Machine learning approach to single nucleotide polymorphism-based asthma prediction

Gaudillo, Joverlyn; Rodriguez, Jae Joseph Russell B.; Nazareno, Allen L.; Baltazar, Lei Rigi Pastor; Vilela, Julianne; Bulalacao, Rommel; Domingo, Mario; Albia, Jason R.

doi:10.1371/journal.pone.0225574

Cited by 44 publications

(21 citation statements)

References 23 publications

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“…Support vector machine is among the robust ML algorithms used for classification and regression ( Ghandi et al, 2014 ; Gaudillo et al, 2019 ). It searches for the optimal hyperplane with maximized margins using support vectors for classification ( Ben-Hur et al, 2008 ).…”

Section: Methodsmentioning

confidence: 99%

DeePred-BBB: A Blood Brain Barrier Permeability Prediction Model With Improved Accuracy

et al. 2022

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The blood-brain barrier (BBB) is a selective and semipermeable boundary that maintains homeostasis inside the central nervous system (CNS). The BBB permeability of compounds is an important consideration during CNS-acting drug development and is difficult to formulate in a succinct manner. Clinical experiments are the most accurate method of measuring BBB permeability. However, they are time taking and labor-intensive. Therefore, numerous efforts have been made to predict the BBB permeability of compounds using computational methods. However, the accuracy of BBB permeability prediction models has always been an issue. To improve the accuracy of the BBB permeability prediction, we applied deep learning and machine learning algorithms to a dataset of 3,605 diverse compounds. Each compound was encoded with 1,917 features containing 1,444 physicochemical (1D and 2D) properties, 166 molecular access system fingerprints (MACCS), and 307 substructure fingerprints. The prediction performance metrics of the developed models were compared and analyzed. The prediction accuracy of the deep neural network (DNN), one-dimensional convolutional neural network, and convolutional neural network by transfer learning was found to be 98.07, 97.44, and 97.61%, respectively. The best performing DNN-based model was selected for the development of the “DeePred-BBB” model, which can predict the BBB permeability of compounds using their simplified molecular input line entry system (SMILES) notations. It could be useful in the screening of compounds based on their BBB permeability at the preliminary stages of drug development. The DeePred-BBB is made available at https://github.com/12rajnish/DeePred-BBB.

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Section: Methodsmentioning

confidence: 99%

DeePred-BBB: A Blood Brain Barrier Permeability Prediction Model With Improved Accuracy

et al. 2022

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“…Machine learning (ML) is an innovative and powerful approach used in solving complex problems in various elds and disciplines due to its capability to handle and analyze high-dimensional datasets [22,23,24]. Several studies have already demonstrated the usability of ML in genomic datasets [25,26,27]; however, to our knowledge, there is only a handful of existing literature discussing its application to SNPset formation [28,29,30,31]. These studies employed cluster analysis to form SNP-sets in a data-driven manner.…”

Section: Introductionmentioning

confidence: 99%

“…For a more varied selection of SNPs to analyze, dimensionality reduction techniques based on random forest (RF) could be used to reduce dataset dimensions before conducting cluster analysis. RF has been widely incorporated in SNP research [25,34,35,36] due to its signi cant properties: (1) a nonparametric nature that allows the establishment of predictive models without the need for preliminary statistical assumptions, and (2) the capability to provide an importance score, i.e. variable importance measure (VIM) for each SNP, which increases the probability of detecting highly relevant biomarkers.…”

Section: Introductionmentioning

confidence: 99%

A machine learning-based SNP-set analysis approach for identifying disease-associated susceptibility loci

Silva

Gaudillo

Vilela

et al. 2022

Preprint

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Background Identifying disease-associated susceptibility loci is one of the most pressing and crucial challenges in modeling complex diseases. Existing approaches to biomarker discovery are subject to several limitations including underpowered detection, neglect for variant interactions, and restrictive dependence on prior biological knowledge. Addressing these challenges necessitates more ingenious ways of approaching the “missing heritability” problem. Objectives This study aims to discover disease-associated susceptibility loci by augmenting previous genome-wide association study (GWAS) using the integration of random forest and cluster analysis. Methods The proposed integrated framework is applied to a hepatitis B virus surface antigen (HBsAg) seroclearance GWAS data. Multiple cluster analyses were performed on (1) single nucleotide polymorphisms (SNPs) considered significant by GWAS and (2) SNPs with the highest feature importance scores obtained using random forest. The resulting SNP-sets from the cluster analyses were subsequently tested for trait-association. Results Three susceptibility loci possibly associated with HBsAg seroclearance were identified: (1) SNP rs2399971, (2) gene LINC00578, and (3) locus 11p15. SNP rs2399971 is a biomarker reported in the literature to be significantly associated with HBsAg seroclearance in patients who had received antiviral treatment. The latter two loci are linked with diseases influenced by the presence of hepatitis B virus infection. Conclusion These findings demonstrate the potential of the proposed integrated framework in identifying disease-associated susceptibility loci. With further validation, results herein could aid in better understanding complex disease etiologies and provide inputs for a more advanced disease risk assessment for patients.

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“…The advent of inexpensive whole genome sequencing methods in recent years has led to the creation of supervised machine learning approaches for predicting putative genetic variants from sequence data. Machine learning methods can effectively investigate the entire genome and provide insight into the subset of variants most likely to influence a particular phenotype, which is particularly useful for disorders or traits with complex, non-Mendelian inheritance patterns [1][2][3][4][5]. Since the high dimensionality of variant feature sets paired with a comparatively low number of training samples tends to result in model overfitting, feature selection methods are often used to narrow the genomic search space and improve model generalizability.…”

Section: Introductionmentioning

confidence: 99%

A maximum flow-based network approach for identification of stable noncoding biomarkers associated with the multigenic neurological condition, autism

et al. 2021

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Background Machine learning approaches for predicting disease risk from high-dimensional whole genome sequence (WGS) data often result in unstable models that can be difficult to interpret, limiting the identification of putative sets of biomarkers. Here, we design and validate a graph-based methodology based on maximum flow, which leverages the presence of linkage disequilibrium (LD) to identify stable sets of variants associated with complex multigenic disorders. Results We apply our method to a previously published logistic regression model trained to identify variants in simple repeat sequences associated with autism spectrum disorder (ASD); this L1-regularized model exhibits high predictive accuracy yet demonstrates great variability in the features selected from over 230,000 possible variants. In order to improve model stability, we extract the variants assigned non-zero weights in each of 5 cross-validation folds and then assemble the five sets of features into a flow network subject to LD constraints. The maximum flow formulation allowed us to identify 55 variants, which we show to be more stable than the features identified by the original classifier. Conclusion Our method allows for the creation of machine learning models that can identify predictive variants. Our results help pave the way towards biomarker-based diagnosis methods for complex genetic disorders.

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Machine learning approach to single nucleotide polymorphism-based asthma prediction

Cited by 44 publications

References 23 publications

DeePred-BBB: A Blood Brain Barrier Permeability Prediction Model With Improved Accuracy

DeePred-BBB: A Blood Brain Barrier Permeability Prediction Model With Improved Accuracy

A machine learning-based SNP-set analysis approach for identifying disease-associated susceptibility loci

A maximum flow-based network approach for identification of stable noncoding biomarkers associated with the multigenic neurological condition, autism

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