SimpleChrome: Encoding of Combinatorial Effects for Predicting Gene Expression

• URL: http://arxiv.org/abs/2012.08671v2
• Date: Thu, 17 Dec 2020 05:58:21 GMT
• Title: SimpleChrome: Encoding of Combinatorial Effects for Predicting Gene Expression
• Authors: Wei Cheng, Ghulam Murtaza, Aaron Wang
• Abstract summary: We present SimpleChrome, a deep learning model that learns the histone modification representations of genes. The features learned from the model allow us to better understand the latent effects of cross-gene interactions and direct gene regulation on the target gene expression.
• Score: 8.326669256957352
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Due to recent breakthroughs in state-of-the-art DNA sequencing technology, genomics data sets have become ubiquitous. The emergence of large-scale data sets provides great opportunities for better understanding of genomics, especially gene regulation. Although each cell in the human body contains the same set of DNA information, gene expression controls the functions of these cells by either turning genes on or off, known as gene expression levels. There are two important factors that control the expression level of each gene: (1) Gene regulation such as histone modifications can directly regulate gene expression. (2) Neighboring genes that are functionally related to or interact with each other that can also affect gene expression level. Previous efforts have tried to address the former using Attention-based model. However, addressing the second problem requires the incorporation of all potentially related gene information into the model. Though modern machine learning and deep learning models have been able to capture gene expression signals when applied to moderately sized data, they have struggled to recover the underlying signals of the data due to the nature of the data's higher dimensionality. To remedy this issue, we present SimpleChrome, a deep learning model that learns the latent histone modification representations of genes. The features learned from the model allow us to better understand the combinatorial effects of cross-gene interactions and direct gene regulation on the target gene expression. The results of this paper show outstanding improvements on the predictive capabilities of downstream models and greatly relaxes the need for a large data set to learn a robust, generalized neural network. These results have immediate downstream effects in epigenomics research and drug development.

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)
• 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)
• 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)
• MuSe-GNN: Learning Unified Gene Representation From Multimodal Biological Graph Data [22.938437500266847]
  We introduce a novel model called Multimodal Similarity Learning Graph Neural Network. It combines Multimodal Machine Learning and Deep Graph Neural Networks to learn gene representations from single-cell sequencing and spatial transcriptomic data. Our model efficiently produces unified gene representations for the analysis of gene functions, tissue functions, diseases, and species evolution.
  arXiv  Detail & Related papers  (2023-09-29T13:33:53Z)
• 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)
• 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)

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.