Authors: Isabel Segura-Bedmar, Paloma Martínez, César de Pablo-Sánchez
DTMBIO 2010, 4th International Workshop and Data Text Mining in Biomedical Informatics, Toronto, Ontario, Canada (October 26, 2010)
Building a Graph of Names and Contextual Patterns for Named Entity Classifica...Grupo HULAT
Authors: César de Pablo Sánchez and Paloma Martínez
31st European Conference on Information Retrieval, Tolouse, France (April 6-9, 2009)
Building a Graph of Names and Contextual Patterns for Named Entity Classification
A successful car accident claim should result in compensation for pain and suffering, general damages, and loss of quality of life. The severity of your injury will greatly influence the size of your settlement.
An aspect of the invention is directed to a polymer comprising a sulfonated
perfluorocyclopentyl compound. Another aspect of the invention is directed to a sulfonated
copolymer comprising one or more sulfonated polymers. A further aspect of the invention is
directed to membranes prepared from the polymers of the claimed invention.
Building a Graph of Names and Contextual Patterns for Named Entity Classifica...Grupo HULAT
Authors: César de Pablo Sánchez and Paloma Martínez
31st European Conference on Information Retrieval, Tolouse, France (April 6-9, 2009)
Building a Graph of Names and Contextual Patterns for Named Entity Classification
A successful car accident claim should result in compensation for pain and suffering, general damages, and loss of quality of life. The severity of your injury will greatly influence the size of your settlement.
An aspect of the invention is directed to a polymer comprising a sulfonated
perfluorocyclopentyl compound. Another aspect of the invention is directed to a sulfonated
copolymer comprising one or more sulfonated polymers. A further aspect of the invention is
directed to membranes prepared from the polymers of the claimed invention.
Ultra low dielectric constant (k 1⁄4 1.53) materials with self-cleansing properties were synthesized via incorporation of fluorodecyl-polyhedral oligomeric silsesquioxane (FD-POSS) into recently synthesized perfluorocyclopentenyl (PFCP) aryl ether polymers. Incorporation of fluorine rich, high free volume, and low surface energy POSS into a semifluorinated PFCP polymer matrix at various weight percentages resulted in a dramatic drop in dielectric constant, as well as a significant increase in hydrophobicity and oleophobicity of the system. These ultra-low dielectric self-cleansing materials (qtilt 1⁄4 38) were fabricated into electrospun mats from a solvent blend of fluorinated FD-POSS with PFCP polymers.
Detecting Semantic Relations between Nominals using Support Vector Machines a...Grupo HULAT
Authors: Isabel Segura Bedmar, Doaa Samy, José L. Martínez Fernández, Paloma Martínez
SemEval 2007, 4th International Workshop on Semantic Evaluations, (2007)
Detecting Semantic Relations between Nominals using Support Vector Machines and Linguistic-Based Rules
DINTO An Ontology for Drug-Drug InteractionsGrupo HULAT
Authors: María Herrero Zazo, Janna Hastings, Isabel Segura-Bedmar, Samuel Croset, Paloma Martínez and Christoph Steinbeck
SWAT4LS, International Workshop Semantic Web Applications and tools for life sciences, Edinburgh, UK (December 10, 2013)
DINTO, An Ontology for Drug-Drug Interactions
Bis-perfluorocycloalkenyl (PFCA) aryl ether monomers towards a versatile clas...Babloo Sharma, Ph.D.
A unique class of perfluorocycloalkenyl (PFCA) aryl ether monomers was synthesized from commercially available perfluorocycloalkenes (PFCAs) and bisphenols in good yields. This facile one pot reaction of perfluorocycloalkenes, namely, octafluorocyclopentene (OFCP), and decafluorocyclohexene (DFCH), with bisphenols occurs at room temperature via an addition–elimination reaction in the presence of a base. The synthesis of PFCA monomers and their condensation with bisphenols lead to perfluorocycloalkenyl (PFCA) aryl ether homopolymers and copolymers with random and/or alternating polymer architectures.
Perfluorocyclopentenyl (PFCP) Aryl Ether Polymers via Polycondensation of Oct...Babloo Sharma, Ph.D.
A unique class of aromatic ether polymers
containing perfluorocyclopentenyl (PFCP) enchainment was
prepared from the simple step growth polycondensation of
commercial bisphenols and octafluorocyclopentene (OFCP)
in the presence of triethylamine. Model studies indicate that
the second addition/elimination on OFCP is fast and poly-
condensation results in linear homopolymers and copolymers
without side products. The synthesis of bis(heptafluoro-
cyclopentenyl) aryl ether monomers and their condensation
with bisphenols further led to PFCP copolymers with alternating structures. This new class of semifluorinated polymers exhibit surprisingly high crystallinity in some cases and excellent thermal stability.
Infrastructure is Dead - Pepijn PalmansHousingcenter
Infrastructure is dead. Or how we need to focus on data in stead of the underlying platform. Pepijn Palmans - Managing Director of Housingcenter - at HP Discover in Barcelona, about the need for smart networking as enabler of services inside the data center.
Towards a foundational representation of potential drug-drug interaction know...Mathias Brochhausen
Inadequate representation of evidence and knowledge about potential drug-drug interactions is a major factor underlying disagreements among sources of drug information that are used by clinicians. In this paper we describe the initial steps toward developing a foundational domain representation that allows tracing the evidence underlying potential drug-drug interaction knowledge. The new representation includes biological and biomedical entities represented in existing ontologies and terminologies to foster integration of data from relevant fields such as physiology, anatomy, and laboratory sciences.
CADD is a mixture of bioinformatics and computer science where the information from bioinformatics is combined into a software which makes it easier to process.
Ultra low dielectric constant (k 1⁄4 1.53) materials with self-cleansing properties were synthesized via incorporation of fluorodecyl-polyhedral oligomeric silsesquioxane (FD-POSS) into recently synthesized perfluorocyclopentenyl (PFCP) aryl ether polymers. Incorporation of fluorine rich, high free volume, and low surface energy POSS into a semifluorinated PFCP polymer matrix at various weight percentages resulted in a dramatic drop in dielectric constant, as well as a significant increase in hydrophobicity and oleophobicity of the system. These ultra-low dielectric self-cleansing materials (qtilt 1⁄4 38) were fabricated into electrospun mats from a solvent blend of fluorinated FD-POSS with PFCP polymers.
Detecting Semantic Relations between Nominals using Support Vector Machines a...Grupo HULAT
Authors: Isabel Segura Bedmar, Doaa Samy, José L. Martínez Fernández, Paloma Martínez
SemEval 2007, 4th International Workshop on Semantic Evaluations, (2007)
Detecting Semantic Relations between Nominals using Support Vector Machines and Linguistic-Based Rules
DINTO An Ontology for Drug-Drug InteractionsGrupo HULAT
Authors: María Herrero Zazo, Janna Hastings, Isabel Segura-Bedmar, Samuel Croset, Paloma Martínez and Christoph Steinbeck
SWAT4LS, International Workshop Semantic Web Applications and tools for life sciences, Edinburgh, UK (December 10, 2013)
DINTO, An Ontology for Drug-Drug Interactions
Bis-perfluorocycloalkenyl (PFCA) aryl ether monomers towards a versatile clas...Babloo Sharma, Ph.D.
A unique class of perfluorocycloalkenyl (PFCA) aryl ether monomers was synthesized from commercially available perfluorocycloalkenes (PFCAs) and bisphenols in good yields. This facile one pot reaction of perfluorocycloalkenes, namely, octafluorocyclopentene (OFCP), and decafluorocyclohexene (DFCH), with bisphenols occurs at room temperature via an addition–elimination reaction in the presence of a base. The synthesis of PFCA monomers and their condensation with bisphenols lead to perfluorocycloalkenyl (PFCA) aryl ether homopolymers and copolymers with random and/or alternating polymer architectures.
Perfluorocyclopentenyl (PFCP) Aryl Ether Polymers via Polycondensation of Oct...Babloo Sharma, Ph.D.
A unique class of aromatic ether polymers
containing perfluorocyclopentenyl (PFCP) enchainment was
prepared from the simple step growth polycondensation of
commercial bisphenols and octafluorocyclopentene (OFCP)
in the presence of triethylamine. Model studies indicate that
the second addition/elimination on OFCP is fast and poly-
condensation results in linear homopolymers and copolymers
without side products. The synthesis of bis(heptafluoro-
cyclopentenyl) aryl ether monomers and their condensation
with bisphenols further led to PFCP copolymers with alternating structures. This new class of semifluorinated polymers exhibit surprisingly high crystallinity in some cases and excellent thermal stability.
Infrastructure is Dead - Pepijn PalmansHousingcenter
Infrastructure is dead. Or how we need to focus on data in stead of the underlying platform. Pepijn Palmans - Managing Director of Housingcenter - at HP Discover in Barcelona, about the need for smart networking as enabler of services inside the data center.
Towards a foundational representation of potential drug-drug interaction know...Mathias Brochhausen
Inadequate representation of evidence and knowledge about potential drug-drug interactions is a major factor underlying disagreements among sources of drug information that are used by clinicians. In this paper we describe the initial steps toward developing a foundational domain representation that allows tracing the evidence underlying potential drug-drug interaction knowledge. The new representation includes biological and biomedical entities represented in existing ontologies and terminologies to foster integration of data from relevant fields such as physiology, anatomy, and laboratory sciences.
CADD is a mixture of bioinformatics and computer science where the information from bioinformatics is combined into a software which makes it easier to process.
Poster presented at the Elixir All-Hands Meeting in Lisbon, June 2019. Gives a broad summary of Guide to Pharmacology activities in the last year. Emphasising new tools and our extension into malaria pharmacology.
Sorting bioactive wheat from database chaff: Challenges of discerning correct...Guide to PHARMACOLOGY
Since 2009 the Guide to PHARMACOLOGY database (GtoPdb) team have curated 7586 ligands from papers, including approved drugs, clinical candidates , research compounds peptides and clinical antibodies (PMID 24234439). As PubChem pushes towards 70 million compound identifiers (CIDs), we have noticed the problem
of “multiplexing” during the curation of 5713 small molecules as CIDs. we encountered many representations (i.e. different CIDs) of the same pharmacological entities. Three types of variation dominate: stereochemistry, mixtures and isotopic analogues. These are known constitutive issues for chemical databases but in
recent years we observed this multiplexing was reaching
problematic proportions (i.e. more chaff), especially for clinically used drugs (i.e. proportionally less wheat)
Sorting bioactive wheat from database chaffChris Southan
Abstract
Databases of bioactive compounds are crucial for pharmacology, drug discovery and chemical genomics as public sources approach ~ 100 million records. However, in recent years this famine-to-feast presents difficulties for searching chemical structures and linked activity data, particularly for those unfamiliar with the constitutive challenges of molecular representation in silico (PMID 25415348). A key problem is entries of structural variants of “the same thing” as pharmacological entities (i.e. representational multiplexing). For example, a 2009 comparison of three database subsets of ~1200 approved drugs recorded only 807 structures in-common (PMID 20298516). In addition, published counts of approved drugs vary widely. These issues have been continually encountered by the Guide to PHARMACOLOGY database (GtoPdb) team that, since 2009, has achieved the curation of ~5500 small molecules (including approved drugs) from papers. Concomitantly, we have noticed an increase in multiplexing as PubChem pushes