PDF(Pubmed)
Abstract：
Feature selection is a critical step for translating advances afforded by systems-scale molecular profiling into actionable clinical insights. While data-driven methods are commonly utilized for selecting candidate genes, knowledge-driven methods must contend with the challenge of efficiently sifting through extensive volumes of biomedical information. This work aimed to assess the utility of large language models (LLMs) for knowledge-driven gene prioritization and selection.
In this proof of concept, we focused on 11 blood transcriptional modules associated with an Erythroid cells signature. We evaluated four leading LLMs across multiple tasks. Next, we established a workflow leveraging LLMs. The steps consisted of: (1) Selecting one of the 11 modules; (2) Identifying functional convergences among constituent genes using the LLMs; (3) Scoring candidate genes across six criteria capturing the gene\'s biological and clinical relevance; (4) Prioritizing candidate genes and summarizing justifications; (5) Fact-checking justifications and identifying supporting references; (6) Selecting a top candidate gene based on validated scoring justifications; and (7) Factoring in transcriptome profiling data to finalize the selection of the top candidate gene.
Of the four LLMs evaluated, OpenAI\'s GPT-4 and Anthropic\'s Claude demonstrated the best performance and were chosen for the implementation of the candidate gene prioritization and selection workflow. This workflow was run in parallel for each of the 11 erythroid cell modules by participants in a data mining workshop. Module M9.2 served as an illustrative use case. The 30 candidate genes forming this module were assessed, and the top five scoring genes were identified as BCL2L1, ALAS2, SLC4A1, CA1, and FECH. Researchers carefully fact-checked the summarized scoring justifications, after which the LLMs were prompted to select a top candidate based on this information. GPT-4 initially chose BCL2L1, while Claude selected ALAS2. When transcriptional profiling data from three reference datasets were provided for additional context, GPT-4 revised its initial choice to ALAS2, whereas Claude reaffirmed its original selection for this module.
Taken together, our findings highlight the ability of LLMs to prioritize candidate genes with minimal human intervention. This suggests the potential of this technology to boost productivity, especially for tasks that require leveraging extensive biomedical knowledge.
In this proof of concept, we focused on 11 blood transcriptional modules associated with an Erythroid cells signature. We evaluated four leading LLMs across multiple tasks. Next, we established a workflow leveraging LLMs. The steps consisted of: (1) Selecting one of the 11 modules; (2) Identifying functional convergences among constituent genes using the LLMs; (3) Scoring candidate genes across six criteria capturing the gene\'s biological and clinical relevance; (4) Prioritizing candidate genes and summarizing justifications; (5) Fact-checking justifications and identifying supporting references; (6) Selecting a top candidate gene based on validated scoring justifications; and (7) Factoring in transcriptome profiling data to finalize the selection of the top candidate gene.
Of the four LLMs evaluated, OpenAI\'s GPT-4 and Anthropic\'s Claude demonstrated the best performance and were chosen for the implementation of the candidate gene prioritization and selection workflow. This workflow was run in parallel for each of the 11 erythroid cell modules by participants in a data mining workshop. Module M9.2 served as an illustrative use case. The 30 candidate genes forming this module were assessed, and the top five scoring genes were identified as BCL2L1, ALAS2, SLC4A1, CA1, and FECH. Researchers carefully fact-checked the summarized scoring justifications, after which the LLMs were prompted to select a top candidate based on this information. GPT-4 initially chose BCL2L1, while Claude selected ALAS2. When transcriptional profiling data from three reference datasets were provided for additional context, GPT-4 revised its initial choice to ALAS2, whereas Claude reaffirmed its original selection for this module.
Taken together, our findings highlight the ability of LLMs to prioritize candidate genes with minimal human intervention. This suggests the potential of this technology to boost productivity, especially for tasks that require leveraging extensive biomedical knowledge.
摘要：
背景：特征选择是将系统规模分子谱分析提供的进展转化为可操作的临床见解的关键步骤。虽然数据驱动方法通常用于选择候选基因，知识驱动的方法必须应对有效筛选大量生物医学信息的挑战。这项工作旨在评估大型语言模型（LLM）在知识驱动的基因优先级排序和选择中的实用性。
方法：在这个概念证明中，我们关注与红系细胞特征相关的11个血液转录模块。我们在多个任务中评估了四个领先的LLM。接下来,我们建立了一个利用LLM的工作流程。步骤包括:(1)选择11个模块中的一个；(2)使用LLM识别组成基因之间的功能融合；(3)在六个标准中对候选基因进行评分，以捕获该基因的生物学和临床相关性；(4)对候选基因进行优先排序并总结理由；(5)事实检查理由并确定支持基因分析；(6)在经过验证的候选基因筛选中选择中
结果：在评估的四个LLM中，OpenAI的GPT-4和Anthropic的Claude表现出最佳性能，并被选择用于实施候选基因优先级排序和选择工作流程。数据挖掘研讨会的参与者对11个红系细胞模块中的每一个并行运行该工作流程。模块M9.2用作说明性用例。评估了形成该模块的30个候选基因，得分最高的5个基因分别为BCL2L1、ALAS2、SLC4A1、CA1和FECH。研究人员仔细检查了总结的评分理由，之后，系统会提示LLM根据此信息选择最高的候选人。GPT-4最初选择BCL2L1,而Claude选择ALAS2。当提供来自三个参考数据集的转录分析数据用于其他上下文时，GPT-4将其最初的选择修改为ALAS2，而克劳德重申了该模块的原始选择。
结论：综合来看，我们的研究结果强调了LLM在最少的人为干预下优先考虑候选基因的能力.这表明该技术具有提高生产率的潜力，特别是对于需要利用广泛的生物医学知识的任务。
方法：在这个概念证明中，我们关注与红系细胞特征相关的11个血液转录模块。我们在多个任务中评估了四个领先的LLM。接下来,我们建立了一个利用LLM的工作流程。步骤包括:(1)选择11个模块中的一个；(2)使用LLM识别组成基因之间的功能融合；(3)在六个标准中对候选基因进行评分，以捕获该基因的生物学和临床相关性；(4)对候选基因进行优先排序并总结理由；(5)事实检查理由并确定支持基因分析；(6)在经过验证的候选基因筛选中选择中
结果：在评估的四个LLM中，OpenAI的GPT-4和Anthropic的Claude表现出最佳性能，并被选择用于实施候选基因优先级排序和选择工作流程。数据挖掘研讨会的参与者对11个红系细胞模块中的每一个并行运行该工作流程。模块M9.2用作说明性用例。评估了形成该模块的30个候选基因，得分最高的5个基因分别为BCL2L1、ALAS2、SLC4A1、CA1和FECH。研究人员仔细检查了总结的评分理由，之后，系统会提示LLM根据此信息选择最高的候选人。GPT-4最初选择BCL2L1,而Claude选择ALAS2。当提供来自三个参考数据集的转录分析数据用于其他上下文时，GPT-4将其最初的选择修改为ALAS2，而克劳德重申了该模块的原始选择。
结论：综合来看，我们的研究结果强调了LLM在最少的人为干预下优先考虑候选基因的能力.这表明该技术具有提高生产率的潜力，特别是对于需要利用广泛的生物医学知识的任务。
