ArticleBMC bioinformatics2011
Cross-species gene normalization by species inference.
Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
48 citing papers in PubMed.
- AI-based mining of biomedical literature: Applications for drug repurposing for the treatment of dementia.Artificial intelligence in medicine · 2025Article
- An Accurate and Efficient Approach to Knowledge Extraction from Scientific Publications Using Structured Ontology Models, Graph Neural Networks, and Large Language Models.International journal of molecular sciences · 2024Article
- HunFlair2 in a cross-corpus evaluation of biomedical named entity recognition and normalization tools.Bioinformatics (Oxford, England) · 2024Article
- Biomedical literature mining: graph kernel-based learning for gene-gene interaction extraction.European journal of medical research · 2024Article
- BELHD: improving biomedical entity linking with homonym disambiguation.Bioinformatics (Oxford, England) · 2024Article
- GNorm2: an improved gene name recognition and normalization system.Bioinformatics (Oxford, England) · 2023Article
- Mutational signatures for breast cancer diagnosis using artificial intelligence.Journal of the Egyptian National Cancer Institute · 2023Article
- Edge Weight Updating Neural Network for Named Entity Normalization.Neural processing letters · 2022Article
- Joint Learning for Biomedical NER and Entity Normalization: Encoding Schemes, Counterfactual Examples, and Zero-Shot Evaluation.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2021Article
- NLM-Gene, a richly annotated gold standard dataset for gene entities that addresses ambiguity and multi-species gene recognition.Journal of biomedical informatics · 2021Article
- Recent advances of automated methods for searching and extracting genomic variant information from biomedical literature.Briefings in bioinformatics · 2021Review
- PubTator central: automated concept annotation for biomedical full text articles.Nucleic acids research · 2019Article
- Linking entities through an ontology using word embeddings and syntactic re-ranking.BMC bioinformatics · 2019Article
- DES-Mutation: System for Exploring Links of Mutations and Diseases.Scientific reports · 2018Article
- A gene-phenotype relationship extraction pipeline from the biomedical literature using a representation learning approach.Bioinformatics (Oxford, England) · 2018Article
- ParaBTM: A Parallel Processing Framework for Biomedical Text Mining on Supercomputers.Molecules (Basel, Switzerland) · 2018Article
- A method for named entity normalization in biomedical articles: application to diseases and plants.BMC bioinformatics · 2017Article
- Recent advances in predicting gene-disease associations.F1000Research · 2017Review
- iPTMnet: Integrative Bioinformatics for Studying PTM Networks.Methods in molecular biology (Clifton, N.J.) · 2017Article
- eGenPub, a text mining system for extending computationally mapped bibliography for UniProt Knowledgebase by capturing centrality.Database : the journal of biological databases and curation · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo access and utilize the rich information contained in the biomedical literature, the ability to recognize and normalize gene mentions referenced in the literature is crucial. In this paper, we focus on improvements to the accuracy of gene normalization in cases where species information is not provided. Gene names are often ambiguous, in that they can refer to the genes of many species. Therefore, gene normalization is a difficult challenge.
methodsWe define "gene normalization" as a series of tasks involving several issues, including gene name recognition, species assignation and species-specific gene normalization. We propose an integrated method, GenNorm, consisting of three modules to handle the issues of this task. Every issue can affect overall performance, though the most important is species assignation. Clearly, correct identification of the species can decrease the ambiguity of orthologous genes.
resultsIn experiments, the proposed model attained the top-1 threshold average precision (TAP-k) scores of 0.3297 (k=5), 0.3538 (k=10), and 0.3535 (k=20) when tested against 50 articles that had been selected for their difficulty and the most divergent results from pooled team submissions. In the silver-standard-507 evaluation, our TAP-k scores are 0.4591 for k=5, 10, and 20 and were ranked 2nd, 2nd, and 3rd respectively. AVAILABILITY: A web service and input, output formats of GenNorm are available at http://ikmbio.csie.ncku.edu.tw/GN/.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.