Deep Biological Pathway Informed Pathology-Genomic Multimodal Survival Prediction

• URL: http://arxiv.org/abs/2301.02383v1
• Date: Fri, 6 Jan 2023 05:24:41 GMT
• Title: Deep Biological Pathway Informed Pathology-Genomic Multimodal Survival Prediction
• Authors: Lin Qiu, Aminollah Khormali, Kai Liu
• Abstract summary: We propose PONET- a novel biological pathway-informed pathology-genomic deep model. Our proposed method achieves superior predictive performance and reveals meaningful biological interpretations.
• Score: 7.133948707208067
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The integration of multi-modal data, such as pathological images and genomic data, is essential for understanding cancer heterogeneity and complexity for personalized treatments, as well as for enhancing survival predictions. Despite the progress made in integrating pathology and genomic data, most existing methods cannot mine the complex inter-modality relations thoroughly. Additionally, identifying explainable features from these models that govern preclinical discovery and clinical prediction is crucial for cancer diagnosis, prognosis, and therapeutic response studies. We propose PONET- a novel biological pathway-informed pathology-genomic deep model that integrates pathological images and genomic data not only to improve survival prediction but also to identify genes and pathways that cause different survival rates in patients. Empirical results on six of The Cancer Genome Atlas (TCGA) datasets show that our proposed method achieves superior predictive performance and reveals meaningful biological interpretations. The proposed method establishes insight into how to train biologically informed deep networks on multimodal biomedical data which will have general applicability for understanding diseases and predicting response and resistance to treatment.

Related papers

• Multimodal Data Integration for Precision Oncology: Challenges and Future Directions [10.817613081663007]
  The essence of precision oncology lies in its commitment to tailor targeted treatments and care measures to each patient based on the individual characteristics of the tumor. Over the past decade, multimodal data integration technology for precision oncology has made significant strides. We provide a comprehensive overview of about 300 papers detailing cutting-edge multimodal data integration techniques in precision oncology.
  arXiv  Detail & Related papers  (2024-06-28T02:35:05Z)
• Pathology-genomic fusion via biologically informed cross-modality graph learning for survival analysis [7.996257103473235]
  We propose Pathology-Genome Heterogeneous Graph (PGHG) that integrates whole slide images (WSI) and bulk RNA-Seq expression data with heterogeneous graph neural network for cancer survival analysis. The PGHG consists of biological knowledge-guided representation learning network and pathology-genome heterogeneous graph. We evaluate the model on low-grade gliomas, glioblastoma, and kidney renal papillary cell carcinoma datasets from the Cancer Genome Atlas.
  arXiv  Detail & Related papers  (2024-04-11T09:07:40Z)
• Unlocking the Power of Multi-institutional Data: Integrating and Harmonizing Genomic Data Across Institutions [3.5489676012585236]
  We introduce the Bridge model to derive integrated features to preserve information beyond common genes. The model consistently excels in predicting patient survival across six cancer types in GENIE BPC data.
  arXiv  Detail & Related papers  (2024-01-30T23:25:05Z)
• 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)
• 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)
• Multimodal Data Integration for Oncology in the Era of Deep Neural Networks: A Review [0.0]
  Integrating diverse data types can improve the accuracy and reliability of cancer diagnosis and treatment. Deep neural networks have facilitated the development of sophisticated multimodal data fusion approaches. Recent deep learning frameworks such as Graph Neural Networks (GNNs) and Transformers have shown remarkable success in multimodal learning.
  arXiv  Detail & Related papers  (2023-03-11T17:52:03Z)
• Deep learning methods for drug response prediction in cancer: predominant and emerging trends [50.281853616905416]
  Exploiting computational predictive models to study and treat cancer holds great promise in improving drug development and personalized design of treatment plans. A wave of recent papers demonstrates promising results in predicting cancer response to drug treatments while utilizing deep learning methods. This review allows to better understand the current state of the field and identify major challenges and promising solution paths.
  arXiv  Detail & Related papers  (2022-11-18T03:26:31Z)
• Topological Data Analysis of copy number alterations in cancer [70.85487611525896]
  We explore the potential to capture information contained in cancer genomic information using a novel topology-based approach. We find that this technique has the potential to extract meaningful low-dimensional representations in cancer somatic genetic data.
  arXiv  Detail & Related papers  (2020-11-22T17:31:23Z)
• 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)
• Trajectories, bifurcations and pseudotime in large clinical datasets: applications to myocardial infarction and diabetes data [94.37521840642141]
  We suggest a semi-supervised methodology for the analysis of large clinical datasets, characterized by mixed data types and missing values. The methodology is based on application of elastic principal graphs which can address simultaneously the tasks of dimensionality reduction, data visualization, clustering, feature selection and quantifying the geodesic distances (pseudotime) in partially ordered sequences of observations.
  arXiv  Detail & Related papers  (2020-07-07T21:04:55Z)
• A Pipeline for Integrated Theory and Data-Driven Modeling of Genomic and Clinical Data [5.921993992338802]
  We propose a pipeline for knowledge discovery from integrated genomic and clinical data. We demonstrate how this pipeline can improve breast cancer outcome prediction models, and can provide a biologically interpretable view of sequencing data.
  arXiv  Detail & Related papers  (2020-05-05T22:23:27Z)

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.