GASVeM: A New Machine Learning Methodology for Multi-SNP Analysis of GWAS Data Based on Genetic Algorithms and Support Vector Machines

Díaz, Fidel Díez; Lasheras, Fernando Sánchez; Moratalla-Navarro, Ferrán; Torre, Antonio José Molina de la; Sánchez, Vicente Martín

doi:10.3390/math9060654

Cited by 8 publications

(21 citation statements)

References 59 publications

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“…For the mutation, the value of 1% was chosen; for the crossover, it was 100%, and for elitism, it was 5%. Please note that these values have shown good performance in previous research studies by the authors [44, 45]. The classification version of SVM was applied in this algorithm, using the radial basis function kernel and a gamma value equal to the inverse of the number of input variables of the model.…”

Section: Methodsmentioning

confidence: 96%

PsiOvi Staging Model for Schizophrenia (PsiOvi SMS): A New Internet Tool for Staging Patients with Schizophrenia

Martínez-Cao,

Sánchez-Lasheras,

García-Fernández

et al. 2024

Eur. Psychiatr.

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Background. One of the challenges of psychiatry is the staging of patients, especially those with severe mental disorders. Therefore, we aim to develop an empirical staging model for schizophrenia. Methods. Data were obtained from 212 stable outpatients with schizophrenia: demographic, clinical, psychometric (PANSS, CAINS, CDSS, OSQ, CGI-S, PSP, MATRICS), inflammatory peripheral blood markers (C-reactive protein, interleukins-1RA and 6, and platelet/lymphocyte [PLR], neutrophil/lymphocyte [NLR], and monocyte/lymphocyte [MLR] ratios). We used machine learning techniques to develop the model (genetic algorithms, support vector machines) and applied a fitness function to measure the model's accuracy (% agreement between patient classification of our model and the CGI-S). Results. Our model includes 12 variables from 5 dimensions: 1) psychopathology: positive, negative, depressive, general psychopathology symptoms; 2) clinical features: number of hospitalizations; 3) cognition: processing speed, visual learning, social cognition; 4) biomarkers: PLR, NLR, MLR; and 5) functioning: PSP total score. Accuracy was 62% (SD = 5.3), and sensitivity values were appropriate for mild, moderate, and marked severity (from 0.62106 to 0.6728). Discussion. We present a multidimensional, accessible, and easy-to-apply model that goes beyond simply categorizing patients according to CGI-S score. It provides clinicians with a multifaceted patient profile that facilitates the design of personalized intervention plans.

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Section: Methodsmentioning

confidence: 96%

PsiOvi Staging Model for Schizophrenia (PsiOvi SMS): A New Internet Tool for Staging Patients with Schizophrenia

Martínez-Cao,

Sánchez-Lasheras,

García-Fernández

et al. 2024

Eur. Psychiatr.

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“…Moreover, improved machine learning techniques have been used for action recognition from collaborative learning networks [59], for the automatic recognition and classification of ECG and EEG signals [60][61][62], for complex processing on images [63], for health monitoring systems using IoT-based techniques [64], and several others works. In [65], a support vector machine is employed as a novel methodology to compute the genetic algorithm's fitness. A similar work can be seen in [66].…”

Section: Related Workmentioning

confidence: 99%

A Knowledge-Based Hybrid Approach on Particle Swarm Optimization Using Hidden Markov Models

et al. 2021

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Bio-inspired computing is an engaging area of artificial intelligence which studies how natural phenomena provide a rich source of inspiration in the design of smart procedures able to become powerful algorithms. Many of these procedures have been successfully used in classification, prediction, and optimization problems. Swarm intelligence methods are a kind of bio-inspired algorithm that have been shown to be impressive optimization solvers for a long time. However, for these algorithms to reach their maximum performance, the proper setting of the initial parameters by an expert user is required. This task is extremely comprehensive and it must be done in a previous phase of the search process. Different online methods have been developed to support swarm intelligence techniques, however, this issue remains an open challenge. In this paper, we propose a hybrid approach that allows adjusting the parameters based on a state deducted by the swarm intelligence algorithm. The state deduction is determined by the classification of a chain of observations using the hidden Markov model. The results show that our proposal exhibits good performance compared to the original version.

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“…Li et al constructed a neural network model using magnetic resonance imaging (MRI) images and used transfer learning to train the constructed model, demonstrating for the first time that non-invasive MRI is related to the development of AD ( 10 ). Fidel et al used genetic algorithm and support vector machine (SVM) to screen 370,750 SNPs and obtained the pathways related to colorectal cancer ( 11 ). Sun et al proposed a multi-layer deep neural network survival model and compared the survival model based on classical machine learning.…”

Section: Introductionmentioning

confidence: 99%

Research on Pathogenic Hippocampal Voxel Detection in Alzheimer's Disease Using Clustering Genetic Random Forest

et al. 2022

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Alzheimer's disease (AD) is an age-related neurological disease, which is closely associated with hippocampus, and subdividing the hippocampus into voxels can capture subtle signals that are easily missed by region of interest (ROI) methods. Therefore, studying interpretable associations between voxels can better understand the effect of voxel set on the hippocampus and AD. In this study, by analyzing the hippocampal voxel data, we propose a novel method based on clustering genetic random forest to identify the important voxels. Specifically, we divide the left and right hippocampus into voxels to constitute the initial feature set. Moreover, the random forest is constructed using the randomly selected samples and features. The genetic evolution is used to amplify the difference in decision trees and the clustering evolution is applied to generate offspring in genetic evolution. The important voxels are the features that reach the peak classification. The results demonstrate that our method has good classification and stability. Particularly, through biological analysis of the obtained voxel set, we find that they play an important role in AD by affecting the function of the hippocampus. These discoveries demonstrate the contribution of the voxel set to AD.

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GASVeM: A New Machine Learning Methodology for Multi-SNP Analysis of GWAS Data Based on Genetic Algorithms and Support Vector Machines

Cited by 8 publications

References 59 publications

PsiOvi Staging Model for Schizophrenia (PsiOvi SMS): A New Internet Tool for Staging Patients with Schizophrenia

PsiOvi Staging Model for Schizophrenia (PsiOvi SMS): A New Internet Tool for Staging Patients with Schizophrenia

A Knowledge-Based Hybrid Approach on Particle Swarm Optimization Using Hidden Markov Models

Research on Pathogenic Hippocampal Voxel Detection in Alzheimer's Disease Using Clustering Genetic Random Forest

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