsitohaina - One of the best experts on this subject based on the ideXlab platform.
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Annotations d'entités et de relations sur des résumés d'articles scientifiques pour la détection d'Interactions entre aliments et médicaments
2019Co-Authors: Tsanta Randriatsitohaina, Georgeta Bordea, Fleur Mougin, Cyril Grouin, Pierrick Bedouch, Amélie Daveluy, Vincent Depras, Natalia Grabar, Ghada Miremont-salamé, Cécile PageotAbstract:Dans cet article, nous présentons le schéma d'annotation utilisé pour étudier les Interactions aliments-médicaments (Food-Drug Interaction-FDI). Le corpus se compose de 639 résumés d'articles scientifiques issus de Medline. Nous avons défini un schéma d'annotation constitué de 21 catégories d'entités et de 21 types de relations appliquées sur 9 catégories d'entités. Ces schémas ont été appliqués sur des documents rédigés en anglais ou en français, ouvrant la voie à un corpus multilingue annoté au moyen des mêmes catégories. Nous présentons également quelques expériences d'identification automatique des types de relations. L'adaptation de domaine à partir des Interactions médicament-médicament (DDI) permet d'avoir un schéma d'annotation des relations selon 4 types. L'extraction automatique de ces relations conduit à une F1-mesure de 0.79 obtenue avec un modèle SVM précédé d'un processus de sélection de descripteurs SFM.
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Extracting Food-Drug Interactions from Scientific Literature: Tackling Unspecified Relation
2019Co-Authors: Tsanta Randriatsitohaina, Thierry HamonAbstract:This paper tackles the problem of mining scientific literature to extract Food-Drug Interaction (FDI). This problem is viewed as a relation extraction task which can be solved with classification method. Since FDI need to be described in a very fine way with many relation types, we face the data sparseness and the lack of examples per type of relation. To address this issue, we propose an effective approach for grouping relations sharing similar representation into clusters and reducing the lack of examples. Since unspecified relations represent more than half the data, we propose to contrast supervised and unsupervised methods to identify the specific relation involved in these examples. The performance of our classification-based labeling approach is twice better than on initial dataset and the data imbalance is significantly reduced. Besides, how learning models combine relations can be interpreted to more effectively group relations.
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AIME - Extracting Food-Drug Interactions from Scientific Literature: Tackling Unspecified Relation
Artificial Intelligence in Medicine, 2019Co-Authors: Tsanta Randriatsitohaina, Thierry HamonAbstract:This paper tackles the problem of mining scientific literature to extract Food-Drug Interaction (FDI). This problem is viewed as a relation extraction task which can be solved with classification method. Since FDI need to be described in a very fine way with many relation types, we face the data sparseness and the lack of examples per type of relation. To address this issue, we propose an effective approach for grouping relations sharing similar representation into clusters and reducing the lack of examples. Since unspecified relations represent more than half the data, we propose to contrast supervised and unsupervised methods to identify the specific relation involved in these examples. The performance of our classification-based labeling approach is twice better than on initial dataset and the data imbalance is significantly reduced. Besides, how learning models combine relations can be interpreted to more effectively group relations.
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Extracting Food-Drug Interactions from scientific literature: relation clustering to address lack of data
2019Co-Authors: Tsanta Randriatsitohaina, Thierry HamonAbstract:Food-Drug Interaction (FDI) occurs when Food and Drug are taken simultaneously and cause unexpected effect. This paper tackles the problem of mining scientific literature in order to extract these Interactions. We consider this problem as a relation extraction task which can be solved with classification method. Since Food-Drug Interactions need a fine-grained description with many relation types, we face the data sparseness and the lack of examples per type of relation. To address this issue, we propose an effective approach for grouping relations sharing similar representation into clusters and reducing the lack of examples. Cluster labels are then used as labels of the dataset given to classifiers for the FDI type identification. Our approach, relying on the extraction of relevant features before, between, and after the entities associated by the relation, improves significantly the performance of the FDI classification. Finally, we contrast an intuitive grouping method based on the definition of the relation types and a unsupervised clustering based on the instances of each relation type.
