RT Journal Article
T1 Novel dimensionality reduction method, Taelcore, enhances lung transplantation risk prediction
A1 Gouiaa, Fatma
A1 Vomo-Donfack, Kelly L.
A1 Tran-Dinh, Alexy
A1 Morilla, Ian
K1 Biología molecular
AB In this work, we present a new approach to predict the risk of acute cellular rejection (ACR) after lung transplantation by using machine learning algorithms, such as Multilayer Perceptron (MLP) or Autoencoder (AE), and combining them with topological data analysis (TDA) tools. Our proposed method, named topological autoencoder with best linear combination for optimal reduction of embeddings (Taelcore), effectively reduces the dimensionality of high-dimensional datasets and yields better results compared to other models. We validate the effectiveness of Taelcore in reducing the prediction error rate on four datasets. Furthermore, we demonstrate that Taelcore’s topological improvements have a positive effect on the majority of the machine learning algorithms used. By providing a new way to diagnose patients and detect complications early, this work contributes to improved clinical outcomes in lung transplantation.
PB Elsevier
YR 2024
FD 2024-01-04
LK https://hdl.handle.net/10630/28583
UL https://hdl.handle.net/10630/28583
LA eng
NO Fatma Gouiaa, Kelly L. Vomo-Donfack, Alexy Tran-Dinh, Ian Morilla, Novel dimensionality reduction method, Taelcore, enhances lung transplantation risk prediction, Computers in Biology and Medicine, Volume 169, 2024, 107969, ISSN 0010-4825, https://doi.org/10.1016/j.compbiomed.2024.107969
NO Funding for open Access charge: Universidad de Málaga / CBUA.We would like to thank the funding from the National Research Association (ANR) (Inflamex renewal 10-LABX-0017 to I Morilla), Consejería de Universidades, Ciencias 𝑦 Desarrollo, fondos FEDER de la Junta de Andalucía (ProyExec_0499 to I Morilla), DHU FIRE Emergence 4, and the l’Agence de la Biomedecine.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026