VQDNA: Unleashing the Power of Vector Quantization for Multi-Species Genomic Sequence Modeling

• URL: http://arxiv.org/abs/2405.10812v2
• Date: Sun, 2 Jun 2024 17:50:46 GMT
• Title: VQDNA: Unleashing the Power of Vector Quantization for Multi-Species Genomic Sequence Modeling
• Authors: Siyuan Li, Zedong Wang, Zicheng Liu, Di Wu, Cheng Tan, Jiangbin Zheng, Yufei Huang, Stan Z. Li,
• Abstract summary: VQDNA is a general-purpose framework that renovates genome tokenization from the perspective of genome vocabulary learning. By leveraging vector-quantized codebooks as learnable vocabulary, VQDNA can adaptively tokenize genomes into pattern-aware embeddings.
• Score: 60.91599380893732
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Similar to natural language models, pre-trained genome language models are proposed to capture the underlying intricacies within genomes with unsupervised sequence modeling. They have become essential tools for researchers and practitioners in biology. However, the hand-crafted tokenization policies used in these models may not encode the most discriminative patterns from the limited vocabulary of genomic data. In this paper, we introduce VQDNA, a general-purpose framework that renovates genome tokenization from the perspective of genome vocabulary learning. By leveraging vector-quantized codebooks as learnable vocabulary, VQDNA can adaptively tokenize genomes into pattern-aware embeddings in an end-to-end manner. To further push its limits, we propose Hierarchical Residual Quantization (HRQ), where varying scales of codebooks are designed in a hierarchy to enrich the genome vocabulary in a coarse-to-fine manner. Extensive experiments on 32 genome datasets demonstrate VQDNA's superiority and favorable parameter efficiency compared to existing genome language models. Notably, empirical analysis of SARS-CoV-2 mutations reveals the fine-grained pattern awareness and biological significance of learned HRQ vocabulary, highlighting its untapped potential for broader applications in genomics.

Related papers

• Long-range gene expression prediction with token alignment of large language model [37.10820914895689]
  We introduce Genetic sequence Token Alignment (GTA), which aligns genetic sequence features with natural language tokens. GTA learns the regulatory grammar and allows us to further incorporate gene-specific human annotations as prompts. GTA represents a powerful and novel cross-modal approach to gene expression prediction by utilizing a pretrained language model.
  arXiv  Detail & Related papers  (2024-10-02T02:42:29Z)
• Predicting Genetic Mutation from Whole Slide Images via Biomedical-Linguistic Knowledge Enhanced Multi-label Classification [119.13058298388101]
  We develop a Biological-knowledge enhanced PathGenomic multi-label Transformer to improve genetic mutation prediction performances. BPGT first establishes a novel gene encoder that constructs gene priors by two carefully designed modules. BPGT then designs a label decoder that finally performs genetic mutation prediction by two tailored modules.
  arXiv  Detail & Related papers  (2024-06-05T06:42:27Z)
• Efficient and Scalable Fine-Tune of Language Models for Genome Understanding [49.606093223945734]
  We present textscLingo: textscLanguage prefix ftextscIne-tuning for textscGentextscOmes. Unlike DNA foundation models, textscLingo strategically leverages natural language foundation models' contextual cues. textscLingo further accommodates numerous downstream fine-tune tasks by an adaptive rank sampling method.
  arXiv  Detail & Related papers  (2024-02-12T21:40:45Z)
• BEND: Benchmarking DNA Language Models on biologically meaningful tasks [7.005668635562045]
  We introduce BEND, a Benchmark for DNA language models, featuring a collection of realistic and biologically meaningful downstream tasks. We find that embeddings from current DNA LMs can approach performance of expert methods on some tasks, but only capture limited information about long-range features.
  arXiv  Detail & Related papers  (2023-11-21T12:34:00Z)
• HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution [76.97231739317259]
  We present HyenaDNA, a genomic foundation model pretrained on the human reference genome with context lengths of up to 1 million tokens at the single nucleotide-level. On fine-tuned benchmarks from the Nucleotide Transformer, HyenaDNA reaches state-of-the-art (SotA) on 12 of 18 datasets using a model with orders of magnitude less parameters and pretraining data.
  arXiv  Detail & Related papers  (2023-06-27T20:46:34Z)
• Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review [77.34726150561087]
  Cancer is the second major cause of death after cardiovascular diseases. Gene expression can play a fundamental role in the early detection of cancer. This study reviews recent progress in gene expression analysis for cancer classification using machine learning methods.
  arXiv  Detail & Related papers  (2023-01-28T15:03:03Z)
• SemanticCAP: Chromatin Accessibility Prediction Enhanced by Features Learning from a Language Model [3.0643865202019698]
  We propose a new solution named SemanticCAP to identify accessible regions of the genome. It introduces a gene language model which models the context of gene sequences, thus being able to provide an effective representation of gene sequences. Compared with other systems under public benchmarks, our model proved to have better performance.
  arXiv  Detail & Related papers  (2022-04-05T11:47:58Z)
• Epigenomic language models powered by Cerebras [0.0]
  Epigenomic BERT (or EBERT) learns representations based on both DNA sequence and paired epigenetic state inputs. We show EBERT's transfer learning potential by demonstrating strong performance on a cell type-specific transcription factor binding prediction task. Our fine-tuned model exceeds state of the art performance on 4 of 13 evaluation datasets from ENCODE-DREAM benchmarks and earns an overall rank of 3rd on the challenge leaderboard.
  arXiv  Detail & Related papers  (2021-12-14T17:23:42Z)
• Multi-modal Self-supervised Pre-training for Regulatory Genome Across Cell Types [75.65676405302105]
  We propose a simple yet effective approach for pre-training genome data in a multi-modal and self-supervised manner, which we call GeneBERT. We pre-train our model on the ATAC-seq dataset with 17 million genome sequences.
  arXiv  Detail & Related papers  (2021-10-11T12:48:44Z)

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.