GESR: A Genetic Programming-Based Symbolic Regression Method with Gene Editing

• URL: http://arxiv.org/abs/2605.10685v3
• Date: Wed, 13 May 2026 08:20:22 GMT
• Title: GESR: A Genetic Programming-Based Symbolic Regression Method with Gene Editing
• Abstract summary: Genetic Programming (GP) based on evolutionary algorithms remains one of the most classical and widely adopted methods.<n>We propose in this paper a symbolic regression approach based on gene editing, termed GESR.
• Score: 27.125242277304267
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Mathematical formulas serve as a language through which humans communicate with nature. Discovering mathematical laws from scientific data to describe natural phenomena has been a long-standing pursuit of humanity for centuries. In the field of artificial intelligence, this challenge is known as the symbolic regression problem. Among existing symbolic regression approaches, Genetic Programming (GP) based on evolutionary algorithms remains one of the most classical and widely adopted methods. GP simulates the evolutionary process across generations through genetic mutation and crossover. However, mutations and crossovers in GP are entirely random. While this randomness effectively mimics natural evolution, it inevitably produces both beneficial and detrimental variations. If there existed a metaphorical `God` capable of foreseeing which genetic mutations or crossovers would yield superior outcomes and performing targeted gene editing accordingly, the efficiency of evolution could be substantially improved. Motivated by this idea, we propose in this paper a symbolic regression approach based on gene editing, termed GESR. In GESR, we trained two "hands of God" (two BERT models). Among them, the first leverages the BERT's masked language modeling capability to guide the mutation of genes (expression symbols). The other BERT model guides the crossover of individual genes by predicting the crossover point. Experimental results demonstrate that GESR significantly improves computational efficiency compared with traditional GP algorithms and achieves strong overall performance across multiple symbolic regression tasks.

Related papers

• Beyond Independent Genes: Learning Module-Inductive Representations for Gene Perturbation Prediction [48.80217316452559]
  scBIG is a module-inductive prediction framework that explicitly models coordinated gene programs.<n> scBIG consistently outperforms state-of-the-art methods, particularly on unseen and perturbation settings.
  arXiv  Detail & Related papers  (2026-02-03T16:43:40Z)
• GRAPE: Heterogeneous Graph Representation Learning for Genetic Perturbation with Coding and Non-Coding Biotype [51.58774936662233]
  Building gene regulatory networks (GRN) is essential to understand and predict the effects of genetic perturbations.<n>In this work, we leverage pre-trained large language model and DNA sequence model to extract features from gene descriptions and DNA sequence data.<n>We introduce gene biotype information for the first time in genetic perturbation, simulating the distinct roles of genes with different biotypes in regulating cellular processes.
  arXiv  Detail & Related papers  (2025-05-06T03:35:24Z)
• 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)
• VQDNA: Unleashing the Power of Vector Quantization for Multi-Species Genomic Sequence Modeling [60.91599380893732]
  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.
  arXiv  Detail & Related papers  (2024-05-13T20:15:03Z)
• 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)
• Epigenetics Algorithms: Self-Reinforcement-Attention mechanism to regulate chromosomes expression [0.0]
  This paper proposes a new epigenetics algorithm that mimics the epigenetics phenomenon known as methylation. The novelty of our epigenetics algorithms lies primarily in taking advantage of attention mechanisms and deep learning, which fits well with the genes/silencing concept.
  arXiv  Detail & Related papers  (2023-03-15T21:33:21Z)
• The Effect of Multi-Generational Selection in Geometric Semantic Genetic Programming [8.0322025529523]
  Geometric Semantic Genetic Programming (GSGP) has shown to be successfully applicable to many real-world problems. Due to a peculiarity in its implementation, GSGP needs to store all the evolutionary history, i.e., all populations from the first one. We exploit this stored information to define a multi-generational selection scheme that is able to use individuals from older populations.
  arXiv  Detail & Related papers  (2022-05-05T12:26:25Z)
• rfPhen2Gen: A machine learning based association study of brain imaging phenotypes to genotypes [71.1144397510333]
  We learned machine learning models to predict SNPs using 56 brain imaging QTs. SNPs within the known Alzheimer disease (AD) risk gene APOE had lowest RMSE for lasso and random forest. Random forests identified additional SNPs that were not prioritized by the linear models but are known to be associated with brain-related disorders.
  arXiv  Detail & Related papers  (2022-03-31T20:15:22Z)
• VEGN: Variant Effect Prediction with Graph Neural Networks [19.59965282985234]
  We propose VEGN, which models variant effect prediction using a graph neural network (GNN) that operates on a heterogeneous graph with genes and variants. The graph is created by assigning variants to genes and connecting genes with an gene-gene interaction network. VeGN improves the performance of existing state-of-the-art models.
  arXiv  Detail & Related papers  (2021-06-25T13:51:46Z)
• Zoetrope Genetic Programming for Regression [2.642406403099596]
  The Zoetrope Genetic Programming (ZGP) algorithm is based on an original representation for mathematical expressions. ZGP is validated using a large number of public domain regression datasets.
  arXiv  Detail & Related papers  (2021-02-26T10:47:10Z)

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.