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Related Concept Videos

Language01:16

Language

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Language is a unique communication system that uses words and systematic rules to organize and transmit information. Unlike other forms of communication, which may involve postures, movements, odors, or vocalizations, language relies on symbols and grammar. This makes human communication distinct from that of other species, who also communicate but do not use language in the same way humans do.
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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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The fineness of cement directly influences the rate of hydration, as the hydration begins at the surface of the cement particles. In addition to hydration, the fineness of cement is vital for various properties of concrete including workability, gypsum requirement, and long-term behavior. The fineness of cement is represented in terms of the specific surface of cement which is typically measured in square meters per kilogram, with several methods available for this determination.
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Fineness Modulus01:19

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The fineness modulus (FM) of aggregate is a numerical index that measures the coarseness or fineness of the particles. It is calculated by adding the cumulative percentages of aggregate retained on each of a specified series of sieves and dividing the sum by 100.
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Language, whether spoken, signed, or written, consists of specific components: lexicon and grammar. The lexicon is the vocabulary of a language, comprising its words. Grammar is the set of rules used to convey meaning through the lexicon. For example, English grammar adds “-ed” to most verbs to indicate past tense. Words are formed by combining phonemes, which are the basic sound units of a language. Different languages have different sets of phonemes (e.g., “ah” vs.
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Language Development01:22

Language Development

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Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
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BioTriplex: a full-text annotated corpus for fine-tuning language models in gene-disease relation extraction tasks.

Charlotte Collins1,2, Panagiotis Fytas1, İlknur Karadeniz1,3

  • 1Language Technology Laboratory, Theoretical and Applied Linguistics, Faculty of Modern and Medieval Languages and Linguistics, University of Cambridge, Cambridge CB3 9DA, United Kingdom.

Bioinformatics (Oxford, England)
|January 21, 2026
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Summary
This summary is machine-generated.

We created BioTriplex, a new dataset for training AI models on gene-disease relationships. Fine-tuned AI models using BioTriplex show improved accuracy in extracting complex gene-disease connections from biomedical texts.

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Area of Science:

  • Biomedical informatics
  • Computational biology
  • Natural Language Processing

Background:

  • Machine learning for biomedical text analysis requires specialized models due to technical terminology and semantic variations.
  • Existing large language models (LLMs) struggle with the nuances of biomedical literature.
  • There is a need for annotated full-text datasets to fine-tune LLMs for specific biomedical applications, particularly gene-disease relationship extraction.

Purpose of the Study:

  • To introduce BioTriplex, a novel annotated corpus of full-length biomedical research articles.
  • To develop and evaluate a fine-tuned language model for extracting gene-disease relationships.
  • To demonstrate the effectiveness of specialized datasets in improving LLM performance for biomedical tasks.

Main Methods:

  • Developed BioTriplex, a corpus of 100 full-length biomedical articles manually annotated with diseases, genes, and 21 subtypes of gene-disease relationships.
  • Utilized BioTriplex to fine-tune the LLaMA 3.1 8B language model for gene-disease relation extraction.
  • Compared the performance of the fine-tuned model against zero-shot and few-shot approaches within the same architecture and against other state-of-the-art LLMs (GPT-4, Claude Sonnet 3.7).

Main Results:

  • The BioTriplex-trained LLaMA 3.1 8B model significantly outperformed baseline and other advanced LLMs in gene-disease relation extraction.
  • The fine-tuned model demonstrated superior accuracy and a broader, more granular classification of gene-disease relationship types.
  • BioTriplex proved to be a valuable resource for enhancing LLM capabilities in biomedical information extraction.

Conclusions:

  • BioTriplex is an effective resource for fine-tuning language models for biomedical applications.
  • Specialized datasets are crucial for improving the performance of LLMs in complex scientific domains like gene-disease relationship extraction.
  • The fine-tuned LLaMA 3.1 8B model represents a significant advancement in automated extraction of gene-disease relationships.