Biomedical Text Mining and Ontologies

NLIMED: Natural Language Interface for ModelEntity Discovery in Biosimulation ModelRepositories

NLIMED converts natural language queries into SPARQL to discover model entities from biosimulation model repositories.

Auto-CORPus: A Natural Language Processing Tool for Standardising and Reusing Biomedical Literature

Auto-CORPus converts biomedical publication HTML and tables to standardized formats for text analytics.

Rapid Knowledgebase Construction and Hypotheses Generation Using Extractive Literature Search

The paper presents a method and tool for constructing personalized knowledgebases from scientific literature, enabling researchers to generate hypotheses and explore data efficiently.

LinkExplorer: Predicting, explaining and exploring links in large biomedical knowledge graphs

LinkExplorer predicts, explains, and explores links in biomedical knowledge graphs using an explainable algorithm with a web interface for interactive analysis.

BioThings SDK: a toolkit for building high-performance data APIs in biomedical research

The paper introduces the BioThings SDK, a toolkit for building high-performance data APIs in biomedical research, enabling integration of annotations from multiple data sources.

A visual atlas of genes tissue-specific pathological roles

The paper constructs a visual atlas of gene-pathology relationships by mining PubMed abstracts and using NLP to identify tissue-specific gene roles in disease.

HLA-SPREAD: A Natural Language Processing based resource for curating HLA association from PubMed abstracts

HLA-SPREAD curates HLA associations from PubMed abstracts using NLP, providing structured data on diseases, ADRs, and populations across 112 countries and 32 ethnic groups.

STonKGs: A Sophisticated Transformer Trained on Biomedical Text and Knowledge Graphs

The paper introduces STonKGs, a Transformer model trained on biomedical text and knowledge graphs, which outperforms single-modality models in classification tasks.

A Sequence Labeling Framework for Extracting Drug-Protein Relations from Biomedical Literature

This paper develops a sequence labeling framework for drug-protein relation extraction, achieving higher performance than a text classification framework in the BioCreative VII challenge.

Gilda: biomedical entity text normalization with machine-learned disambiguation as a service

Gilda is a tool that normalizes biomedical entity names using machine-learned disambiguation models and provides a web service for this task.

DISEASES 2.0: a weekly updated database of disease-gene associations from text mining and data integration

DISEASES 2.0 updates a disease-gene association database by integrating text mining, curated data, and GWAS, increasing text-mined associations by 9-fold.

Creation and evaluation of full-text literature-derived, feature-weighted disease models of genetically determined developmental disorders

The paper develops automated methods to extract phenotypic features from literature to improve disease modeling for developmental disorders, showing increased accuracy over manual curation.

Concept Recognition as a Machine Translation Problem

This paper explores sequence-to-sequence machine translation for concept recognition in biomedical text, achieving state-of-the-art results on the CRAFT Corpus with lower computational demands.

The evolution of knowledge on genes associated with human diseases

This study tracks the evolution of gene-disease associations over 30 years using text-mining to reveal shared mechanisms and drug targets across diseases.

LitRev: A data driven method for quick literature review from PubMed

LitRev identifies common and important biological phrases from PubMed to aid in quick literature reviews.

LEXAS: a web application for life science experiment search and suggestion

LEXAS is a web tool that curates biomedical experiments and suggests next experiments on genes by analyzing literature and training a machine learning model.

TaxoNERD: deep neural models for the recognition of taxonomic entities in the ecological and evolutionary literature

TaxoNERD is a deep learning tool for recognizing taxonomic entities in ecological texts, outperforming existing methods on ecological corpora.

GraphPrompt: Biomedical Entity Normalization Using Graph-based Prompt Templates

This paper introduces GraphPrompt, a graph-based prompt learning method for biomedical entity normalization, improving performance on zero-shot and few-shot settings using a curated dataset of entity-synonym pairs.

The Aliment to Bodily Condition knowledgebase (ABCkb): A database connecting plants and human health

The paper introduces ABCkb, a database linking plants to human health by integrating curated data and mined relationships to generate nutritive hypotheses.

Automatic Consistency Assurance for Literature-based Gene Ontology Annotation

This paper introduces a method to automatically detect inconsistencies in gene ontology annotations using text mining models, achieving high precision on synthetic data.

Extending and using anatomical vocabularies in the Stimulating Peripheral Activity to Relieve Conditions (SPARC) program

The paper describes the development and implementation of a vocabulary extension pipeline for the SPARC program to improve FAIR data practices in autonomic nervous system research.

Combining dictionary- and rule-based approximate entity linking with tuned BioBERT

The paper describes a two-stage approach combining fine-tuned BioBERT for chemical entity recognition with semantic approximate search in MeSH and PubChem for entity linking, addressing challenges in chemical text-mining.

ontoFAST: An R package for interactive and semi-automatic annotation of characters with biological ontologies

ontoFAST is an R package that enables fast and interactive annotation of characters using biological ontologies, with features for querying and visualizing ontological hierarchies.

Interpretable Visualization of Scientific Hypotheses in Literature-based Discovery

The paper introduces an interpretable visualization approach for scientific hypotheses using semantic concept interconnectivity, network-based methods, and topic modeling, validated with a pipeline evaluated by experts.

A BERT-Based Hybrid System for Chemical Identification and Indexing in Full-Text Articles

The paper develops a hybrid system for chemical identification and indexing in full-text articles using BERT-based methods and dictionary matching, achieving high F1 scores on chemical NER and normalization.

Text Mining Drug-Protein Interactions using an Ensemble of BERT, Sentence BERT and T5 models

The paper develops an ensemble model combining BERT, Sentence BERT, and T5 to predict drug-protein interactions from text, achieving an F-1 score of 0.753 on a benchmark dataset.

GoMi - A new gold standard corpus for miRNA Named Entity Recognition to test dictionary, rule-based and machine-learning approaches.

The paper introduces GoMi, a manually curated gold standard corpus for miRNA Named Entity Recognition, to train and test machine learning and rule-based approaches.

OnTheFly2.0: a text-mining web application for automated biomedical entity recognition, document annotation, network and functional enrichment analysis

OnTheFly2.0 automates biomedical entity recognition and analysis from documents, revealing inflammatory and senescence pathways in severe COVID-19.

Supervised learning with word embeddings derived from PubMed captures latent knowledge about protein kinases and cancer

The paper uses word embeddings from PubMed to predict which protein kinases are relevant for treating specific cancers using machine learning.

OBO Foundry in 2021: Operationalizing Open Data Principles to Evaluate Ontologies

The paper describes how the OBO Foundry formalized its principles into operational rules and implemented automated checks to evaluate ontology compliance, improving interoperability and quality.