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Name: BioTM, LASIGE, Fac. Ciências, Universidade de Lisboa
Type: Organization
Bio: Biomedical Text Mining Team
Location: Lisboa, Portugal
Name: BioTM, LASIGE, Fac. Ciências, Universidade de Lisboa
Type: Organization
Bio: Biomedical Text Mining Team
Location: Lisboa, Portugal
Biomedical Term Annotator
LASIGE ULisboa Team work at BioASQ9B
lasigeBioTM at BioCreative VII Track 1: Text mining drug and chemical-protein interactions using biomedical ontologies
Repository with code and data used on the Biomedical Text Mining workshop at Bioinformatics Open Days 2017
BiOnt: Deep Learning using Multiple Biomedical Ontologies for Relation Extraction
Generating Scientific Question Answering Corpora from Q&A forums
Annotation of Human Phenotype-Gene Relations - Improving Accessibility and Distinction between Negative Results in Biomedical Relation Extraction
Annotating a multilingual COVID-19-related corpus
Biomarker Literature Retrieval
BOE: BiOnt Enhanced for Relation Extraction in Gene and Chemical Compounds interactions (DrugProt corpus)
Classifying Relations via Long Short-Term Memory Networks along Biomedical Ontologies
Code relative to the participation of the lasigeBioTM team in the CANTEMIST-CANcer TExt Mining Shared Task – tumor named entity recognition.
create Astro Ratings Matrix
Chemical Compounds Recommender Matrix
Clinical Timeline Annotation (CiTA)
Semantic Similarity Measures using Disjunctive Shared Information
Deep Recommenders with Python: A Python library for building Deep Learning based Recommender Systems
Evaluating Recommender Systems
A Systematic Pipeline to Unravel the Convergence Patterns of Genetic Variants in Complex Diseases
Identifying Biomedical Entities
Identifying Biomedical Relations
ICERL system: Iberian Cancer-related Entity Recognition and Linking system ICR corpus: Iberian Cancer-related corpus
Identifying Cellular Relations
Identification of Human Phenotype Entities
Calculate the Jaccard Similarity Coefficient between two sets of terms based on their ancestors in an OBO ontology
Biomedical Relation Extraction with Knowledge Graph-based Recommendations
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