Peter Langguth - One of the best experts on this subject based on the ideXlab platform.
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ion pairing with bile salts modulates intestinal permeability and contributes to Food Drug Interaction of bcs class iii compound trospium chloride
Molecular Pharmaceutics, 2013Co-Authors: Christian Heinen, Stefan Reuss, Gordon L Amidon, Peter LangguthAbstract:In the current study the involvement of ion pair formation between bile salts and trospium chloride (TC), a positively charged Biopharmaceutical Classification System (BCS) class III substance, showing a decrease in bioavailability upon coadministration with Food (negative Food effect) was investigated. Isothermal titration calorimetry provided evidence of a reaction between TC and bile acids. An effect of ion pair formation on the apparent partition coefficient (APC) was examined using 3H-trospium. The addition of bovine bile and bile extract porcine led to a significant increase of the APC. In vitro permeability studies of trospium were performed across Caco-2-monolayers and excised segments of rat jejunum in a modified Ussing chamber. The addition of bile acids led to an increase of trospium permeation across Caco-2-monolayers and rat excised segments by approximately a factor of 1.5. The addition of glycochenodeoxycholate (GCDC) was less effective than taurodeoxycholate (TDOC). In the presence of an o...
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Ion Pairing with Bile Salts Modulates Intestinal Permeability and Contributes to Food–Drug Interaction of BCS Class III Compound Trospium Chloride
Molecular Pharmaceutics, 2013Co-Authors: Christian Heinen, Stefan Reuss, Gordon L Amidon, Peter LangguthAbstract:In the current study the involvement of ion pair formation between bile salts and trospium chloride (TC), a positively charged Biopharmaceutical Classification System (BCS) class III substance, showing a decrease in bioavailability upon coadministration with Food (negative Food effect) was investigated. Isothermal titration calorimetry provided evidence of a reaction between TC and bile acids. An effect of ion pair formation on the apparent partition coefficient (APC) was examined using 3H-trospium. The addition of bovine bile and bile extract porcine led to a significant increase of the APC. In vitro permeability studies of trospium were performed across Caco-2-monolayers and excised segments of rat jejunum in a modified Ussing chamber. The addition of bile acids led to an increase of trospium permeation across Caco-2-monolayers and rat excised segments by approximately a factor of 1.5. The addition of glycochenodeoxycholate (GCDC) was less effective than taurodeoxycholate (TDOC). In the presence of an o...
Sahely Bhadra - One of the best experts on this subject based on the ideXlab platform.
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Cytochrome P450 inhibitory potential of selected Indian spices — possible Food Drug Interaction
Food Research International, 2012Co-Authors: Subrata Pandit, Pulok K. Mukherjee, Kakali Mukherjee, Rahul Gajbhiye, M. Venkatesh, S. Ponnusankar, Sahely BhadraAbstract:Abstract Spices constitute an important group of Food which is virtually indispensable in the culinary art. In a view, these spices feared to pose a probability to affect the disposition of conventional pharmaceuticals through inhibition of human cytochrome P450 (CYPs) enzymes. In the present study an approach has been made to evaluate the possible CYP inhibition potential with some Indian spices ( Capsicum annuum , Murraya koenigii , Zingiber officinale ) and their major bioactive compounds, in combination with pooled microsome; as well as commercially available recombinant human CYP3A4, CYP2D6, CYP2C9 and CYP1A2. Quantification of the bioactive compound was determined through RP-HPLC, in order to standardize the plant material. CYP–carbon monoxide (CYP–CO) complex assay result indicated that all the plants and their bioactive compounds have an Interaction potential with CYPs. Fluoregenic assay results indicated that the spice extracts have higher inhibition potential comparing to their single bioactive molecule. The higher enzyme inhibition potential by the extracts may be related to the synergistic effects due to the presence of other constituents in the extract. Capsaicin and C. annuum showed the lowest IC 50 value and 6-gingerol and Z. officinale extract showed the highest IC 50 value among the entire sample tested. The entire sample showed significantly less ( P P 0.01) Interaction potential than known inhibitors. These findings indicate that selected spices are unlikely to cause clinically relevant Drug Interactions involving the inhibition of major CYP isozymes.
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cytochrome p450 inhibitory potential of selected indian spices possible Food Drug Interaction
Food Research International, 2012Co-Authors: Subrata Pandit, Pulok K. Mukherjee, Kakali Mukherjee, Rahul Gajbhiye, M. Venkatesh, S. Ponnusankar, Sahely BhadraAbstract:Abstract Spices constitute an important group of Food which is virtually indispensable in the culinary art. In a view, these spices feared to pose a probability to affect the disposition of conventional pharmaceuticals through inhibition of human cytochrome P450 (CYPs) enzymes. In the present study an approach has been made to evaluate the possible CYP inhibition potential with some Indian spices ( Capsicum annuum , Murraya koenigii , Zingiber officinale ) and their major bioactive compounds, in combination with pooled microsome; as well as commercially available recombinant human CYP3A4, CYP2D6, CYP2C9 and CYP1A2. Quantification of the bioactive compound was determined through RP-HPLC, in order to standardize the plant material. CYP–carbon monoxide (CYP–CO) complex assay result indicated that all the plants and their bioactive compounds have an Interaction potential with CYPs. Fluoregenic assay results indicated that the spice extracts have higher inhibition potential comparing to their single bioactive molecule. The higher enzyme inhibition potential by the extracts may be related to the synergistic effects due to the presence of other constituents in the extract. Capsaicin and C. annuum showed the lowest IC 50 value and 6-gingerol and Z. officinale extract showed the highest IC 50 value among the entire sample tested. The entire sample showed significantly less ( P P 0.01) Interaction potential than known inhibitors. These findings indicate that selected spices are unlikely to cause clinically relevant Drug Interactions involving the inhibition of major CYP isozymes.
Christian Heinen - One of the best experts on this subject based on the ideXlab platform.
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ion pairing with bile salts modulates intestinal permeability and contributes to Food Drug Interaction of bcs class iii compound trospium chloride
Molecular Pharmaceutics, 2013Co-Authors: Christian Heinen, Stefan Reuss, Gordon L Amidon, Peter LangguthAbstract:In the current study the involvement of ion pair formation between bile salts and trospium chloride (TC), a positively charged Biopharmaceutical Classification System (BCS) class III substance, showing a decrease in bioavailability upon coadministration with Food (negative Food effect) was investigated. Isothermal titration calorimetry provided evidence of a reaction between TC and bile acids. An effect of ion pair formation on the apparent partition coefficient (APC) was examined using 3H-trospium. The addition of bovine bile and bile extract porcine led to a significant increase of the APC. In vitro permeability studies of trospium were performed across Caco-2-monolayers and excised segments of rat jejunum in a modified Ussing chamber. The addition of bile acids led to an increase of trospium permeation across Caco-2-monolayers and rat excised segments by approximately a factor of 1.5. The addition of glycochenodeoxycholate (GCDC) was less effective than taurodeoxycholate (TDOC). In the presence of an o...
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Ion Pairing with Bile Salts Modulates Intestinal Permeability and Contributes to Food–Drug Interaction of BCS Class III Compound Trospium Chloride
Molecular Pharmaceutics, 2013Co-Authors: Christian Heinen, Stefan Reuss, Gordon L Amidon, Peter LangguthAbstract:In the current study the involvement of ion pair formation between bile salts and trospium chloride (TC), a positively charged Biopharmaceutical Classification System (BCS) class III substance, showing a decrease in bioavailability upon coadministration with Food (negative Food effect) was investigated. Isothermal titration calorimetry provided evidence of a reaction between TC and bile acids. An effect of ion pair formation on the apparent partition coefficient (APC) was examined using 3H-trospium. The addition of bovine bile and bile extract porcine led to a significant increase of the APC. In vitro permeability studies of trospium were performed across Caco-2-monolayers and excised segments of rat jejunum in a modified Ussing chamber. The addition of bile acids led to an increase of trospium permeation across Caco-2-monolayers and rat excised segments by approximately a factor of 1.5. The addition of glycochenodeoxycholate (GCDC) was less effective than taurodeoxycholate (TDOC). In the presence of an o...