Graph algorithms for predicting subcellular localization at the pathway level

• URL: http://arxiv.org/abs/2212.05991v1
• Date: Mon, 12 Dec 2022 15:49:43 GMT
• Title: Graph algorithms for predicting subcellular localization at the pathway level
• Authors: Chris S. Magnano, Anthony Gitter
• Abstract summary: We develop graph algorithms to predict the localization of all interactions in a biological pathway as an edge-labeling task. We also perform a case study where we construct biological pathways and predict localizations of human fibroblasts undergoing viral infection.
• Score: 1.370633147306388
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Protein subcellular localization is an important factor in normal cellular processes and disease. While many protein localization resources treat it as static, protein localization is dynamic and heavily influenced by biological context. Biological pathways are graphs that represent a specific biological context and can be inferred from large-scale data. We develop graph algorithms to predict the localization of all interactions in a biological pathway as an edge-labeling task. We compare a variety of models including graph neural networks, probabilistic graphical models, and discriminative classifiers for predicting localization annotations from curated pathway databases. We also perform a case study where we construct biological pathways and predict localizations of human fibroblasts undergoing viral infection. Pathway localization prediction is a promising approach for integrating publicly available localization data into the analysis of large-scale biological data.

Related papers

• Interpretable Retinal Disease Prediction Using Biology-Informed Heterogeneous Graph Representations [40.8160960729546]
  Interpretability is crucial to enhance trust in machine learning models for medical diagnostics. This work proposes a method that surpasses the performance of established machine learning models.
  arXiv  Detail & Related papers  (2025-02-23T19:27:47Z)
• Neural Echos: Depthwise Convolutional Filters Replicate Biological Receptive Fields [56.69755544814834]
  We present evidence suggesting that depthwise convolutional kernels are effectively replicating the biological receptive fields observed in the mammalian retina. We propose a scheme that draws inspiration from the biological receptive fields.
  arXiv  Detail & Related papers  (2024-01-18T18:06:22Z)
• A Review of Link Prediction Applications in Network Biology [2.7624021966289605]
  Link prediction (LP) methodologies are instrumental in inferring missing or prospective associations within biological networks. We systematically dissect the attributes of local, centrality, and embedding-based LP approaches, applied to static and dynamic biological networks. We conclude the review with an exploration of the essential characteristics expected from future LP models, poised to advance our comprehension of the intricate interactions governing biological systems.
  arXiv  Detail & Related papers  (2023-12-03T04:23:51Z)
• 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)
• Tertiary Lymphoid Structures Generation through Graph-based Diffusion [54.37503714313661]
  In this work, we leverage state-of-the-art graph-based diffusion models to generate biologically meaningful cell-graphs. We show that the adopted graph diffusion model is able to accurately learn the distribution of cells in terms of their tertiary lymphoid structures (TLS) content.
  arXiv  Detail & Related papers  (2023-10-10T14:37:17Z)
• A novel framework employing deep multi-attention channels network for the autonomous detection of metastasizing cells through fluorescence microscopy [0.20999222360659603]
  We developed a computational framework that can distinguish between normal and metastasizing human cells. The method relies on fluorescence microscopy images showing the spatial organization of actin and vimentin filaments in normal and metastasizing single cells.
  arXiv  Detail & Related papers  (2023-09-02T11:20:10Z)
• Predicting Biomedical Interactions with Probabilistic Model Selection for Graph Neural Networks [5.156812030122437]
  Current biological networks are noisy, sparse, and incomplete. Experimental identification of such interactions is both time-consuming and expensive. Deep graph neural networks have shown their effectiveness in modeling graph-structured data and achieved good performance in biomedical interaction prediction. Our proposed method enables the graph convolutional networks to dynamically adapt their depths to accommodate an increasing number of interactions.
  arXiv  Detail & Related papers  (2022-11-22T20:44:28Z)
• Self-Supervised Graph Representation Learning for Neuronal Morphologies [75.38832711445421]
  We present GraphDINO, a data-driven approach to learn low-dimensional representations of 3D neuronal morphologies from unlabeled datasets. We show, in two different species and across multiple brain areas, that this method yields morphological cell type clusterings on par with manual feature-based classification by experts. Our method could potentially enable data-driven discovery of novel morphological features and cell types in large-scale datasets.
  arXiv  Detail & Related papers  (2021-12-23T12:17:47Z)
• Using ontology embeddings for structural inductive bias in gene expression data analysis [6.587739898387445]
  Stratifying cancer patients based on their gene expression levels allows improving diagnosis, survival analysis and treatment planning. We propose to incorporate biological knowledge about genes into the machine learning system for the task of patient classification given their gene expression data.
  arXiv  Detail & Related papers  (2020-11-22T12:13:29Z)
• Towards an Automatic Analysis of CHO-K1 Suspension Growth in Microfluidic Single-cell Cultivation [63.94623495501023]
  We propose a novel Machine Learning architecture, which allows us to infuse a neural deep network with human-powered abstraction on the level of data. Specifically, we train a generative model simultaneously on natural and synthetic data, so that it learns a shared representation, from which a target variable, such as the cell count, can be reliably estimated.
  arXiv  Detail & Related papers  (2020-10-20T08:36:51Z)
• 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)

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.