An LLM -based Knowledge Synthesis and Scientific Reasoning Framework for Biomedical Discovery (2024)

Abstract: We present BioLunar, developed using the Lunar framework, as a tool for supporting biological analyses, with a particular emphasis on molecular-level evidence enrichment for biomarker discovery in oncology. The platform integrates Large Language Models (LLMs) to facilitate complex scientific reasoning across distributed evidence spaces, enhancing the capability for harmonizing and reasoning over heterogeneous data sources. Demonstrating its utility in cancer research, BioLunar leverages modular design, reusable data access and data analysis components, and a low-code user interface, enabling researchers of all programming levels to construct LLM-enabled scientific workflows. By facilitating automatic scientific discovery and inference from heterogeneous evidence, BioLunar exemplifies the potential of the integration between LLMs, specialised databases and biomedical tools to support expert-level knowledge synthesis and discovery.

## introduction contemporary biomedical discovery represents a prototypical instance of complex scientific reasoning, which requires the coordination of controlled in-vivo/in-silico interventions, complex multi-step data analysis pipelines and the interpretation of the results under the light of previous evidence (available in different curated databases and in the literature) @xcite . this intricacy emerges out of the inherent complexity of biological mechanisms underlying organism responses, which are defined by a network of multi-scale inter-dependencies @xcite . while more granular data is being generated by the evolution of instruments, assays and methods, and the parallel abundance of experimental interventions @xcite , there a practical barrier for integrating and cohering this evidence space into a specific context of analysis. within biomedical discovery, the language interpretation capabilities of large language models (llms) can provide an integrative framework for harmonising and reasoning over distributed evidence spaces and tools, systematising and lowering the barriers to access and reason over multiple structured databases, textual bases such as pubmed, enriching the background knowledge through specialised ontologies and serving as interfaces to external analytical tools (e.g. mechanistic/perturbation models, gene enrichment models, etc). in this context, llms can serve as a linguistic analytical layer which can reduce the syntactic impedance across diverse functional components: once an adapter to an external component is built it can be integrated and reused in different contexts, creating a monotonic increase of functional components. complementarily, from a biomedical-nlp perspective, in order to address real-world problems, llms need to be complemented with mechanisms which can deliver contextual control (e.g. via retrieval augmented generation: rag: access the relevant background knowledge and facts) and perform the analytical tasks which are integral to contemporary biomedical inference @xcite . emerging llm-focused coordination frameworks such as langchain foot_0 , flowise foot_1 and lunar foot_2 provide the capabilities to deliver a composition of functional components, some of them under a low-code/no-code use environment, using the abstraction of workflows. while there are generalpurpose coordination frameworks, there is a lack of specialised components for addressing biomedical analyses. in this paper we demonstrate biolunar, a suite of components developed over the lunar environment to support biological analyses. we demonstrate the key functionalities of the platform contextualised within a real-use case in the context of molecularlevel evidence enrichment for biomarker discovery in oncology. ## biolunar biolunar enables the creation of llm-based biomedical scientific workflows using software components with standardised apis. a workflow is composed of components and subworkflows connected through input-output relationships, and are capable of handling multiple inputs. in the user interface, components are clustered according to their function (see fig. 1 ). creating a workflow does not require programming knowledge since components are predefined and merely require data inputs or parameter settings. however, for users who wish to write custom code, 'python coder' and 'r coder' components are provided, enabling the definition of custom methods. these custom components can be saved and subsequently accessed in the 'custom' group tab. in the paper we describe an exemplar biomedical workflow designed to integrate evidence and infer conclusions from bioinformatics pipeline results. specifically, the biomedical workflow queries expert knowledge bases (kbs) that continuously compile clinical, experimental, and population genetic study outcomes, aligning them with assertions relevant to the significance of the observed gene or variant. it then employs natural language inference (nli) (via llm) to integrate and harmonise the evidence space and interpreting the results, culminating in a comprehensive summary for the entire gene set input. this interpretation takes into account the bioanalytical context supplied by the user. ## case study