Dataset Information

Biomedical generative pre-trained based transformer language model for age-related disease target discovery.

ABSTRACT: Target discovery is crucial for the development of innovative therapeutics and diagnostics. However, current approaches often face limitations in efficiency, specificity, and scalability, necessitating the exploration of novel strategies for identifying and validating disease-relevant targets. Advances in natural language processing have provided new avenues for predicting potential therapeutic targets for various diseases. Here, we present a novel approach for predicting therapeutic targets using a large language model (LLM). We trained a domain-specific BioGPT model on a large corpus of biomedical literature consisting of grant text and developed a pipeline for generating target prediction. Our study demonstrates that pre-training of the LLM model with task-specific texts improves its performance. Applying the developed pipeline, we retrieved prospective aging and age-related disease targets and showed that these proteins are in correspondence with the database data. Moreover, we propose CCR5 and PTH as potential novel dual-purpose anti-aging and disease targets which were not previously identified as age-related but were highly ranked in our approach. Overall, our work highlights the high potential of transformer models in novel target prediction and provides a roadmap for future integration of AI approaches for addressing the intricate challenges presented in the biomedical field.

SUBMITTER: Zagirova D

PROVIDER: S-EPMC10564439 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Biomedical generative pre-trained based transformer language model for age-related disease target discovery.

Zagirova Diana D Pushkov Stefan S Leung Geoffrey Ho Duen GHD Liu Bonnie Hei Man BHM Urban Anatoly A Sidorenko Denis D Kalashnikov Aleksandr A Kozlova Ekaterina E Naumov Vladimir V Pun Frank W FW Ozerov Ivan V IV Aliper Alex A Zhavoronkov Alex A

Aging 20230922 18

Target discovery is crucial for the development of innovative therapeutics and diagnostics. However, current approaches often face limitations in efficiency, specificity, and scalability, necessitating the exploration of novel strategies for identifying and validating disease-relevant targets. Advances in natural language processing have provided new avenues for predicting potential therapeutic targets for various diseases. Here, we present a novel approach for predicting therapeutic targets usi ...[more]

PMID: 37742294

Similar Datasets

Project description:Purpose: Processing and analyzing clinical texts are challenging due to its unstructured nature. This study compared the performance of GPT (Generative Pre-trained Transformer)-3.5 and GPT-4 for extracting information from clinical text. Materials and Methods: Three types of clinical texts, containing patient characteristics, medical history, and clinical test results extracted from case reports in open-access journals were utilized as input. Simple prompts containing queries for information extraction were then applied to both models using the Greedy Approach as the decoding strategy. When GPT models underperformed in certain tasks, we applied alternative decoding strategies or incorporated prompts with task-specific definitions. The outputs generated by GPT models were evaluated as True or False to determine the accuracy of information extraction. Results: Clinical texts containing patient characteristics (60 texts), medical history (50 texts), and clinical test results (25 texts) were extracted from 60 case reports. GPT models could extract information accurately with simple prompts to extract straightforward information from clinical texts. Regarding sex, GPT-4 demonstrated a significantly higher accuracy rate (95%) compared to GPT-3.5 (70%). GPT-3.5 (78%) outperformed GPT-4 (57%) in extracting body mass index (BMI). Utilizing alternative decoding strategies to sex and BMI did not practically improve the performance of the two models. In GPT-4, the revised prompts, including definitions of each sex category or the BMI formula, rectified all incorrect responses regarding sex and BMI generated during the main workflow. Conclusion: GPT models could perform adequately with simple prompts for extracting straightforward information. For complex tasks, incorporating task-specific definitions into the prompts is a suitable strategy than relying solely on simple prompts. Therefore, researchers and clinicians should use their expertise to create effective prompts and monitor LLM outcomes when extracting complex information from clinical texts.

