ArticleBMC bioinformatics2020
Identifying diseases that cause psychological trauma and social avoidance by GCN-Xgboost.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Soft-computing models for predicting plastic viscosity and interface yield stress of fresh concrete.Scientific reports · 2025Article
- Knowledge, attitude, and practice toward postoperative self-management among kidney transplant recipients.BMC medical education · 2024Article
- A stacking ensemble model for predicting the occurrence of carotid atherosclerosis.Frontiers in endocrinology · 2024Article
- Factors influencing social avoidance and distress after radical lung cancer resection: a mediation analysis.American journal of translational research · 2024Article
- Robust Machine Learning Framework for Modeling the Compressive Strength of SFRC: Database Compilation, Predictive Analysis, and Empirical Verification.Materials (Basel, Switzerland) · 2023Article
- Self-supervised graph representation learning integrates multiple molecular networks and decodes gene-disease relationships.Patterns (New York, N.Y.) · 2023Article
- Predicting compressive strength of high-performance concrete with high volume ground granulated blast-furnace slag replacement using boosting machine learning algorithms.Scientific reports · 2022Article
- Multi-View Feature Enhancement Based on Self-Attention Mechanism Graph Convolutional Network for Autism Spectrum Disorder Diagnosis.Frontiers in human neuroscience · 2022Article
- Trajectories and predictors of social avoidance in female patients with breast cancer.Frontiers in psychiatry · 2022Article
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Authors and funding
7 authors.
Funding
Abstract
backgroundWith the rapid development of medical treatment, many patients not only consider the survival time, but also care about the quality of life. Changes in physical, psychological and social functions after and during treatment have caused a lot of troubles to patients and their families. Based on the bio-psycho-social medical model theory, mental health plays an important role in treatment. Therefore, it is necessary for medical staff to know the diseases which have high potential to cause psychological trauma and social avoidance (PTSA).
resultsFirstly, we obtained diseases which can cause PTSA from literatures. Then, we calculated the similarities of related-diseases to build a disease network. The similarities between diseases were based on their known related genes. Then, we obtained these diseases-related proteins from UniProt. These proteins were extracted as the features of diseases. Therefore, in the disease network, each node denotes a disease and contains the information of its related proteins, and the edges of the network are the similarities of diseases. Then, graph convolutional network (GCN) was used to encode the disease network. In this way, each disease's own feature and its relationship with other diseases were extracted. Finally, Xgboost was used to identify PTSA diseases.
conclusionWe developed a novel method 'GCN-Xgboost' and compared it with some traditional methods. Using leave-one-out cross-validation, the AUC and AUPR were higher than some existing methods. In addition, case studies have been done to verify our results. We also discussed the trajectory of social avoidance and distress during acute survival of breast cancer patients.
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