Johan

Wang, Ding; Tan, Pang-Ning

doi:10.1145/3447548.3467400

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…The nuisance terms E [ Y | X ], E [ T | Z,X ] and E [ T | X ] can be estimated from an independent training set using any supervised learning models. In our application, we applied the DRIV algorithm implemented by EconML package to estimate the ITEs 17 . Since finding a separate dataset is usually difficult, the above terms are estimated based on cross-validation (CV) to avoid overfitting, following EconML’s implementation.…”

Section: Methodsmentioning

confidence: 99%

A framework for detecting causal effects of risk factors at an individual level based on principles of Mendelian randomization: Applications to modelling individualized effects of lipids on coronary artery disease

Shi,

Xiang,

et al. 2024

Preprint

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Mendelian Randomization (MR), a method that employs genetic variants as instruments for causal inference, has gained popularity in assessing the causal effects of risk factors. However, almost all MR studies primarily concentrate on the population's average causal effects. With the advent of precision medicine, the individualized treatment effect (ITE) is often of greater interest. For instance, certain risk factors may pose a higher risk to some individuals compared to others, and the benefits of a treatment may vary among individuals. This highlights the importance of considering individual differences in risk and treatment response. We propose a new framework that expands the concept of MR to investigate individualized causal effects. We presented several approaches for estimating Individualized Treatment Effects (ITEs) within this MR framework, primarily grounded on the principles of the "R-learner". To evaluate the existence of causal effect heterogeneity, we proposed two permutation testing methods. We employed Polygenic Risk Scores (PRS) as the instrument and demonstrated that the removal of potentially pleiotropic SNPs could enhance the accuracy of ITE estimates. The validity of our approach was substantiated through comprehensive simulations. We applied our framework to study the individualized causal effect of various lipid traits, including Low-Density Lipoprotein Cholesterol (LDL-C), High-Density Lipoprotein Cholesterol (HDL-C), Triglycerides (TG), and Total Cholesterol (TC), on the risk of Coronary Artery Disease (CAD) using data from the UK Biobank. Our findings indicate that an elevated level of LDL-C is causally linked to increased CAD risks, with the effect demonstrating significant heterogeneity. Similar results were observed for TC. We also revealed clinical factors contributing to the heterogeneity of ITE based on Shapley value analysis. Furthermore, we identified clinical factors contributing to the heterogeneity of ITEs through Shapley value analysis. This underscores the importance of individualized treatment plans in managing CAD risks.

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Section: Methodsmentioning

confidence: 99%

A framework for detecting causal effects of risk factors at an individual level based on principles of Mendelian randomization: Applications to modelling individualized effects of lipids on coronary artery disease

Shi,

Xiang,

et al. 2024

Preprint

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show abstract

“…Generally, heterogeneous networks face certain difficulties in the representation learning of nodes due to their complicated structure [ 39 ]. Hence, is one of the great challenges to design an automated learning framework to fully explore the complex meta-path-based relationships over heterogeneous graphs for embedding learning [ 40 ].…”

Section: Introductionmentioning

confidence: 99%

MGCNSS: miRNA–disease association prediction with multi-layer graph convolution and distance-based negative sample selection strategy

Tian,

Han,

et al. 2024

Briefings in Bioinformatics

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Identifying disease-associated microRNAs (miRNAs) could help understand the deep mechanism of diseases, which promotes the development of new medicine. Recently, network-based approaches have been widely proposed for inferring the potential associations between miRNAs and diseases. However, these approaches ignore the importance of different relations in meta-paths when learning the embeddings of miRNAs and diseases. Besides, they pay little attention to screening out reliable negative samples which is crucial for improving the prediction accuracy. In this study, we propose a novel approach named MGCNSS with the multi-layer graph convolution and high-quality negative sample selection strategy. Specifically, MGCNSS first constructs a comprehensive heterogeneous network by integrating miRNA and disease similarity networks coupled with their known association relationships. Then, we employ the multi-layer graph convolution to automatically capture the meta-path relations with different lengths in the heterogeneous network and learn the discriminative representations of miRNAs and diseases. After that, MGCNSS establishes a highly reliable negative sample set from the unlabeled sample set with the negative distance-based sample selection strategy. Finally, we train MGCNSS under an unsupervised learning manner and predict the potential associations between miRNAs and diseases. The experimental results fully demonstrate that MGCNSS outperforms all baseline methods on both balanced and imbalanced datasets. More importantly, we conduct case studies on colon neoplasms and esophageal neoplasms, further confirming the ability of MGCNSS to detect potential candidate miRNAs. The source code is publicly available on GitHub https://github.com/15136943622/MGCNSS/tree/master

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When Self-attention and Topological Structure Make a Difference: Trajectory Modeling in Road Networks

Zhu

Shu

Chen

et al. 2023

Lecture Notes in Computer Science

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Johan

Cited by 4 publications

References 17 publications

A framework for detecting causal effects of risk factors at an individual level based on principles of Mendelian randomization: Applications to modelling individualized effects of lipids on coronary artery disease

A framework for detecting causal effects of risk factors at an individual level based on principles of Mendelian randomization: Applications to modelling individualized effects of lipids on coronary artery disease

MGCNSS: miRNA–disease association prediction with multi-layer graph convolution and distance-based negative sample selection strategy

When Self-attention and Topological Structure Make a Difference: Trajectory Modeling in Road Networks

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