Project description:BackgroundPatients are increasingly using Generative Pre-trained Transformer 4 (GPT-4) to better understand their own radiology findings.PurposeTo evaluate the performance of GPT-4 in transforming cardiovascular magnetic resonance (CMR) reports into text that is comprehensible to medical laypersons.MethodsChatGPT with GPT-4 architecture was used to generate three different explained versions of 20 various CMR reports (n = 60) using the same prompt: "Explain the radiology report in a language understandable to a medical layperson". Two cardiovascular radiologists evaluated understandability, factual correctness, completeness of relevant findings, and lack of potential harm, while 13 medical laypersons evaluated the understandability of the original and the GPT-4 reports on a Likert scale (1 "strongly disagree", 5 "strongly agree"). Readability was measured using the Automated Readability Index (ARI). Linear mixed-effects models (values given as median [interquartile range]) and intraclass correlation coefficient (ICC) were used for statistical analysis.ResultsGPT-4 reports were generated on average in 52 s ± 13. GPT-4 reports achieved a lower ARI score (10 [9-12] vs 5 [4-6]; p < 0.001) and were subjectively easier to understand for laypersons than original reports (1 [1] vs 4 [4,5]; p < 0.001). Eighteen out of 20 (90%) standard CMR reports and 2/60 (3%) GPT-generated reports had an ARI score corresponding to the 8th grade level or higher. Radiologists' ratings of the GPT-4 reports reached high levels for correctness (5 [4, 5]), completeness (5 [5]), and lack of potential harm (5 [5]); with "strong agreement" for factual correctness in 94% (113/120) and completeness of relevant findings in 81% (97/120) of reports. Test-retest agreement for layperson understandability ratings between the three simplified reports generated from the same original report was substantial (ICC: 0.62; p < 0.001). Interrater agreement between radiologists was almost perfect for lack of potential harm (ICC: 0.93, p < 0.001) and moderate to substantial for completeness (ICC: 0.76, p < 0.001) and factual correctness (ICC: 0.55, p < 0.001).ConclusionGPT-4 can reliably transform complex CMR reports into more understandable, layperson-friendly language while largely maintaining factual correctness and completeness, and can thus help convey patient-relevant radiology information in an easy-to-understand manner.

Project description:PurposeTo evaluate the capabilities of Chat Generative Pre-Trained Transformer (ChatGPT), as a large language model (LLM), for diagnosing glaucoma using the Ocular Hypertension Treatment Study (OHTS) dataset, and comparing the diagnostic capability of ChatGPT 3.5 and ChatGPT 4.0.DesignProspective data collection study.ParticipantsA total of 3170 eyes of 1585 subjects from the OHTS were included in this study.MethodsWe selected demographic, clinical, ocular, visual field, optic nerve head photo, and history of disease parameters of each participant and developed case reports by converting tabular data into textual format based on information from both eyes of all subjects. We then developed a procedure using the application programming interface of ChatGPT, a LLM-based chatbot, to automatically input prompts into a chat box. This was followed by querying 2 different generations of ChatGPT (versions 3.5 and 4.0) regarding the underlying diagnosis of each subject. We then evaluated the output responses based on several objective metrics.Main outcome measuresArea under the receiver operating characteristic curve (AUC), accuracy, specificity, sensitivity, and F1 score.ResultsChat Generative Pre-Trained Transformer 3.5 achieved AUC of 0.74, accuracy of 66%, specificity of 64%, sensitivity of 85%, and F1 score of 0.72. Chat Generative Pre-Trained Transformer 4.0 obtained AUC of 0.76, accuracy of 87%, specificity of 90%, sensitivity of 61%, and F1 score of 0.92.ConclusionsThe accuracy of ChatGPT 4.0 in diagnosing glaucoma based on input data from OHTS was promising. The overall accuracy of ChatGPT 4.0 was higher than ChatGPT 3.5. However, ChatGPT 3.5 was found to be more sensitive than ChatGPT 4.0. In its current forms, ChatGPT may serve as a useful tool in exploring disease status of ocular hypertensive eyes when specific data are available for analysis. In the future, leveraging LLMs with multimodal capabilities, allowing for integration of imaging and diagnostic testing as part of the analyses, could further enhance diagnostic capabilities and enhance diagnostic accuracy.Financial disclosuresProprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Dataset Information

Biomedical generative pre-trained based transformer language model for age-related disease target discovery.

Publications

Biomedical generative pre-trained based transformer language model for age-related disease target discovery.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets