Cargando…

A two-stage hybrid gene selection algorithm combined with machine learning models to predict the rupture status in intracranial aneurysms

An IA is an abnormal swelling of cerebral vessels, and a subset of these IAs can rupture causing aneurysmal subarachnoid hemorrhage (aSAH), often resulting in death or severe disability. Few studies have used an appropriate method of feature selection combined with machine learning by analyzing tran...

Descripción completa

Detalles Bibliográficos
Autores principales: Li, Qingqing, Wang, Peipei, Yuan, Jinlong, Zhou, Yunfeng, Mei, Yaxin, Ye, Mingquan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9631203/
https://www.ncbi.nlm.nih.gov/pubmed/36340761
http://dx.doi.org/10.3389/fnins.2022.1034971
_version_ 1784823768732925952
author Li, Qingqing
Wang, Peipei
Yuan, Jinlong
Zhou, Yunfeng
Mei, Yaxin
Ye, Mingquan
author_facet Li, Qingqing
Wang, Peipei
Yuan, Jinlong
Zhou, Yunfeng
Mei, Yaxin
Ye, Mingquan
author_sort Li, Qingqing
collection PubMed
description An IA is an abnormal swelling of cerebral vessels, and a subset of these IAs can rupture causing aneurysmal subarachnoid hemorrhage (aSAH), often resulting in death or severe disability. Few studies have used an appropriate method of feature selection combined with machine learning by analyzing transcriptomic sequencing data to identify new molecular biomarkers. Following gene ontology (GO) and enrichment analysis, we found that the distinct status of IAs could lead to differential innate immune responses using all 913 differentially expressed genes, and considering that there are numerous irrelevant and redundant genes, we propose a mixed filter- and wrapper-based feature selection. First, we used the Fast Correlation-Based Filter (FCBF) algorithm to filter a large number of irrelevant and redundant genes in the raw dataset, and then used the wrapper feature selection method based on the he Multi-layer Perceptron (MLP) neural network and the Particle Swarm Optimization (PSO), accuracy (ACC) and mean square error (MSE) were then used as the evaluation criteria. Finally, we constructed a novel 10-gene signature (YIPF1, RAB32, WDR62, ANPEP, LRRCC1, AADAC, GZMK, WBP2NL, PBX1, and TOR1B) by the proposed two-stage hybrid algorithm FCBF-MLP-PSO and used different machine learning models to predict the rupture status in IAs. The highest ACC value increased from 0.817 to 0.919 (12.5% increase), the highest area under ROC curve (AUC) value increased from 0.87 to 0.94 (8.0% increase), and all evaluation metrics improved by approximately 10% after being processed by our proposed gene selection algorithm. Therefore, these 10 informative genes used to predict rupture status of IAs can be used as complements to imaging examinations in the clinic, meanwhile, this selected gene signature also provides new targets and approaches for the treatment of ruptured IAs.
format Online
Article
Text
id pubmed-9631203
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-96312032022-11-04 A two-stage hybrid gene selection algorithm combined with machine learning models to predict the rupture status in intracranial aneurysms Li, Qingqing Wang, Peipei Yuan, Jinlong Zhou, Yunfeng Mei, Yaxin Ye, Mingquan Front Neurosci Neuroscience An IA is an abnormal swelling of cerebral vessels, and a subset of these IAs can rupture causing aneurysmal subarachnoid hemorrhage (aSAH), often resulting in death or severe disability. Few studies have used an appropriate method of feature selection combined with machine learning by analyzing transcriptomic sequencing data to identify new molecular biomarkers. Following gene ontology (GO) and enrichment analysis, we found that the distinct status of IAs could lead to differential innate immune responses using all 913 differentially expressed genes, and considering that there are numerous irrelevant and redundant genes, we propose a mixed filter- and wrapper-based feature selection. First, we used the Fast Correlation-Based Filter (FCBF) algorithm to filter a large number of irrelevant and redundant genes in the raw dataset, and then used the wrapper