Biology Instructions: A Dataset and Benchmark for Multi-Omics Sequence Understanding Capability of Large Language Models

• URL: http://arxiv.org/abs/2412.19191v1
• Date: Thu, 26 Dec 2024 12:12:23 GMT
• Title: Biology Instructions: A Dataset and Benchmark for Multi-Omics Sequence Understanding Capability of Large Language Models
• Authors: Haonan He, Yuchen Ren, Yining Tang, Ziyang Xu, Junxian Li, Minghao Yang, Di Zhang, Dong Yuan, Tao Chen, Shufei Zhang, Yuqiang Li, Nanqing Dong, Wanli Ouyang, Dongzhan Zhou, Peng Ye,
• Abstract summary: We introduce Biology-Instructions, the first large-scale multi-omics biological sequences-related instruction-tuning dataset. This dataset can bridge the gap between large language models (LLMs) and complex biological sequences-related tasks. We also develop a strong baseline called ChatMultiOmics with a novel three-stage training pipeline.
• Score: 51.316001071698224
• License:
• Abstract: Large language models have already demonstrated their formidable capabilities in general domains, ushering in a revolutionary transformation. However, exploring and exploiting the extensive knowledge of these models to comprehend multi-omics biology remains underexplored. To fill this research gap, we first introduce Biology-Instructions, the first large-scale multi-omics biological sequences-related instruction-tuning dataset including DNA, RNA, proteins, and multi-molecules, designed to bridge the gap between large language models (LLMs) and complex biological sequences-related tasks. This dataset can enhance the versatility of LLMs by integrating diverse biological sequenced-based prediction tasks with advanced reasoning capabilities, while maintaining conversational fluency. Additionally, we reveal significant performance limitations in even state-of-the-art LLMs on biological sequence-related multi-omics tasks without specialized pre-training and instruction-tuning. We further develop a strong baseline called ChatMultiOmics with a novel three-stage training pipeline, demonstrating the powerful ability to understand biology by using Biology-Instructions. Biology-Instructions and ChatMultiOmics are publicly available and crucial resources for enabling more effective integration of LLMs with multi-omics sequence analysis.

Related papers

• Life-Code: Central Dogma Modeling with Multi-Omics Sequence Unification [53.488387420073536]
  Life-Code is a comprehensive framework that spans different biological functions. Life-Code achieves state-of-the-art performance on various tasks across three omics.
  arXiv  Detail & Related papers  (2025-02-11T06:53:59Z)
• GENERator: A Long-Context Generative Genomic Foundation Model [66.46537421135996]
  We present a generative genomic foundation model featuring a context length of 98k base pairs (bp) and 1.2B parameters. The model adheres to the central dogma of molecular biology, accurately generating protein-coding sequences. It also shows significant promise in sequence optimization, particularly through the prompt-responsive generation of promoter sequences.
  arXiv  Detail & Related papers  (2025-02-11T05:39:49Z)
• COMET: Benchmark for Comprehensive Biological Multi-omics Evaluation Tasks and Language Models [56.81513758682858]
  COMET aims to evaluate models across single-omics, cross-omics, and multi-omics tasks. First, we curate and develop a diverse collection of downstream tasks and datasets covering key structural and functional aspects in DNA, RNA, and proteins. Then, we evaluate existing foundational language models for DNA, RNA, and proteins, as well as the newly proposed multi-omics method.
  arXiv  Detail & Related papers  (2024-12-13T18:42:00Z)
• BSM: Small but Powerful Biological Sequence Model for Genes and Proteins [6.6055625629542085]
  We introduce BSM, a small but powerful mixed-modal biological sequence foundation model. It is trained on three types of data: RefSeq, Gene Related Sequences, and interleaved biological sequences from the web. It significantly enhances learning efficiency and cross-modal representation, outperforming models trained solely on unimodal data.
  arXiv  Detail & Related papers  (2024-10-15T11:12:28Z)
• Multimodal Large Language Models for Bioimage Analysis [39.120941702559726]
  Multimodal Large Language Models (MLLMs) exhibit strong emergent capacities, such as understanding, analyzing, reasoning, and generalization. With these capabilities, MLLMs hold promise to extract intricate information from biological images and data obtained through various modalities. Development of MLLMs shows increasing promise in serving as intelligent assistants or agents for augmenting human researchers in biology research.
  arXiv  Detail & Related papers  (2024-07-29T08:21:25Z)
• Genomic Language Models: Opportunities and Challenges [0.2912705470788796]
  Genomic Language Models (gLMs) have the potential to significantly advance our understanding of genomes. We highlight key applications of gLMs, including functional constraint prediction, sequence design, and transfer learning. We discuss major considerations for developing and evaluating gLMs.
  arXiv  Detail & Related papers  (2024-07-16T06:57:35Z)
• An Evaluation of Large Language Models in Bioinformatics Research [52.100233156012756]
  We study the performance of large language models (LLMs) on a wide spectrum of crucial bioinformatics tasks. These tasks include the identification of potential coding regions, extraction of named entities for genes and proteins, detection of antimicrobial and anti-cancer peptides, molecular optimization, and resolution of educational bioinformatics problems. Our findings indicate that, given appropriate prompts, LLMs like GPT variants can successfully handle most of these tasks.
  arXiv  Detail & Related papers  (2024-02-21T11:27:31Z)
• ProBio: A Protocol-guided Multimodal Dataset for Molecular Biology Lab [67.24684071577211]
  The challenge of replicating research results has posed a significant impediment to the field of molecular biology. We first curate a comprehensive multimodal dataset, named ProBio, as an initial step towards this objective. Next, we devise two challenging benchmarks, transparent solution tracking and multimodal action recognition, to emphasize the unique characteristics and difficulties associated with activity understanding in BioLab settings.
  arXiv  Detail & Related papers  (2023-11-01T14:44:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.