towards 65 million compound identifiers (CIDs). Since one of our key objectives is to affinity-map ligands to their targets, we decided to assess this multiplexing problem in order to optimise our curation rules. The results have implications for the entire bioactivity information space. We began by compiling CID sets for seven different sources within PubChem encompassing approved drugs. Initially a 7x7 pairwise comparison matrix indicated low overlap between these sources. A Venn diagram was then made from the approved drug CIDs mapped by DrugBank, Therapeutic Target Database and ChEMBL. At 749, the three-way intersect was less than 35% of the union of all CIDs covered by the sets. Strikingly, this looks worse that the 2009 study (although the sources and comparison methods were different). We will present further analyses that go some way towards explaining these results. One of these is determining “same connectivity” statistics inside PubChem as a measure of multiplexing. For DrugBank, each approved drug was related to, on average, 19 different CIDs as structural variants. Analysis of multiplexing confirmed trends we had observed during individual drug curation. This included ~ 30% stereoisomer enumerations but, surprisingly, ~70% isotopic derivatives, dominated by patent-derived virtual deuteration. We also established the ratio of submissions (SIDs) to CIDs was 78. The paradox was that, despite this high “majority vote” support for approved drug CIDs curated by DrugBank, only 55% were in the 3-way consensus (figures for the other two curated sources were similar). Analysing by year in PubChem indicated how the recent expansion of vendor and patent-extraction structures contributes to both multiplexing and the SID: CID ratio. While approved drugs are strongly impacted, associated problems, such as split activity data and deciding the “correct” structures, affect essentially all public drug discovery chem
Authors: María Herrero-Zazo, Isabel Segura-Bedmar, Paloma Martínez
CILC 2013, 5th International Conference on Corpus Linguistics, Alicante, Spain (March 16, 2013)
The DDI (Drug-Drug Interaction) Corpus
Lourdes Moreno, Rodrigo Alarcon, Isabel Segura-Bedmar, and Paloma Martínez. 2019. Lexical simplification approach to support the accessibility guidelines. In Proceedings of the XX International Conference on Human Computer Interaction (Interacción '19). ACM, New York, NY, USA, Article
https://dl.acm.org/citation.cfm?doid=3335595.3335651
Lourdes Moreno, Xabier Valencia, J. Eduardo Pérez, and Myriam Arrue. 2018. Exploring the Web navigation strategies of people with low vision. In Proceedings of the XIX International Conference on Human Computer Interaction (Interacción 2018). ACM, New York, NY, USA, Article 13, 8 pages
Exploring language technologies to provide support to WCAG 2.0 and E2R guidel...Grupo HULAT
Lourdes Moreno, Paloma Martínez, Isabel Segura-Bedmar, and Ricardo Revert. 2015. Exploring language technologies to provide support to WCAG 2.0 and E2R guidelines. In Proceedings of the XVI International Conference on Human Computer Interaction (Interacción '15). ACM, New York, NY, USA, , Article 57 , 8 pages. DOI=http://dx.doi.org/10.1145/2829875.2829927
URL ACM Digital Library: http://dl.acm.org/citation.cfm?id=2829927
Babelfy: Entity Linking meets Word Sense Disambiguation.Grupo HULAT
Babelfy is a unified, multilingual, graph-based approach to Entity Linking and Word Sense Disambiguation. This presentation is an explanation of the algorithm used by Babelfy.
Integration of Accessibility Requirements in the Design of Multimedia User Ag...Grupo HULAT
Integration of Accessibility Requirements in the Design of Multimedia User Agents Interfaces
PhD presentation
María Gonzalez, UC3M
Labda Group (http://labda.inf.uc3m.es )
Presentation "Spanish Resources in Trendminer Project"Grupo HULAT
Slides used in Course "Combining Language and Semantic Web Techonologies" held in UNED, January 21-22, 2015.
The talk is about the challenges in understanding health related natural language and the work done in TrendMiner European Project.
Mujeres, ciencia y tecnología. Encuesta sobre la percepción de las dificultad...Grupo HULAT
Authors: Lourdes Moreno, Yolanda González, Isabel Segura, Paloma Martínez
XV Congreso Interacción Persona Ordenador
10-12 de septiembre de 2014, Puerto de La Cruz, Tenerife
BioSEPLN 2010 Workshop on Language Technology applied to biomedical and heal...Grupo HULAT
BioSEPLN 2010 Workshop on Language Technology applied to biomedical and health documents, Valencia, España (September 6, 2010)
BioSEPLN 2010 Workshop on Language Technology applied to biomedical and health documents
Building a Graph of Names and Contextual Patterns for Named Entity Classifica...Grupo HULAT
Authors: César de Pablo Sánchez, Paloma Martínez
ECIR 2009: Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval, Tolouse, France (April 6-9 2009)
Accessibility to mobile interfaces for older peopleGrupo HULAT
Authors: Jose Manuel Díaz-Bossini, Lourdes Moreno
DSAI 2013: 5th International Conference on Software Development and Technologies for Enhancing Accessibility and Fightin Info-exclusion (November 2013, Vigo, Spain).
Accessibility to mobile interfaces for older people
Toward an integration of Web accessibility into testing processesGrupo HULAT
Authors: Mary Luz Sánchez, Lourdes Moreno
DSAI 2013: 5th International Conference on Software Development and Technologies for Enhancing Accessibility and Fightin Info-exclusion (November 2013, Vigo, Spain).
Toward an integration of Web accessibility into testing processes
Revisión de los requisitos de accesibilidad en la interacción del usuario anc...Grupo HULAT
Authors: Lourdes Moreno, Paloma Martínez
Interacción 2012: XIII edición del congreso internacional fomentado desde la Asociación para la Interacción Persona-Ordenador (AIPO) (Octubre 2012, Elche, España)
A review of accessibility requirements in elderly users' interactions with web applications, Proceedings of the 13th International Conference on Interacción Persona-Ordenador, October, 2012
Formación y tecnologías en accesibilidad para la UniversidadGrupo HULAT
Author: Lourdes Moreno
V Congreso Nacional sobre Universidad y discapacidad y XIV Reunión del Real Patronato sobre Discapacidad (Diciembre 2011, Madrid, España).
