Multi-omics Sampling-based Graph Transformer for Synthetic Lethality Prediction

• URL: http://arxiv.org/abs/2310.11082v1
• Date: Tue, 17 Oct 2023 09:06:41 GMT
• Title: Multi-omics Sampling-based Graph Transformer for Synthetic Lethality Prediction
• Abstract summary: Synthetic lethality (SL) prediction is used to identify if the co-mutation of two genes results in cell death. graph neural networks (GNNs) suffer from limitations in their message passing mechanisms. We propose a new multi-omics sampling-based graph transformer for SL prediction (MSGT-SL)
• Score: 20.363977976628792
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Synthetic lethality (SL) prediction is used to identify if the co-mutation of two genes results in cell death. The prevalent strategy is to abstract SL prediction as an edge classification task on gene nodes within SL data and achieve it through graph neural networks (GNNs). However, GNNs suffer from limitations in their message passing mechanisms, including over-smoothing and over-squashing issues. Moreover, harnessing the information of non-SL gene relationships within large-scale multi-omics data to facilitate SL prediction poses a non-trivial challenge. To tackle these issues, we propose a new multi-omics sampling-based graph transformer for SL prediction (MSGT-SL). Concretely, we introduce a shallow multi-view GNN to acquire local structural patterns from both SL and multi-omics data. Further, we input gene features that encode multi-view information into the standard self-attention to capture long-range dependencies. Notably, starting with batch genes from SL data, we adopt parallel random walk sampling across multiple omics gene graphs encompassing them. Such sampling effectively and modestly incorporates genes from omics in a structure-aware manner before using self-attention. We showcase the effectiveness of MSGT-SL on real-world SL tasks, demonstrating the empirical benefits gained from the graph transformer and multi-omics data.

