The comparison of machine learning methods for prediction study of type 2 diabetes mellitus’s drug design

Husna, Nadya Asanul; Bustamam, Alhadi; Yanuar, Arry; Sarwinda, Devvi; Hermansyah, Oky

doi:10.1063/5.0024161

Cited by 8 publications

(3 citation statements)

References 8 publications

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“…The significant performance improvement of TIVAN-indel may be attributed to two reasons. First, XGBoost, adopted by TIVAN-indel, has been demonstrated to be more powerful than conventional logistic regression ( Dwidarma et al , 2021 ) and SVM ( Husna et al , 2020 ). Second, TIVAN-indel leverages the strength of both CADD annotations and large-scale epigenomic profiles.…”

Section: Resultsmentioning

confidence: 99%

TIVAN-indel: a computational framework for annotating and predicting non-coding regulatory small insertions and deletions

et al. 2023

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Motivation Small insertion and deletion (sindel) of human genome has an important implication for human disease. One important mechanism for noncoding sindel to have an impact on human diseases and phenotypes is through the regulation of gene expression. Nevertheless, current sequencing experiments may lack statistical power and resolution to pinpoint the functional sindel due to lower minor allele frequency or small effect size. As an alternative strategy, a supervised machine learning method can identify the otherwise masked functional sindels by predicting their regulatory potential directly. However, computational methods for annotating and predicting the regulatory sindels, especially in the noncoding regions, are underdeveloped. Results By leveraging labelled noncoding sindels identified by cis-expression quantitative trait loci (cis-eQTLs) analyses across 44 tissues in GTEx, and a compilation of both generic functional annotations and large-scale epigenomic profiles, we develop TIVAN-indel, which is a supervised computational framework for predicting noncoding regulatory sindels. As a result, we demonstrate that TIVAN-indel achieves the best prediction performance in both with-tissue prediction and cross-tissue prediction. As an independent evaluation, we train TIVAN-indel from the “Whole Blood” tissue in GTEx and test the model using 15 immune cell types from an independent study named DICE. Lastly, we perform an enrichment analysis for both true and predicted sindels in key regulatory regions such as chromatin interactions, open chromatin regions and histone modification sites, and find biologically meaningful enrichment patterns. Availability https://github.com/lichen-lab/TIVAN-indel Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Resultsmentioning

confidence: 99%

TIVAN-indel: a computational framework for annotating and predicting non-coding regulatory small insertions and deletions

et al. 2023

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“…The significant improvement of TIVAN-indel may be attributed to two factors. First, XGBoost, adopted by TIVAN-indel, has been demonstrated to be more powerful than conventional logistic regression [25] and SVM [26]. Second, TIVAN-indel considers both CADD functional annotations and tissue/cell type-specific multi-omics features.…”

Section: Resultsmentioning

confidence: 99%

TIVAN-indel: A computational framework for annotating and predicting noncoding regulatory small insertion and deletion

Chen

Agarwal

2022

Preprint

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Motivation: Small insertion and deletion (sindel) of human genome has an important implication for human disease. One important mechanism for noncoding sindel to have an impact on human diseases and phenotypes is through the regulation of gene expression. Nevertheless, current sequencing technology may lack statistical power and resolution to pinpoint the causal sindel due to lower minor allele frequency or small effect. As an alternative solution, a supervised machine learning method can identify the otherwise missing causal sindels by predicting the regulatory potential of sindels directly. However, computational methods for annotating and predicting the regulatory sindels, especially in the noncoding regions, are underdeveloped. Results: By leveraging recognized sindels in cis-expression quantitative trait loci (cis-eQTLs) across 44 tissues and cell types in GTEx, and a compilation of both generic functional annotations and tissue/cell type- specific multi-omics features generated by a sequence-based deep learning model, we developed TIVAN-indel, which is an XGBoost-based supervised framework for scoring noncoding sindels based their potential to regulate the nearby gene expression. As a result, we demonstrate that TIVAN-indel achieves the best pre- diction performance in both cross-validation with-tissue prediction and independent cross-tissue evaluation. As an independent evaluation, we train TIVAN-indel from Whole Blood tissue in GTEx data and test the model using 15 immune cell types from an independent study DICE. Lastly, we perform an enrichment analysis for both recognized and predicted sindels in key regulatory regions such as chromatin interactions, open chromatin and histone modification sites, and find biologically meaningful enrichment patterns.

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“…A well-known implementation (with over 5,500 citations as of November 2021) is eXtreme Gradient Boosting (XGBoost) [24] , which reformulates the algorithm to provide stronger regularization and improved protection against over-fitting. In chemistry, its applications have been diverse: XGBoost has been used to predict the adsorption energy of noble gases to Metal-Organic Frameworks (MOFs) [25] , biological activity of pharmaceuticals [26] , atmospheric transport [27] , and has even been combined with the representations found in Graph Neural Networks (GNNs) to generate accurate models of various molecular properties [28] .…”

Section: Introductionmentioning

confidence: 99%

Interpretable Machine Learning for Materials Design

James¹,

Scheffler²,

Purcell³

et al. 2021

Preprint

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Fueled by the widespread adoption of Machine Learning and the high-throughput screening of materials, the data-centric approach to materials design has asserted itself as a robust and powerful tool for the in-silico prediction of materials properties. When training models to predict material properties, researchers often face a difficult choice between a model's interpretability or its performance. We study this trade-off by leveraging four different state-of-the-art Machine Learning techniques: XGBoost, SISSO, Roost, and TPOT for the prediction of structural and electronic properties of perovskites and 2D materials. We then assess the future outlook of the continued integration of Machine Learning into materials discovery, and identify key problems that will continue to challenge researchers as the size of the literature's datasets and complexity of models increases. Finally, we offer several possible solutions to these challenges with a focus on retaining interpretability, and share our thoughts on magnifying the impact of Machine Learning on materials design.

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The comparison of machine learning methods for prediction study of type 2 diabetes mellitus’s drug design

Cited by 8 publications

References 8 publications

TIVAN-indel: a computational framework for annotating and predicting non-coding regulatory small insertions and deletions

TIVAN-indel: a computational framework for annotating and predicting non-coding regulatory small insertions and deletions

TIVAN-indel: A computational framework for annotating and predicting noncoding regulatory small insertion and deletion

Interpretable Machine Learning for Materials Design

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