Formación y tecnologías en accesibilidad para la Universidad
Requisitos de accesibilidad web en los reproductores multimediaGrupo HULAT
Authors: María González-García, Lourdes Moreno, Paloma Martínez, Ana Iglesias
Interacción 2011: XII Congreso de Interacción Persona-Ordenador, (September 2011, Lisboa, Portugal).
Requisitos de accesibilidad web en los reproductores multimedia
Integrating HCI in a Web accessibility engineering approachGrupo HULAT
Authors: Lourdes Moreno, Paloma Martínez, Belén Ruiz-Mezcua
HCII 2009: 13th International Conference on Human-Computer Interaction. 5th International Conference on Universal Access in Human-Computer Interaction (HCII 2009), (July 2009, San Diego, CA, USA).
Integrating HCI in a Web accessibility engineering approach
A MDD approach for modelling web accessibilityGrupo HULAT
Authores: Lourdes Moreno, Paloma Martínez, Belén Ruiz-Mezcua
IWWOST 2008: 7th International Workshop on Web-Oriented Software Technologies, in conjuntion the 8th International Conference on Web Engineering (ICWE'2008) (July 2008, Yorktown Heights, New York, USA).
A MDD approach for modelling web accessibility,
Inclusive Usability Techniques in Requirements Analysis of Accessible Web App...Grupo HULAT
Authors: Lourdes Moreno, Paloma Martínez, Belén Ruiz-Mezcua
IWWUA 2007: 1st International Workshop on Web Usability and Accessibility in conjuntion with the 8th International Conference on Web Information Systems Engineering, (January 2007, Nancy, France).
Adaptation Rules for Accessible Media Player Interface Grupo HULAT
Authors: María González, Lourdes Moreno, Paloma Martínez
Interacción 2014: XV International Conference on Human Computer Interaction (September 2014, Puerto de la Cruz, Tenerife, Spain). Proceedings of the XV International Conference on Human Computer Interaction (INTERACCIÓN 2014), ACM, New York, ISBN: 978-1-4503-2, Número: 5
Adaptation Rules for Accessible Media Player Interface
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Combining Syntactic Information and domain-specific Lexical Patterns to Extract Drug-Drug Interactions from Biomedical texts
1. Combining Syntactic Information and
domain-specific Lexical Patterns to
Extract Drug-Drug Interactions from
Biomedical texts
Isabel Segura-Bedmar, Paloma Martínez, César de Pablo-Sánchez,
CS Department, Universidad Carlos III de Madrid, Spain
October 26, 2010
Toronto, Ontorio, Canada
DTMBIO'10
2. 2
Outline
Introduction
I
State of the Art
Proposal
Evaluation
Conclusions and Future Work
DrugDDI Corpus
IE processes for DDI Extraction: DrugDDI prototype
3. 3
What is a Drug-Drug Interaction (DDI)?
Introduction
6. 6
47.8% of adverse
eventes are due to
drugs, of which 3.5%
result from DDI1.
Medication errors kill
7,000 patients per
annum in USA2.
High incidence in certain
patient groups (3-20%).
Increase the Healthcare
costs
Things can get complicated...
1. APEAS Estudio sobre la seguridad de los pacientes en Atención primaria de salud.
Madrid: Ministerio de Sanidad y Consumo, 2008
2. Kohn et al., 2000. “To Err is Human”.
Introduction
9. 9
How does Information Extraction help?
A possible interaction resulting in acute
renal failure has been reported
in a few subjects when indomethacin,
a nonsteroidal anti-inflammatory agent,
was given with triamterene.
DDI ( INDOMETHACIN, TRIAMTERENE)
Introduction
10. 10
Objectives
Creation of an annotated corpus of DDI.
Introduction
Study the main approaches for biomedical RE.
Combine the resolution of complex syntactic
constructions and a set of lexical patterns
defined by a pharmacist in order to extract
DDIs.
12. State of the Art
Approaches
12
State of the Art
Relation
Extraction
In biomedicine
1
Linguisticbased
approaches
2
3
Patternbased
approaches
Machine Learning
based approaches
4
Hybrid approaches
16. Building a corpus for DDI
DrugBank
HTML To Text
Wrapper
Corpus TXT
Drug Name Recognition
MetaMap UMLS:
Text analysis
String Matching
Algorithm
2006AA
UMLSKS
WHOINN
affixes
DDI Extraction
XML annotated with drugs and other
biomedical concepts
XML annotated with drugs
interactions
Proposal: Corpus DrugDDI
18. 18
Text Analysis by MetaMap program
Corpus TXT
XML annotated with shallow
syntactic and semantic
information from UMLS
UMLS MetaMap
(MMTx):
Text analysis
Unified
Medical
Language
System
(UMLS)
Proposal: Corpus DrugDDI
DrugBank
24. 24
Corpus DrugDDI
Total
Avg .