to demonstrate the capabilities of the bioworkflow, we analyzed outputs in two different scenarios, each producing a distinct set of genes from separate bioinformatics analyses. we entered these gene sets along with their analysis contexts into the bioworkflow and executed it. subsequently, we qualitatively assessed the output reports (see @xcite ), considering both the statistical data and the interpretations provided by the promptbased nli modules. in scenario 1, the user aims to explore the unique molecular characteristics of her2-low breast cancer to determine if it constitutes a distinct category within breast cancer types, where the input genes are erbb2, esr1, pik3ca, cbfb, sf3b. the cancer type: breast cancer sample size: 27 cancer type: breast cancer biopsy site: primary site center: informed consent: sample sent: export report analysis run date: 2024-02-24 pipeline version: v2.0. details context: breast cancer (bc) presents a significant global health challenge, with its incidence steadily rising and mortality rates remaining high. its heterogeneous nature complicates treatment strategies, contributing to issues like recurrence and drug resistance. more navigation menu gene set dashboard context toolbox individual gene description erbb2 gene gene info oncogene, more variant erbb2 l755s functional relevance evidence evidence (curated): >oncogenic, @xmath0, resistance, civic hallmark yes, more erbb2 d769y evidence (curated): >oncogenic, @xmath1, civic erbb2 r143g others/inconclusive: >inconclusive/weaker evidence, oncokb hpa: drug targets fda approved drug target, more evidence supporting the variant functional relevance current biomarker clinical relevance cancer biomarkers reported at present investigational, 1 assertions cancer repurposing, 4 assertions hypothetical, 2 assertions investigational, 1 assertions hypothetical, 1 assertions general statistics general statistics erbb2 this gene is considered an oncogene. erbb2, a receptor tyrosine kinase, is altered by mutation, amplification and/or overexpression in various cancer types, most frequently in breast, esophagogastric and endometrial cancers. more gene info gene mutations based on previous pan-cancer cohorts erbb2 l755s http://oncokb.org/#/gene/erbb2/alteration/l755s effect: oncogenic erbb2 l755s please check the original assertions provided by each knowledgebase listed below http://oncokb.org/#/gene/erbb2/alteration/oncogenic mutations biomarker: erbb2 oncogenic mutations effect: drug responsive evidence level: level_3a drug: neratinib disease: breast cancer gene with the hallmark description this gene has a hallmark. more this gene has a stimulating effect via individual cancer hallmarks. interactive pop-up windows access to external resources with the original evidence gene-detailed view 1 1 3 3 2 2 4 4 final conclusion generated by llm erbb2, commonly referred to as her2, is amplified and/or overexpressed in 20-30% of invasive breast carcinomas. her2-positive breast cancer is treated in a separate manner from other subtypes of breast cancer and commonly presents as more aggressive disease. metastatic her2-positive breast cancer is now commonly treated with her2-targeted therapy. apart from being amplified/overexpressed, erbb2 activating mutations have been shown to have clinical importance in her2-negative breast cancer. these mutations have shown sensitivity to the tyrosine kinase inhibitor neratinib, and highlight the importance of clinical sequencing efforts in treating breast cancer. erbb2 l755s was one of the first erbb2 variants to be functionally classified @xcite . this mutation was not shown to be an activating mutation, unlike many of the other variants queried. this mutation was also shown to confer resistance to the tyrosine kinase inhibitor lapatinib in mcf10a cell lines. ## conclusion in this paper we provided a demonstration of a scientific workflow based on llms to support specialised gene analyses using oncology and gene enrichment as a driving motivational scenario. the framework is built using the lunar framework and allows for the composition of specialised analytical workflows, integrating external databases (retrieval augmented generation), external tools (toolformers) and contextualised chains of llmbased interpretation. the paper highlights that a workflow environment with specialised components for rag, toolformers and a set of specialised prompts-based natural language inference can serve as the foundation for streamlining and automating complex analytical process within a biomedical setting. . we showcase analytical applications within the biomedical domain, particularly in oncology, constructively progressing towards more complex gene analysis workflows. the developed bioworkflow demonstrates the llms can be instrumental in enabling a complex endto-end highly-specialised analytical workflow, in a reproducible manner, supporting the integration of heterogeneous evidence, synthesising conclusions and while simultaneously documenting and linking to the data sources within a comprehensive output report. the proposed workflow is based on a low-code paradigm that enables domain experts, regardless of their programming skills, to construct and scientific workflows enabled by generaqtive ai amethods.