Explaining the Genetic Causality for Complex Phenotype via Deep Association Kernel Learning

Bao, Feng; Deng, Yue; Du, Mulong; Ren, Zhiquan; Wan, Sen; Liang, Kenny Ye; Liu, Shaohua; Wang, Bo; Xin, Junyi; Chen, Feng; Christiani, David C.; Wang, Meilin; Dai, Qionghai

doi:10.1016/j.patter.2020.100057

Cited by 8 publications

(5 citation statements)

References 48 publications

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“…Such tools usually adapt complicated machine learning models that may consider nonlinear analysis as well as causality assumptions or inference (Muneeb et al, 2022;Meijering and Gianola, 1985;Sailer and Harms, 2017;Bao et al, 2020;Basu et al, 2018;Lee et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…These data exposed the challenges of handling correlations between in-between-ome terms (e.g., co-expressions in the transcriptome), however they also provide opportunities (Wainberg et al ., 2019) These data have triggered development of sophisticated tools leveraging in-between-omes to characterize the genetic basis of complex traits. Such tools usually adapt complicated machine learning models that may consider nonlinear analysis as well as causality assumptions or inference (Muneeb et al ., 2022; Meijering and Gianola, 1985; Sailer and Harms, 2017; Bao et al ., 2020; Basu et al ., 2018; Lee et al ., 2016). However, there are no standard simulators to benchmark the performance of the newly developed tools, leaving authors to develop different ad hoc simulations tailoring to their works.…”

Section: Introductionmentioning

confidence: 99%

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OmeSim: a genetics-based nonlinear simulator for in-between-ome and phenotype

Long,

Zhang

2024

Preprint

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MotivationDeciphering genetic basis of complex traits via genotype-phenotype association studies is a long-standing theme in genetics. The availability of molecular omics data (such as transcriptome) has enabled researchers to utilize “in-between-omes” in association studies, for instance transcriptome-wide association study. Although many statistical tests and machine learning models integrating omics in genetic mapping are emerging, there is no standard way to simulate phenotype by genotype with the role of in-between-omes incorporated. Moreover, the involvement of in-between-omes usually bring substantial nonlinear architecture (e.g., co-expression network), that may be non-trivial to simulate. As such, rigorous power estimations, a critical step to test novel models, may not be conducted fairly.ResultsTo address the gap between emerging methods development and the unavailability of adequate simulators, we developed OmeSim, a phenotype simulator incorporating genetics, an in-between-ome (e.g., transcriptome), and their complex relationships including nonlinear architectures. OmeSim outputs detailed causality graphs together with original data, correlations, and associations structures between phenotypic traits and omes terms as comprehensive gold-standard datasets for the verifications of novel tools integrating an in-between-ome in genotype-phenotype association studies. We expect OmeSim to enable rigorous benchmarking for the future multi-omics integrations.Availabilityhttps://github.com/zhoulongcoding/OmeSimContactqingrun.zhang@ucalgary.ca

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

OmeSim: a genetics-based nonlinear simulator for in-between-ome and phenotype

Long,

Zhang

2024

Preprint

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“… 4 Advanced statistical methods are available for linking genes with phenotypes of interest within a given species, leading to significant breakthroughs in fields as diverse as complex genetic diseases and genetic breeding. 5 , 6 , 7 …”

Section: Introductionmentioning

confidence: 99%

CALANGO: A phylogeny-aware comparative genomics tool for discovering quantitative genotype-phenotype associations across species

Hongo¹,

Castro²,

Menezes³

et al. 2023

Patterns

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“…Disease classification using heterogeneous gene expression data is greatly utilized for determining fundamental issues like disease analysis and drug detection [6]- [7]. The gene expression data collected from DNA micro arrays are characterized through diverse evaluated variables known as genes [8].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Multiple Indefinite Kernel Learning Framework for Disease Classification from Gene Expression Data

Swetha¹,

Srinivasan²,

P³

2023

IJACSA

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In recent years, Machine Learning (ML) techniques have been used by several researchers to classify diseases using gene expression data. Disease categorization using heterogeneous gene expression data is often used for defining critical problems such as cancer analysis. A variety of evaluated factors known as genes are used to characterize the gene expression data gathered from DNA microarrays. Accurate classification of genetic data is essential to provide accurate treatments to sick people. A large number of genes can be viewed simultaneously from the collected data. However, processing this data has some limitations due to noises, redundant data, frequent errors, increased complexity, smaller samples with high dimensionality, difficult interpretation, etc. A model must be able to distinguish the features in such heterogeneous data with high accuracy to make accurate predictions. So this paper presents an innovative model to overcome these issues. The proposed model includes an effective multiple indefinite kernel learning based model for analyze the gene expression microarray data, then an optimized kernel principal component analysis (OKPCA) to select best features and hybrid flow-directed arithmetic support vector machine (SVM)-based multiple infinite kernel learning (FDASVM-MIKL) model for classification. Flow direction and arithmetic optimization algorithms are combined with SVM to increase classification accuracy. The proposed technique has an accuracy of 99.95%, 99.63%, 99.60%, 99.51%, and 99.79% using the datasets including colon, Isolet, ALLAML, Lung_cancer, and Snp2 graph.

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Explaining the Genetic Causality for Complex Phenotype via Deep Association Kernel Learning

Cited by 8 publications

References 48 publications

OmeSim: a genetics-based nonlinear simulator for in-between-ome and phenotype

OmeSim: a genetics-based nonlinear simulator for in-between-ome and phenotype

CALANGO: A phylogeny-aware comparative genomics tool for discovering quantitative genotype-phenotype associations across species

A Hybrid Multiple Indefinite Kernel Learning Framework for Disease Classification from Gene Expression Data

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