per
doc
DDIs 3,160 5.5
Sentences 5,806 10.2
Sentences with at least one DDI 2,044 3.5
Drugs 14,930 25.7
Documents 579
Proposal: Corpus DrugDDI
(http://labda.inf.uc3m.es/DrugDDI/)
25. Create an
annotated corpus
to study the
Extraction of DDI
Build a
Pattern-based
method to
Extract DDI
Proposal: DDI extraction
26. 26
IE System for DDI
Corpus TXT
Text analysis
XML annotated
with shallow
syntactic and
semantic
information
Drug Name
Recognition
Anaphora
Resolution
DDI Extraction
+
drugs and other
biomedical
concepts
+
anaphoras
+
Drug
interactions
Biomedical Resources
Proposal: DrugDDI prototype
whoole
27. 27
Drug Name Recognition (DrugNer)
Corpus TXT
+
drugs and other
biomedical
concepts
+
anaphoras
+
DrugDrug
interactions
WHOINN
affixes
UMLS
XML annotated
with shallow
syntactic and
semantic
information
Proposal: DrugDDI prototype
Drug Name
Recognition
Anaphora
Resolution
DDI Extraction
Text analysis
28. 28
Drug Anaphora Resolution
Corpus TXT
Biomedical Resources
Proposal: DrugDDI prototype
Drug Name
Recognition
Anaphora
Resolution
DDI Extraction
Text analysis
+
drugs and other
biomedical
concepts
+
anaphoras
+
DrugDrug
interactions
XML annotated
with shallow
syntactic and
semantic
information
29. 29
Drug-Drug Interaction Extraction
Corpus TXT
Biomedical Resources
Proposal: DrugDDI prototype
Drug Name
Recognition
Anaphora
Resolution
DDI Extraction
Text analysis
+
drugs and other
biomedical
concepts
+
anaphoras
+
DrugDrug
interactions
XML annotated
with shallow
syntactic and
semantic
information
30. 30
Syntactic Information + Lexical
Patterns
Proposal: DrugDDI prototype
XML annotated with
shallow syntactic and
semantic information,
drugs and other
biomedical concepts,
anaphoras
DrugDrug
interactions
Detection of
coordinate structures
Detection of
appositions
Pattern
Matching
Clause Splitting
Rules for sentence
simplification
32. Allopurinol interacts with anisindione, azathioprine and cyclosporine
How does syntactic information help?
Detecting coordinate structures
Proposal: DrugDDI prototype
33. Allopurinol interacts with anisindione, azathioprine and cyclosporine
Detection of
Coordinate structures
COORD := ([NP|PP|AJD|UNK],)* [NP|PP|
ADJ|UNK] CONJ [NP|PP|ADJ|UNK]
Allopurinol interact with COORD
Detecting coordinate structures
Proposal: DrugDDI prototype
How does syntactic information help?
34. Allopurinol interacts with anisindione, azathioprine and cyclosporine
Detection of
Coordinate structures
COORD := ([NP|PP|AJD|UNK],)* [NP|PP|
ADJ|UNK] CONJ [NP|PP|ADJ|UNK]
Allopurinol interacts with COORD
Drug Name Recognition
DRUG.1 interacts with COORD
Detecting coordinate structures
Proposal: DrugDDI prototype
How does syntactic information help?
35. Allopurinol interacts with anisindione, azathioprine and cyclosporine
DDI := <DRUG1|COORD|APPOSITION>
INTERACTS WITH
<DRUG2|COORD|APPOSITION>.
Detection of
Coordinate structures
Pattern Matching
COORD := ([NP|PP|AJD|UNK],)* [NP|PP|
ADJ|UNK] CONJ [NP|PP|ADJ|UNK]
Allopurinol interacts with COORD
Drug Name Recognition
DRUG.1 interacts with COORD
Detecting coordinate structures
Proposal: DrugDDI prototype
How does syntactic information help?
36. Allopurinol interacts with anisindione, azathioprine and cyclosporine
DDI := <DRUG1|COORD|APPOSITION>
INTERACTS WITH
<DRUG2|COORD|APPOSITION>.
Detection of
Coordinate structures
Pattern Matching
DRUG-DRUG INTERACTION:
Drug 1: Allopurinol
Drug 2: anisindione
COORD := ([NP|PP|AJD|UNK],)* [NP|PP|
ADJ|UNK] CONJ [NP|PP|ADJ|UNK]
Allopurinol interacts with COORD
Drug Name Recognition
DRUG.1 interacts with COORD
DRUG-DRUG INTERACTION:
Drug 1: Allopurinol
Drug 2: azathioprine
DRUG-DRUG INTERACTION:
Drug 1: Allopurinol
Drug 2: cyclosporine
Detecting coordinate structures
Proposal: DrugDDI prototype
How does syntactic information help?
37. Catecholamine-depleting drugs, such as reserpine, may have an additive
effect when given with beta-blocking agents.
DDI := <DRUG1|APPOSITION>
(HAVE|INCREASE|...) <EFFECT>
WHEN GIVEN WITH
<DRUG2|APPOSITION>.
Detection of
Appositions
Pattern Matching
DRUG-DRUG INTERACTION:
Drug 1: Catecholamine-depleting drugs
Drug 2: beta-blocking agents
Property|Effect: additive
DRUG-DRUG INTERACTION:
Drug 1: Reserpine
Drug 2: beta-blocking agents
Property|Effect: additive
APPOSITION may have an additive effect when given with DRUG.
APPOSITION := <APPOSITIVE>
MARKER <APOSITIVE>
How syntactic information helps?
Proposal: DrugDDI prototype
Detecting appositions
38. Concomitant administration of corticosteroids with Aspirin may increase the risk of
gastrointestinal ulceration and may reduce serum salicylate levels.
Concomitant administration of
corticosteroids with Aspirin may
increase the risk of
gastrointestinal ulceration
Concomitant administration of
corticosteroids with Aspirin may
reduce serum salicylate levels.
PATTERN: ADMINISTRATION
OF <DRUG1> WITH
<DRUG2>
MAY (INCREASE|REDUCE)...
Clause splitting
Pattern Matching
DRUG-DRUG INTERACTION:
Drug 1: Corticosteroids
Drug 2: Aspirin
Action: increase
Property|Effect: Gastrointestinal
ulceration
DRUG-DRUG INTERACTION:
Drug 1: Corticosteroids
Drug 2: Aspirin
Action: reduce
Property|Effect: serum salicylate
levels
Proposal: DrugDDI prototype
Detecting clauses
How syntactic information helps?
