Biomedical Text Mining and Ontologies

A Framework for Automated Construction of Heterogeneous Large-Scale Biomedical Knowledge Graphs

The paper introduces PheKnowLator, a framework for constructing large-scale biomedical knowledge graphs, and evaluates its performance on human disease mechanisms.

Lisen&Curate: A platform to facilitate knowledge tools for curation of regulation of transcription initiation in bacteria

Lisen&Curate is a system that aids manual curation of bacterial transcription initiation regulation by integrating text mining and sentence similarity metrics to accelerate literature review and knowledge extraction.

L-Regulon: A novel "soft-curation" approach supported by a semantic enriched reading for RegulonDB literature.

The paper introduces L-Regulon, a soft-curation approach using semantic enrichment to improve knowledge extraction from scientific literature, specifically applied to RegulonDB data.

Self-normalizing learning on biomedical ontologies using a deep Siamese neural network

The paper introduces a method that uses deep Siamese neural networks to normalize biomedical ontologies and improve predictions of protein interactions and gene-disease associations.

Real-time biomedical knowledge synthesis of the exponentially growing world wide web using unsupervised neural networks

The paper introduces a platform for real-time synthesis of biomedical knowledge using unsupervised neural networks, integrating unstructured literature with structured databases.

Pangaea: A modular and extensible collection of tools for mining context dependent gene relationships from the biomedical literature

Pangaea extracts context-dependent gene relationships from biomedical literature using natural language processing and provides scalable, extensible tools for analysis.

deepMINE - Natural Language Processing based Automatic Literature Mining and Research Summarization for Early Stage Comprehension in Pandemic Situations specifically for COVID-19

The paper presents a system for automatic literature mining and summarization using deep learning to accelerate research during pandemics, specifically for COVID-19.

MeSHHeading2vec: A new method for representing MeSH headings as feature vectors based on graph embedding algorithm

This paper introduces MeSHHeading2vec, a method that represents MeSH headings as vectors using graph embedding algorithms, showing improved performance in node classification and relationship prediction tasks.

Wikidata as a FAIR knowledge graph for the life sciences

The paper describes Wikidata as a FAIR knowledge graph for life sciences, showcasing its breadth, tools for integration, and use cases in biomedical analysis.

A computational approach for mapping heme biology in the context of hemolytic disorders

The paper develops an ontology and knowledge graph to model heme biology and investigates its role in Toll-like receptor signaling.

Automated recognition of functional compound-protein relationships in literature

The paper develops a benchmark dataset and two machine learning models to classify compound-protein relationships in scientific abstracts as functional or non-functional.

DISNET: A framework for extracting phenotypic disease information from public sources

DISNET extracts phenotypic disease information from public sources to create customizable disease networks for drug repositioning.

A post-processing algorithm for building longitudinal medication dose data from extracted medication information using natural language processing from electronic health records

This paper introduces a post-processing algorithm that converts raw NLP output from EHRs into usable medication dose data for analysis.

Evaluating Representations for Gene Ontology Terms

This paper evaluates neural network models for embedding Gene Ontology terms and introduces new models based on GCN, ELMo, and BERT, finding that BERT-based embeddings perform best for rare labels.

Automated methods enable direct computation on phenotypic descriptions for novel candidate gene prediction

The paper translates plant phenotype descriptions into structured formats using computational methods and shows they perform comparably to manual curation for identifying genetic associations.

BEE : a web service for biomedical entity exploration

BEE provides a natural language query interface for exploring relationships among biomedical entities across multiple datasets.

PGxCorpus: a Manually Annotated Corpus for Pharmacogenomics

PGxCorpus provides a manually annotated dataset of 945 sentences to extract pharmacogenomics relationships from text, enabling supervised machine learning applications in the field.

Vapur: A Search Engine to Find Related Protein - Compound Pairs in COVID-19 Literature

Vapur is a search engine that finds protein-chemical pairs in COVID-19 literature using a relation-oriented inverted index.

Structured Reviews for Data and Knowledge Driven Research

The paper presents a structured review of NGLY1 Deficiency as a knowledge graph, improving hypothesis generation through FAIR principles and enabling collaborative curation.

ModEx: A text mining system for extracting mode of regulation of Transcription Factor-gene regulatory interaction

ModEx extracts mode of regulation (activation/repression) for TF-gene interactions from ChIP-Seq data using text mining and achieves high F-scores on curated datasets.

Formal axioms in biomedical ontologies improve analysis and interpretation of associated data

The paper evaluates how formal axioms and ontology metadata improve ontology-based predictions of protein-protein interactions and gene-disease associations.

Relation extraction between bacteria and biotopes from biomedical texts with attention mechanisms and domain-specific contextual representations

The paper presents a deep learning model for biomedical relation extraction between bacteria and biotopes, achieving higher F1 scores than existing methods.

Text-mining clinically relevant cancer biomarkers for curation into the CIViC database

The paper describes the development of CIViCmine, a text-mining tool that extracts clinically relevant cancer biomarkers from literature to aid curation into the CIViC database.

A Literature-Based Knowledge Graph Embedding Method for Identifying Drug Repurposing Opportunities in Rare Diseases

This paper develops a knowledge graph embedding method to identify drug repurposing opportunities for rare diseases by integrating biomedical literature and validating hypotheses through external sources.

GeDex: A consensus Gene-disease Event Extraction System based on frequency patterns and supervised learning

GeDex extracts consensus gene-disease associations from biomedical literature using frequency patterns and supervised learning, achieving competitive performance compared to existing methods.

Enzyme annotation in UniProtKB using Rhea

The paper updates UniProtKB enzyme annotations using Rhea, replacing textual descriptions with structured biochemical reaction data and improving search capabilities.

Ontology based mining of pathogen-disease associations from literature

The paper extracts pathogen-disease associations from literature using ontology and statistical methods, resulting in 3,420 associations integrated into a database.

Applying Citizen Science to Gene, Drug, Disease Relationship Extraction from Biomedical Abstracts.

The paper demonstrates that citizen scientists can perform relationship extraction from biomedical abstracts using Mark2Cure, highlighting the importance of accurate named entity recognition.

WikiMedMap: Expanding the Phenotyping Mapping Toolbox Using Wikipedia

WikiMedMap maps phenotype strings to clinical vocabularies using Wikipedia, outperforming existing tools in matching accuracy.

Using distant supervision to augment manually annotated data for relation extraction

The paper uses distant supervision to augment small manually annotated datasets for relation extraction, improving model performance after filtering noisy data.