# Biological Sequence with Language Model Prompting: A Survey

arXiv: https://arxiv.org/abs/2503.04135 · HTML: https://mlsanity.com/paper/2503.04135/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 39 references that are in the map.
- First posted on arXiv 6 Mar 2025.

## Builds on (most cited first, 16 of 16)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Data-centric Artificial Intelligence: A Survey](https://mlsanity.com/paper/2303.10158/) (2023)
- [The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning](https://mlsanity.com/paper/2205.03401/) (2022)
- [InstructProtein: Aligning Human and Protein Language via Knowledge Instruction](https://mlsanity.com/paper/2310.03269/) (2023)
- [Dynamic and Adaptive Feature Generation with LLM](https://mlsanity.com/paper/2406.03505/) (2024)
- [PRoLoRA: Partial Rotation Empowers More Parameter-Efficient LoRA](https://mlsanity.com/paper/2402.16902/) (2024)
- [Simplifying Reinforced Feature Selection via Restructured Choice Strategy of Single Agent](https://mlsanity.com/paper/2009.09230/) (2020)
- [Reinforcement Feature Transformation for Polymer Property Performance Prediction](https://mlsanity.com/paper/2409.15616/) (2024)
- [Protein Multimer Structure Prediction via Prompt Learning](https://mlsanity.com/paper/2402.18813/) (2024)
- [SSM-DTA: Breaking the Barriers of Data Scarcity in Drug-Target Affinity Prediction](https://mlsanity.com/paper/2206.09818/) (2022)
- [MoS: Unleashing Parameter Efficiency of Low-Rank Adaptation with Mixture of Shards](https://mlsanity.com/paper/2410.00938/) (2024)
- [Genomics-guided Representation Learning for Pathologic Pan-cancer Tumor Microenvironment Subtype Prediction](https://mlsanity.com/paper/2406.06517/) (2024)
- [Prompt-Guided Injection of Conformation to Pre-trained Protein Model](https://mlsanity.com/paper/2202.02944/) (2022)

## Built on by (most cited first, 2 of 2)

- [A Principle-Driven Adaptive Policy for Group Cognitive Stimulation Dialogue for Elderly with Cognitive Impairment](https://mlsanity.com/paper/2603.10034/) (2026)
- [DS-ProGen: A Dual-Structure Deep Language Model for Functional Protein Design](https://mlsanity.com/paper/2505.12511/) (2025)

## Abstract (arXiv)

Large Language models (LLMs) have emerged as powerful tools for addressing challenges across diverse domains. Notably, recent studies have demonstrated that large language models significantly enhance the efficiency of biomolecular analysis and synthesis, attracting widespread attention from academics and medicine. In this paper, we systematically investigate the application of prompt-based methods with LLMs to biological sequences, including DNA, RNA, proteins, and drug discovery tasks. Specifically, we focus on how prompt engineering enables LLMs to tackle domain-specific problems, such as promoter sequence prediction, protein structure modeling, and drug-target binding affinity prediction, often with limited labeled data. Furthermore, our discussion highlights the transformative potential of prompting in bioinformatics while addressing key challenges such as data scarcity, multimodal fusion, and computational resource limitations. Our aim is for this paper to function both as a foundational primer for newcomers and a catalyst for continued innovation within this dynamic field of study.