41. 41
2nd Experiment: Lexical Patterns
Evaluation
XML annotated with
shallow syntactic and
semantic information,
drugs and other
biomedical concepts,
anaphoras
DrugDrug
interactions
Pattern
Matching
42. 42
3th Experiment: Coordinate structures
and appositions
XML annotated with
shallow syntactic and
semantic information,
drugs and other
biomedical concepts,
anaphoras
DrugDrug
interactions
Detection of
coordinate structures
Detection of
appositions
Pattern
Matching
Clause Splitting
Rules for sentence
simplification
Evaluation
43. 43
4th Experiment: Coordinate structures,
appositions and clauses
XML annotated with
shallow syntactic and
semantic information,
drugs and other
biomedical concepts,
anaphoras
DrugDrug
interactions
Detection of
coordinate structures
Detection of
appositions
Pattern
Matching
Clause Splitting
Rules for sentence
simplification
Evaluation
47. Future Work
47
Conclusions
Treatment of negation and modality.
Integrate the drug anaphora resolution in the
DDI extraction.
Improve the clause splitting process.
Handle the mistakes made by MetaMap.
48. Future Work
48
Conclusions
Use the SPINDEL [De Pablo-Sánchez et al., 2009]
system to acquire new patterns.
Evaluate Machine learning techniques.
Increase the quality of the DrugDDI corpus.
52. Projects
52
Conclusions
This work has been partially supported by the Spanish research
projects:
MAVIR consortium (S-0505/TIC-0267, www.mavir.net), a network
of excellence funded by the Madrid Regional Government.
ISSE: Semantic Interoperability in Electronic Healthcare (FIT-
350300-2007-75).
BRAVO: Advanced Multimodal and Multilingual Question
Answering. (TIN2007-67407-C03-01).
MULTIMEDICA.
53. 53
WHO affixes
for identifying and classifying drugs
Affixes
WHOINN
Drug Family Pattern Drugs
pristin Antibacterials,
pristinamycin
derivatives
[AZaz09]*[pristin] Efepristin
gatran Antithrombotic
agents
[AZaz09]*[gatran] Dabigatran
-tinib Antineoplastic
agents
[AZaz09]*[tinib] Dasatinib,
Sunitinib,
Nilotinib
-mycin -Antibiotics [AZaz09]*[mycin] Tanespimycin
Proposal: DrugDDI prototype
55. Score-based approach for
Anaphora Resolution in Drug-
Drug Interacion Documents
(Segura-Bedmar et al., 2009a)
NLDB 2009
DrugNerAR: Linguistic Rule-
Based Anaphora Resolution for
DDI Extraction in
pharmacological documents
(Segura-Bedmar et al., 2009b
To appear in DTMBIO 2009
56. 56
How does Information Extraction help?
Triamterene, metformin and amiloride
should be administered with care
as they might increase dofetilide levels.
DDI ( TRIAMTERENE, DOFETILIDE)
DDI ( METFORMIN, DOFETILIDE)
DDI ( AMILORIDE, DOFETILIDE)
Introduction
Ok, I am the next speaker. My name is Isabel Segura Bedmar from the
University Carlos III of Madrid.
I&apos;m going to talk about an approach for the extraction of drug-drug interactions from biomedical texts. This approach combines syntactic information and a set of lexical patterns to extract these interactions from text. Of course, the method is not novel, but it is the problem of extraction drug interactions. Maybe after you have listened to me, you may are interested in working in this novel domain.
Ok, this is the typical outline /autline/. Firstly, I am going to explain /iks&apos;plein/ why the extraction of DDI /didiai/ from texts is important. Secondly, I&apos;m going to talk about the main approaches in biomedical relation extraction, Thirdly, I&apos;m going to describe our hybrid approach and finally, I am going to present the results /&apos;risolts/ and main conclusions.
So, what is a drug-drug interactiong?
A DDI occurs /a&apos;curs/ when a drug influences /in&apos;fluensis/ the level or the activity of another drug. In other words /wuerds/, it&apos;s a reaction between two or more drugs taken concurrently.
Some interactions can be beneficial, in fact, it&apos;s very common to combine several drugs in order to obtain more effective drugs. For example, the combination of retrovirals like ritonavir and lopinavir achieves a more potent /po&apos;tnt/ antiretrovial.
/Sam interakshons can be benefishol, in fact, it&apos;s very kamon to com-bain sevral drags in order to obtein more iffektive drags. For exampol, the combinesion of re&apos;trovirals like ritonavir and lopinavir achieves a more potent antiretroviral/.
But unfortunatly, many interactions are very dangerous. For example, aspirin and heparin taken concurrently can cause a bleeding.
/Bat anfortunatli, meni interakshons are very den&apos;yeras/.
For igsam&apos;pol, asprin and heparin teikin cancurrentli can co:s a bli:din/
Or, Aspirin and Acetazolamide can cause the death of the patient.
Or, asprin and acetazolamide can co:s the dez of the peishent.
In fact, several studies have shown that drug interactions are a serious problem for the patient safety.
In fact, sevral stadis hav shon dat drag interakshons are a sirias problem for the peishent seifti.
In addition, drug interactions increase the healthcare costs.
In adision, drug interakshons incri:s the helzker costs.
Therefore, it&apos;s very important to develop resources for healthcare professionals in order to detect these drug interactions.
Derefor, its very important to dive&apos;lop risoursis for helzker professionals in order to ditekt thiis drug interakshons.
j
So, hau do helzker proffesionasl avoid /&apos;avoid/ drug-drug interarkshons
There are several resources that contain information about drug-drug interactions (for example, micromedex or drugbank databases /deitabesis/), but unfortunatly, they are not comprehensive, because many interactions are only reported in medical journal or in drug safety reports. Therefore, healthcare professionals have to spend a long time in reading them.