feature selection method based on the he Multi-layer Perceptron (MLP) neural network and the Particle Swarm Optimization (PSO), accuracy (ACC) and mean square error (MSE) were then used as the evaluation criteria. Finally, we constructed a novel 10-gene signature (YIPF1, RAB32, WDR62, ANPEP, LRRCC1, AADAC, GZMK, WBP2NL, PBX1, and TOR1B) by the proposed two-stage hybrid algorithm FCBF-MLP-PSO and used different machine learning models to predict the rupture status in IAs. The highest ACC value increased from 0.817 to 0.919 (12.5% increase), the highest area under ROC curve (AUC) value increased from 0.87 to 0.94 (8.0% increase), and all evaluation metrics improved by approximately 10% after being processed by our proposed gene selection algorithm. Therefore, these 10 informative genes used to predict rupture status of IAs can be used as complements to imaging examinations in the clinic, meanwhile, this selected gene signature also provides new targets and approaches for the treatment of ruptured IAs. Frontiers Media S.A. 2022-10-20 /pmc/articles/PMC9631203/ /pubmed/36340761 http://dx.doi.org/10.3389/fnins.2022.1034971 Text en Copyright © 2022 Li, Wang, Yuan, Zhou, Mei and Ye. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Li, Qingqing
Wang, Peipei
Yuan, Jinlong
Zhou, Yunfeng
Mei, Yaxin
Ye, Mingquan
A two-stage hybrid gene selection algorithm combined with machine learning models to predict the rupture status in intracranial aneurysms
title A two-stage hybrid gene selection algorithm combined with machine learning models to predict the rupture status in intracranial aneurysms
title_full A two-stage hybrid gene selection algorithm combined with machine learning models to predict the rupture status in intracranial aneurysms
title_fullStr A two-stage hybrid gene selection algorithm combined with machine learning models to predict the rupture status in intracranial aneurysms
title_full_unstemmed A two-stage hybrid gene selection algorithm combined with machine learning models to predict the rupture status in intracranial aneurysms
title_short A two-stage hybrid gene selection algorithm combined with machine learning models to predict the rupture status in intracranial aneurysms
title_sort two-stage hybrid gene selection algorithm combined with machine learning models to predict the rupture status in intracranial aneurysms
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9631203/
https://www.ncbi.nlm.nih.gov/pubmed/36340761
http://dx.doi.org/10.3389/fnins.2022.1034971
work_keys_str_mv AT liqingqing atwostagehybridgeneselectionalgorithmcombinedwithmachinelearningmodelstopredicttherupturestatusinintracranialaneurysms
AT wangpeipei atwostagehybridgeneselectionalgorithmcombinedwithmachinelearningmodelstopredicttherupturestatusinintracranialaneurysms
AT yuanjinlong atwostagehybridgeneselectionalgorithmcombinedwithmachinelearningmodelstopredicttherupturestatusinintracranialaneurysms
AT zhouyunfeng atwostagehybridgeneselectionalgorithmcombinedwithmachinelearningmodelstopredicttherupturestatusinintracranialaneurysms
AT meiyaxin atwostagehybridgeneselectionalgorithmcombinedwithmachinelearningmodelstopredicttherupturestatusinintracranialaneurysms
AT yemingquan atwostagehybridgeneselectionalgorithmcombinedwithmachinelearningmodelstopredicttherupturestatusinintracranialaneurysms
AT liqingqing twostagehybridgeneselectionalgorithmcombinedwithmachinelearningmodelstopredicttherupturestatusinintracranialaneurysms
AT wangpeipei twostagehybridgeneselectionalgorithmcombinedwithmachinelearningmodelstopredicttherupturestatusinintracranialaneurysms
AT yuanjinlong twostagehybridgeneselectionalgorithmcombinedwithmachinelearningmodelstopredicttherupturestatusinintracranialaneurysms
AT zhouyunfeng twostagehybridgeneselectionalgorithmcombinedwithmachinelearningmodelstopredicttherupturestatusinintracranialaneurysms
AT meiyaxin twostagehybridgeneselectionalgorithmcombinedwithmachinelearningmodelstopredicttherupturestatusinintracranialaneurysms
AT yemingquan twostagehybridgeneselectionalgorithmcombinedwithmachinelearningmodelstopredicttherupturestatusinintracranialaneurysms