scBIT: Integrating Single-cell Transcriptomic Data into fMRI-based Prediction for Alzheimer's Disease Diagnosis

• URL: http://arxiv.org/abs/2502.02630v1
• Date: Tue, 04 Feb 2025 18:37:46 GMT
• Title: scBIT: Integrating Single-cell Transcriptomic Data into fMRI-based Prediction for Alzheimer's Disease Diagnosis
• Authors: Yu-An Huang, Yao Hu, Yue-Chao Li, Xiyue Cao, Xinyuan Li, Kay Chen Tan, Zhu-Hong You, Zhi-An Huang,
• Abstract summary: scBIT is a novel method for enhancing Alzheimer's disease (AD) prediction by combining fMRI with single-nucleus RNA (snRNA) It employs a sampling strategy to segment snRNA data into cell-type-specific gene networks and utilizes a self-explainable graph neural network to extract critical subgraphs. Extensive experiments validate scBIT's effectiveness in revealing intricate brain region-gene associations.
• Score: 24.268703526039367
• License:
• Abstract: Functional MRI (fMRI) and single-cell transcriptomics are pivotal in Alzheimer's disease (AD) research, each providing unique insights into neural function and molecular mechanisms. However, integrating these complementary modalities remains largely unexplored. Here, we introduce scBIT, a novel method for enhancing AD prediction by combining fMRI with single-nucleus RNA (snRNA). scBIT leverages snRNA as an auxiliary modality, significantly improving fMRI-based prediction models and providing comprehensive interpretability. It employs a sampling strategy to segment snRNA data into cell-type-specific gene networks and utilizes a self-explainable graph neural network to extract critical subgraphs. Additionally, we use demographic and genetic similarities to pair snRNA and fMRI data across individuals, enabling robust cross-modal learning. Extensive experiments validate scBIT's effectiveness in revealing intricate brain region-gene associations and enhancing diagnostic prediction accuracy. By advancing brain imaging transcriptomics to the single-cell level, scBIT sheds new light on biomarker discovery in AD research. Experimental results show that incorporating snRNA data into the scBIT model significantly boosts accuracy, improving binary classification by 3.39% and five-class classification by 26.59%. The codes were implemented in Python and have been released on GitHub (https://github.com/77YQ77/scBIT) and Zenodo (https://zenodo.org/records/11599030) with detailed instructions.

Related papers

• SGUQ: Staged Graph Convolution Neural Network for Alzheimer's Disease Diagnosis using Multi-Omics Data [7.090283934070421]
  Alzheimer's disease (AD) is a chronic neurodegenerative disorder and the leading cause of dementia. Conventional approaches typically require the completion of all omics data at the outset to achieve optimal AD diagnosis. We propose a novel staged graph convolutional network with uncertainty quantification (SGUQ)
  arXiv  Detail & Related papers  (2024-10-14T19:51:32Z)
• Trustworthy Enhanced Multi-view Multi-modal Alzheimer's Disease Prediction with Brain-wide Imaging Transcriptomics Data [9.325994464749998]
  Brain transcriptomics provides insights into the molecular mechanisms by which the brain coordinates its functions and processes. Existing multimodal methods for predicting Alzheimer's disease (AD) primarily rely on imaging and sometimes genetic data, often neglecting the transcriptomic basis of brain. Here, we propose TMM, a trusted multiview multimodal graph attention framework for AD diagnosis using extensive brain-wide transcriptomics and imaging data.
  arXiv  Detail & Related papers  (2024-06-21T08:39:24Z)
• scCDCG: Efficient Deep Structural Clustering for single-cell RNA-seq via Deep Cut-informed Graph Embedding [12.996418312603284]
  scCDCG (single-cell RNA-seq Clustering via Deep Cut-informed Graph) is a novel framework designed for efficient and accurate clustering of scRNA-seq data. scCDCG comprises three main components: (i) A graph embedding module utilizing deep cut-informed techniques, which effectively captures intercellular high-order structural information. (ii) A self-supervised learning module guided by optimal transport, tailored to accommodate the unique complexities of scRNA-seq data.
  arXiv  Detail & Related papers  (2024-04-09T09:46:17Z)
• Single-Cell Deep Clustering Method Assisted by Exogenous Gene Information: A Novel Approach to Identifying Cell Types [50.55583697209676]
  We develop an attention-enhanced graph autoencoder, which is designed to efficiently capture the topological features between cells. During the clustering process, we integrated both sets of information and reconstructed the features of both cells and genes to generate a discriminative representation. This research offers enhanced insights into the characteristics and distribution of cells, thereby laying the groundwork for early diagnosis and treatment of diseases.
  arXiv  Detail & Related papers  (2023-11-28T09:14:55Z)
• Source-Free Collaborative Domain Adaptation via Multi-Perspective Feature Enrichment for Functional MRI Analysis [55.03872260158717]
  Resting-state MRI functional (rs-fMRI) is increasingly employed in multi-site research to aid neurological disorder analysis. Many methods have been proposed to reduce fMRI heterogeneity between source and target domains. But acquiring source data is challenging due to concerns and/or data storage burdens in multi-site studies. We design a source-free collaborative domain adaptation framework for fMRI analysis, where only a pretrained source model and unlabeled target data are accessible.
  arXiv  Detail & Related papers  (2023-08-24T01:30:18Z)
• Incomplete Multimodal Learning for Complex Brain Disorders Prediction [65.95783479249745]
  We propose a new incomplete multimodal data integration approach that employs transformers and generative adversarial networks. We apply our new method to predict cognitive degeneration and disease outcomes using the multimodal imaging genetic data from Alzheimer's Disease Neuroimaging Initiative cohort.
  arXiv  Detail & Related papers  (2023-05-25T16:29:16Z)
• Machine Learning Based Multimodal Neuroimaging Genomics Dementia Score for Predicting Future Conversion to Alzheimer's Disease [2.914776804701307]
  We developed an image/genotype-based DAT score that represents a subject's likelihood of developing DAT in the future. Using a pre-defined 0.5 threshold on DAT scores, we predicted whether or not a subject would develop DAT in the future.
  arXiv  Detail & Related papers  (2022-03-11T01:35:30Z)
• MuCoMiD: A Multitask Convolutional Learning Framework for miRNA-Disease Association Prediction [0.4061135251278187]
  We propose a novel multi-tasking convolution-based approach, which we refer to as MuCoMiD. MuCoMiD allows automatic feature extraction while incorporating knowledge from 4 heterogeneous biological information sources. We construct large-scale experiments on standard benchmark datasets as well as our proposed larger independent test sets and case studies. MuCoMiD shows an improvement of at least 5% in 5-fold CV evaluation on HMDDv2.0 and HMDDv3.0 datasets and at least 49% on larger independent test sets with unseen diseases and unseen diseases over state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-08-08T10:01:46Z)
• G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification [49.53651166356737]
  We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, that provides interpretable biomarkers. We have evaluated our model on a population study of schizophrenia that includes two functional MRI (fMRI) paradigms and Single Nucleotide Polymorphism (SNP) data.
  arXiv  Detail & Related papers  (2021-01-27T19:28:04Z)
• 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)
• A Systematic Approach to Featurization for Cancer Drug Sensitivity Predictions with Deep Learning [49.86828302591469]
  We train >35,000 neural network models, sweeping over common featurization techniques. We found the RNA-seq to be highly redundant and informative even with subsets larger than 128 features.
  arXiv  Detail & Related papers  (2020-04-30T20:42:17Z)

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.