/There are sevral risoursis that contein informashon abaut drag-drag interakshons, bat anfortunatli, dei are not compre&apos;jensive, because meni interakshons are only reportid in medical yurnal or riports. Helzker professionals have to spend a long time in ri:din them/
For this reason, we think that Information Extraction can help to improve the early detection of drug interactions and to reduce the time spent by healthcare proffessionals on reading the drug safety reports and medical journals,
/for dis ri&apos;son, wi think that informeison extrakson can help to impruv the erli detekshon of drag interakshons and to ridus the taim spent by helzker profesionals on riiding the medical journals and riports/.
For example, you can see this sentence “A possible interaction resulting in acute renal failure has been reported in a few subjects when indomethacin, a nonsteroidal anti-inflammatory agent, was given with triamterene”
/for igsampol, yu can sii dat dis sentens “a posibol interakshon risalting in akiut rinal feilier has biin riportid in a fiu sabjects when indometazin, a nonsteroidal anti-inflamatori agent, was given with triamteren/
The final goal of our method is to identify the drugs (indomethacin and tramteren) and to detect the interaction between them.
/the fainal gol of auer mezod is to identifai de drags (indometazin and tramteren) and to ditect the interakshon bituin dem/
In more detail /di&apos;teil/, the objectives of this work are:
First, we must study the main approaches for biomedical relation /rileshon/ extraction.
Second, we need to build an annotated /a&apos;noteitid/ corpus to evaluate /ivalueti/ the approach.
Third, we are combining the resolution /risolusion/ of complex syntactic constructions /canstrakshons/ and a set of lexical patterns defined /difainid/ by a pharmacist in order to extract drug interactions.
The goal /gol/ of biomedical relation /rileishon/ extraction is to detect /di&apos;tekt/ occurrences /a&apos;kurensis/ of a predefined /pridifainid/ type of
relationship between a pair /per/ of given entity types (like genes, proteins or drugs) in text.
During the last years /yirs/, many works /wurks/ have
addressed /a-drest/ this task, in particular, the extraction of protein interactions, however /hau&apos;var/, no approach has been proposed /propoust/ to solve /solf/ the problem of extracting DDIs in biomedical texts.
Current /ka-rent/ approaches to biomedical relation extraction may be classified in four main categories:
Linguistic approaches are based /beist/ on the application of linguistic technology /teknology/. Patterns approaches apply rules to detect the relationships. Approaches that use machine learning techniques /tekniks/. Of course, many works combine these approaches.
In general, the approaches show poor results. In fact, the best system for PPI extraction in BioCreative workshop in last year achieve an f-measure of 30%.
Few approaches have dealt with the complexity /&apos;campleksity/ of biomedical sentences. However, language structures /strakchurs/ such as appositions /a&apos;posishons/, coordinations and complex sentences are very common in the biomedical literature /&apos;literachur/.
In our case, for this first approximation for DDI extraction, we decided to combine shallow parsing and pattern matching.
Ok, Now, I&apos;m goint to describe our proposal. First of all, we built an annotated corpus with DDI to evaluate our method.
First, I&apos;m going to describe the creation of the first corpus annotated with DDI, and then, I&apos;m goint to present our system for DDI extraction.
We are talking about the construction of a corpus for drug interactions and the developtment of a system for drug drug interaction extraction.
For building the corpus, we used the pharmacological database DrugBank that contains information about almost five thousand drugs.
For bildin the corpus, wiyus the pharmacological databeis DragBank dat conteins informations about olmost faiv zausand drags.
In particular, for each drug, DrugBank also contains a text document with information about its drug interactions.
For example, this text shows interactions for Heparin.
In the last paragraph, the sentence:
Digitalis, tetracyclines, nicotine, or antihistamines may partially counteract the anticoagulant action of heparin sodium.
Contains several interactions with heparin sodium.
We have used these text documents to build our corpus.
/En particular, for ich drag, DragBank olso conteins a text document with information about its drug interakshons/
For example, this text shous the interakshons for heparin. In the last paragraf, the sentens:
Digitalis, tetracyclines, nicotine, or antihistamines mei parshali kaunterakt di anticoagualnt acsion of heparin sodium.
Contains sevral interakshons wiz heparin sodium/
Wi haev ju:st thi:s text documents to bild aur corpus.
Then, We used the MetaMap tool for parsing the texts.
This tool provides shallow syntactic information and semantic information from
the biomedical UMLS ontology.
You can see an example:
First, MetaMap splits the text into sentences.
Then, MetaMap makes shallow parsing.
Also, Metamap performs tokenization
Finally, for each phrase, MetaMap tries to look for a concept in the UMLS ontology.
For example, for the phrase &apos;Aspirin&apos;.
MetaMap found the UMLS concept with the identifier
C0004057.
Also, MetaMap provides semantic types to classify the phrases, for example, the phrase &apos;Aspirin&apos; was classified by MetaMap as a pharmacological substance and as organic chemical.
Once /guans/ we have collected /kolektid/ and processed the corpus, we annotated the documents with the assistant of a pharmacist
For example, in this case, we annotated the three interactions between Aspirin and these three drugs.
Each drug interaction is represened in a DDI element that contains attributes for the name of the drugs that interact and its phrase id.
The corpus consists of almost six hundred documents and contains around of fifteen thousands drugs, and three thousand drug interactions.
In a future, we would like to work with more pharmacists in order to assess the agreement among /aman/ several annotators /anoteitars/
Ok, I have just described the process of construction of the corpus.
And, now, I&apos;m going to explain our hybrid approach for the extraction of drug-drug interactions.
.
You can see our system that consits of four main modules. I have just describe the first process for obtaining the syntactic and semantic information by the MetaMap tool.
