Epigenetics Algorithms: Self-Reinforcement-Attention mechanism to regulate chromosomes expression

• URL: http://arxiv.org/abs/2303.10154v1
• Date: Wed, 15 Mar 2023 21:33:21 GMT
• Title: Epigenetics Algorithms: Self-Reinforcement-Attention mechanism to regulate chromosomes expression
• Authors: Mohamed Djallel Dilmi, Hanene Azzag and Mustapha Lebbah
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Genetic algorithms are a well-known example of bio-inspired heuristic methods. They mimic natural selection by modeling several operators such as mutation, crossover, and selection. Recent discoveries about Epigenetics regulation processes that occur "on top of" or "in addition to" the genetic basis for inheritance involve changes that affect and improve gene expression. They raise the question of improving genetic algorithms (GAs) by modeling epigenetics operators. This paper proposes a new epigenetics algorithm that mimics the epigenetics phenomenon known as DNA methylation. The novelty of our epigenetics algorithms lies primarily in taking advantage of attention mechanisms and deep learning, which fits well with the genes enhancing/silencing concept. The paper develops theoretical arguments and presents empirical studies to exhibit the capability of the proposed epigenetics algorithms to solve more complex problems efficiently than has been possible with simple GAs; for example, facing two Non-convex (multi-peaks) optimization problems as presented in this paper, the proposed epigenetics algorithm provides good performances and shows an excellent ability to overcome the lack of local optimum and thus find the global optimum.

Related papers

• Learning to Discover Regulatory Elements for Gene Expression Prediction [59.470991831978516]
  Seq2Exp is a Sequence to Expression network designed to discover and extract regulatory elements that drive target gene expression. Our approach captures the causal relationship between epigenomic signals, DNA sequences and their associated regulatory elements.
  arXiv  Detail & Related papers  (2025-02-19T03:25:49Z)
• 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)
• Survey and Improvement Strategies for Gene Prioritization with Large Language Models [61.24568051916653]
  Large language models (LLMs) have performed well in medical exams, but their effectiveness in diagnosing rare genetic diseases has not been assessed. We used multi-agent and Human Phenotype Ontology (HPO) classification to categorized patients based on phenotypes and solvability levels. At baseline, GPT-4 outperformed other LLMs, achieving near 30% accuracy in ranking causal genes correctly.
  arXiv  Detail & Related papers  (2025-01-30T23:03:03Z)
• Cross-Attention Graph Neural Networks for Inferring Gene Regulatory Networks with Skewed Degree Distribution [9.919024883502322]
  Cross-Attention Complex Dual Graph Embedding Model (XATGRN) Our model consistently outperforms existing state-of-the-art methods across various datasets.
  arXiv  Detail & Related papers  (2024-12-18T10:56:40Z)
• GeneQuery: A General QA-based Framework for Spatial Gene Expression Predictions from Histology Images [41.732831871866516]
  Whole-slide hematoxylin and eosin stained histological images are readily accessible and allow for detailed examinations of tissue structure and composition at the microscopic level. Recent advancements have utilized these histological images to predict spatially resolved gene expression profiles. GeneQuery aims to solve this gene expression prediction task in a question-answering (QA) manner for better generality and flexibility.
  arXiv  Detail & Related papers  (2024-11-27T14:33:13Z)
• An Evolutional Neural Network Framework for Classification of Microarray Data [0.0]
  This research aims to apply a hybrid model of Genetic Algorithm and Neural Network to overcome the problem during subset selection of informative genes. Experimental results show the proposed method suggested high accuracy and minimum number of selected genes in comparison with other machine learning algorithms.
  arXiv  Detail & Related papers  (2024-11-20T13:48:40Z)
• 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)
• Unsupervised ensemble-based phenotyping helps enhance the discoverability of genes related to heart morphology [57.25098075813054]
  We propose a new framework for gene discovery entitled Un Phenotype Ensembles. It builds a redundant yet highly expressive representation by pooling a set of phenotypes learned in an unsupervised manner. These phenotypes are then analyzed via (GWAS), retaining only highly confident and stable associations.
  arXiv  Detail & Related papers  (2023-01-07T18:36:44Z)
• The Effect of Epigenetic Blocking on Dynamic Multi-Objective Optimisation Problems [1.4502611532302039]
  Epigenetic mechanisms allow quick non- or partially-genetic adaptations to environmental changes. This paper asks if the advantages that epigenetic inheritance provide in the natural world are replicated in dynamic multi-objective problems.
  arXiv  Detail & Related papers  (2022-11-25T16:33:05Z)
• Modeling epigenetic evolutionary algorithms: An approach based on the epigenetic regulation process [0.0]
  This thesis presents an epigenetic technique for Evolutionary Algorithms, inspired by the epigenetic regulation process. Epigenetic regulation comprises biological mechanisms by which small molecules, also known as epigenetic tags, are attached to or removed from a particular gene.
  arXiv  Detail & Related papers  (2021-02-18T21:51:50Z)

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.