Related papers

• SynLeaF: A Dual-Stage Multimodal Fusion Framework for Synthetic Lethality Prediction Across Pan- and Single-Cancer Contexts [49.98062629139681]
  Synthetic lethality (SL) prediction is important for guiding the development of cancer drugs and therapies.<n>Existing multimodal methods often suffer from "modality laziness" due to disparate convergence speeds.<n>We propose SynLeaF, a dual-stage multimodal fusion framework for SL prediction across pan- and single-cancer contexts.
  arXiv  Detail & Related papers  (2026-03-23T05:55:39Z)
• Decoding Dynamic Visual Experience from Calcium Imaging via Cell-Pattern-Aware SSL [6.099145932421348]
  We present a novel approach to self-supervised pretraining, POYO-SSL, that exploits the heterogeneity of neural data to improve pre-training and achieve benefits of scale.<n>Specifically, in POYO-SSL we pretrain only on predictable (statistically regular) neurons-identified on the pretraining split via simple higher-order statistics (skewness and kurtosis)-then we fine-tune on the unpredictable population for downstream tasks.<n>On the Allen Brain Observatory dataset, this strategy yields approximately 12-13% relative gains over from-scratch training and exhibits smooth, monotonic scaling with model size.
  arXiv  Detail & Related papers  (2025-10-21T10:57:52Z)
• Attributes as Textual Genes: Leveraging LLMs as Genetic Algorithm Simulators for Conditional Synthetic Data Generation [4.268367038882249]
  Genetic Prompt is a framework that combines genetic algorithms with Large Language Models (LLMs) to augment synthetic data generation.<n>Our approach treats semantic text attributes as gene sequences and leverages the LLM to simulate crossover and mutation operations.<n>Our findings validate that Genetic Prompt is an effective method for producing high-quality synthetic data for a wide range of NLP applications.
  arXiv  Detail & Related papers  (2025-09-02T07:35:20Z)
• 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)
• Interpretable High-order Knowledge Graph Neural Network for Predicting Synthetic Lethality in Human Cancers [16.6037911734201]
  Synthetic lethality (SL) is a promising gene interaction for cancer therapy. Recent SL prediction methods integrate knowledge graphs into graph neural networks (GNNs) We propose Diverse Graph Information Bottleneck for Synthetic Lethality (DGIB4SL)
  arXiv  Detail & Related papers  (2025-03-08T04:37:28Z)
• GraphSeqLM: A Unified Graph Language Framework for Omic Graph Learning [20.906136206438102]
  Graph Neural Networks (GNNs) offer a robust framework for analyzing large-scale signaling pathways and protein-protein interaction networks. We propose Graph Sequence Language Model (GraphSeqLM), a framework that enhances GNNs with biological sequence embeddings.
  arXiv  Detail & Related papers  (2024-12-20T11:05:26Z)
• Sampling-guided Heterogeneous Graph Neural Network with Temporal Smoothing for Scalable Longitudinal Data Imputation [17.81217890585335]
  We propose a novel framework, the Sampling-guided Heterogeneous Graph Neural Network (SHT-GNN), to tackle the challenge of missing data imputation. By leveraging subject-wise mini-batch sampling and a multi-layer temporal smoothing mechanism, SHT-GNN efficiently scales to large datasets. Experiments on both synthetic and real-world datasets, including the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, demonstrate that SHT-GNN significantly outperforms existing imputation methods.
  arXiv  Detail & Related papers  (2024-11-07T17:41:07Z)
• Quantifying the Optimization and Generalization Advantages of Graph Neural Networks Over Multilayer Perceptrons [50.33260238739837]
  Graph networks (GNNs) have demonstrated remarkable capabilities in learning from graph-structured data.<n>There remains a lack of analysis comparing GNNs and generalizations from an optimization and generalization perspective.
  arXiv  Detail & Related papers  (2023-06-24T10:21:11Z)
• GNN-SL: Sequence Labeling Based on Nearest Examples via GNN [50.55076156520809]
  We introduce graph neural networks sequence labeling (GNN-SL) GNN-SL augments vanilla sequence labeling model output with similar tagging examples retrieved from the whole training set. We conduct a variety of experiments on three typical sequence labeling tasks. GNN-SL achieves results of 96.9 (+0.2) on PKU, 98.3 (+0.4) on CITYU, 98.5 (+0.2) on MSR, and 96.9 (+0.2) on AS for the CWS task.
  arXiv  Detail & Related papers  (2022-12-05T04:22:00Z)
• Generative Data Augmentation for Non-IID Problem in Decentralized Clinical Machine Learning [8.24935363381541]
  We propose a generative augmentation framework in swarm learning called SL-GAN. SL-GAN augments the non-IID data by generating the synthetic data from participants. We show that SLGAN outperforms state-of-art methods on three clinical datasets including Tuberculosis, Leukemia, COVID-19.
  arXiv  Detail & Related papers  (2022-12-02T11:46:09Z)
• Mixed Graph Contrastive Network for Semi-Supervised Node Classification [63.924129159538076]
  We propose a novel graph contrastive learning method, termed Mixed Graph Contrastive Network (MGCN) In our method, we improve the discriminative capability of the latent embeddings by an unperturbed augmentation strategy and a correlation reduction mechanism. By combining the two settings, we extract rich supervision information from both the abundant nodes and the rare yet valuable labeled nodes for discriminative representation learning.
  arXiv  Detail & Related papers  (2022-06-06T14:26:34Z)
• Diverse Sample Generation: Pushing the Limit of Data-free Quantization [85.95032037447454]
  This paper presents a generic Diverse Sample Generation scheme for the generative data-free post-training quantization and quantization-aware training. For large-scale image classification tasks, our DSG can consistently outperform existing data-free quantization methods.
  arXiv  Detail & Related papers  (2021-09-01T07:06:44Z)
• Score-based Generative Modeling in Latent Space [93.8985523558869]
  Score-based generative models (SGMs) have recently demonstrated impressive results in terms of both sample quality and distribution coverage. Here, we propose the Latent Score-based Generative Model (LSGM), a novel approach that trains SGMs in a latent space. Moving from data to latent space allows us to train more expressive generative models, apply SGMs to non-continuous data, and learn smoother SGMs in a smaller space.
  arXiv  Detail & Related papers  (2021-06-10T17:26:35Z)
• MG-GCN: Fast and Effective Learning with Mix-grained Aggregators for Training Large Graph Convolutional Networks [20.07942308916373]
  Graph convolutional networks (GCNs) generate the embeddings of nodes by aggregating the information of their neighbors layer by layer. The high computational and memory cost of GCNs makes it infeasible for training on large graphs. A new model, named Mix-grained GCN (MG-GCN), achieves state-of-the-art performance in terms of accuracy, training speed, convergence speed, and memory cost.
  arXiv  Detail & Related papers  (2020-11-17T14:51:57Z)
• Understanding Self-supervised Learning with Dual Deep Networks [74.92916579635336]
  We propose a novel framework to understand contrastive self-supervised learning (SSL) methods that employ dual pairs of deep ReLU networks. We prove that in each SGD update of SimCLR with various loss functions, the weights at each layer are updated by a emphcovariance operator. To further study what role the covariance operator plays and which features are learned in such a process, we model data generation and augmentation processes through a emphhierarchical latent tree model (HLTM)
  arXiv  Detail & Related papers  (2020-10-01T17:51:49Z)
• Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning [64.98816284854067]
  Graph-based Semi-Supervised Learning (SSL) aims to transfer the labels of a handful of labeled data to the remaining massive unlabeled data via a graph. A novel GCN-based SSL algorithm is presented in this paper to enrich the supervision signals by utilizing both data similarities and graph structure.
  arXiv  Detail & Related papers  (2020-09-15T13:59:28Z)
• Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction [55.94378672172967]
  We focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients. We introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks. Our new model is built upon a carefully designed meta-learner, called Prototypical Network, that is a simple yet effective meta learning machine for few-shot image classification.
  arXiv  Detail & Related papers  (2020-09-02T02:50:30Z)
• Semi-supervised Medical Image Classification with Global Latent Mixing [8.330337646455957]
  Computer-aided diagnosis via deep learning relies on large-scale annotated data sets. Semi-supervised learning mitigates this challenge by leveraging unlabeled data. We present a novel SSL approach that trains the neural network on linear mixing of labeled and unlabeled data.
  arXiv  Detail & Related papers  (2020-05-22T14:49:13Z)

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.