The second process tries ecognizes and classifies the drug names. The third process is an anaphora resolver. But in this presentation, I focuns on the fourth process for the extraction of DDI.
Regarding the drug name recognition, we used the semantic information provided by MetaMap in order to identify drug names.
The third process tries to resolve the pronominal and drug nominal anaphors occuring in the texts by a set of linguistic heuristics.
Now, we are going to explain the most important process to extract DDIs from texts.
The basic idea is to detect linguistic complex structures such as coordinations, appositions and clauses /closis/ becuase they are very common in biomedical texts.
The method proposes a set of syntactic patterns to detect the structures and then applies a set of rules for sentence simplification.
Finally, relations are extracted from simple sentences by a pattern matching
Despite the richness of natural language expressions, in practice, DDI are often expressed by a limited number of constructions. This fact favors the use of patterns as an excellent method for their extraction. Based on her professional
experience and the corpus observation, our pharmacist defined a set of lexical patterns (see Table 6) to capture the various language constructions used to express DDI in
pharmacological texts. Moreover, the pharmacist provided a set of synonyms for the verbs that can indicate a possible DDI.
Now, I am going to explain by an example why the resolution of these structures is important.
For example, you can see the following sentence:
Our method, first, is able to identify the coordination by this pattern.
In a similar way, our method identifies appositions and applies pattern matching to detect DDIs.
For the clause splitting, we use a set of clues (for example, the use of relative pronouns, subordinators, coordinators, commas) to detect the clause boundaries /baundris/
Once the bondaries /baundris/ have been detected, new simple sentences are generated
from these clauses by a set of simplification rules
Now, I&apos;m going to describe the experiments that we have performed.
Our baseline experiment considers every pair of drugs as a DDI. This baseline achives the maximum recall but a low precision.
In our 2nd experiment, we have only applied the lexical pattern on the sentences in the corpus DrugDDI, without performing syntactic processing.
In our 3rd experiment, we first resolve coordinatives and appositions by a set of syntactic patterns, as I have just described, and then, we applied the lexical patterns on the sentences in order to detect the DDIs.
In our last experiment, we also detected the clause boundaries /clos baundris/ by a clause splitting algorithm and split the complex and compound /com-pound/ sentences into simple sentences. Finally, we applied lexical pattern matching /maching/ on these simple sentences.
This graph compares the three experiments.
You can see that the baseline achieves a very low precision (only 11%).(the green bars)
Llexical patterns achieve the best precision (67%), however, obtain a low recall (14%). (the blue bars)
The detection of coordinatives and appositions helps to improve the recall to 26%, but the precision decreases to 48%. (the red bars)
Finally, you can see that the clause splitting and sentence simplification do not achieve our second experiment. (the yellow bars)
This is mainly due to two reasons: first, our method for clause splitting is too much basic and is not able to detect the boundaries, but on the other hand, many interactions often span several clauses and our lexical patterns are not able to capture them.
So to conclude...
Our main contributions are:
We have created the first annotated corpus with drug-drug interactions. and we have developed the first approximation for DDI extraction based on the use of syntactic information and lexical patterns. Also, we have performed several experiments.
We can conclude that the resolution of complex linguistic constructions and pattern matching can help to detect DDI, although we know that there is lot of work to do yet.
Future directions include to improve the DrugDDI corpus.
Also, we will improve the resolution of the syntactic constructions by the handling of the mistakes made by Metamap.
Other important point, is the integration of the drug anaphora resolver in order to extract DDIs that span several sentences.
Of course, we must improve our clause splitting process and the treatment of negation and modality.
We will apply a bootstraping method called Spindel to acquire new patterns.
Also, we will consider to apply machine learning techniques.
We will also detect relevant information such as the severity, mechanism /mekanisim/, dosage drugs /dosich drags/, time course that will help to improve the quality of drug interaction resources.
Also, we think that it is very important to carry out an user-oriented evaluation.
Debo decir que este proyecto se ha desarrollado en el marco de dos proyectos de investigación: el proyecto ISSE sobre interoperabilidad semántica en el dominio de la salud y el proyecto BRAVO. También agradecer al consorcio MAVIR su apoyo durante este trabajo.
And also, we applied a set of affixes to identify and classify drugs that are not detected by MetaMap.
You can see a more detailed /diteld/ description of this drug name recogniton taks in our paper “Drug name recognition and classification in biomedical texts”
\
We have developed two different approaches for anaphora resolution.
We believe that they are the fisrt works for the pharmacological domain.
The first approach uses syntactic and semantic features to score the
antecedents. This approach has been published in NLDB conference, held in
saarbrucken.
The second approach uses a set of linguistic rules (based on the
centering theory) in order to find the antecedents.
This work has been submitted to the DTMBIO workshop.
\textcolor{blue}{\textit{
We have di-vilop two different a-ppro-ches for anaphora ri-so-lusson.
We bi-liv that they are the first works for the pharmacological domein.
The first approach uses syn-tac-tic and si-man-tic features to score the
antecedents.
This approch has been published in the NLDB (en-el-di-bi) conference, held in
saabrucken, this year.}
The second approach ius a set of linguistic ruuls based, on the sente-ring
zeo-ri, in order to faind the antecedents}
For this reason, we think that the information extraction can improve the early
detection of drug interactions.
We are developing a system that provides a more effective access to this information.
This is a very simple sentence
&apos;Aspirin may decrease the effects of probenecid, sulfinpyrazone and phenylbutazone&apos;.
It contains three interactions.
So, the goal of our system is to identify the drug names and extract the interactions between them.
DrugBank is an online and free database. You can seach /serch/ any drug, for example, heparin.
DragBank isan online and fri databeis.
Yu can serch ani drug, for igsampol, heparin, and then, drugbank shows pharmacological and kemical information about this drug.