Language Model Powered Digital Biology with BRAD
Fuente:
arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866913600444039168 |
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| author | Pickard, Joshua Prakash, Ram Choi, Marc Andrew Oliven, Natalie Stansbury, Cooper Cwycyshyn, Jillian Gorodetsky, Alex Velasquez, Alvaro Rajapakse, Indika |
| author_facet | Pickard, Joshua Prakash, Ram Choi, Marc Andrew Oliven, Natalie Stansbury, Cooper Cwycyshyn, Jillian Gorodetsky, Alex Velasquez, Alvaro Rajapakse, Indika |
| contents | Recent advancements in Large Language Models (LLMs) are transforming biology, computer science, engineering, and every day life. However, integrating the wide array of computational tools, databases, and scientific literature continues to pose a challenge to biological research. LLMs are well-suited for unstructured integration, efficient information retrieval, and automating standard workflows and actions from these diverse resources. To harness these capabilities in bioinformatics, we present a prototype Bioinformatics Retrieval Augmented Digital assistant (BRAD). BRAD is a chatbot and agentic system that integrates a variety of bioinformatics tools. The Python package implements an AI \texttt{Agent} that is powered by LLMs and connects to a local file system, online databases, and a user's software. The \texttt{Agent} is highly configurable, enabling tasks such as Retrieval-Augmented Generation, searches across bioinformatics databases, and the execution of software pipelines. BRAD's coordinated integration of bioinformatics tools delivers a context-aware and semi-autonomous system that extends beyond the capabilities of conventional LLM-based chatbots. A graphical user interface (GUI) provides an intuitive interface to the system. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_02864 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Language Model Powered Digital Biology with BRAD Pickard, Joshua Prakash, Ram Choi, Marc Andrew Oliven, Natalie Stansbury, Cooper Cwycyshyn, Jillian Gorodetsky, Alex Velasquez, Alvaro Rajapakse, Indika Artificial Intelligence Information Retrieval Software Engineering Recent advancements in Large Language Models (LLMs) are transforming biology, computer science, engineering, and every day life. However, integrating the wide array of computational tools, databases, and scientific literature continues to pose a challenge to biological research. LLMs are well-suited for unstructured integration, efficient information retrieval, and automating standard workflows and actions from these diverse resources. To harness these capabilities in bioinformatics, we present a prototype Bioinformatics Retrieval Augmented Digital assistant (BRAD). BRAD is a chatbot and agentic system that integrates a variety of bioinformatics tools. The Python package implements an AI \texttt{Agent} that is powered by LLMs and connects to a local file system, online databases, and a user's software. The \texttt{Agent} is highly configurable, enabling tasks such as Retrieval-Augmented Generation, searches across bioinformatics databases, and the execution of software pipelines. BRAD's coordinated integration of bioinformatics tools delivers a context-aware and semi-autonomous system that extends beyond the capabilities of conventional LLM-based chatbots. A graphical user interface (GUI) provides an intuitive interface to the system. |
| title | Language Model Powered Digital Biology with BRAD |
| topic | Artificial Intelligence Information Retrieval Software Engineering |
| url | https://arxiv.org/abs/2409.02864 |