Siting Li
Dartmouth College
5 Papers
Siting Li is an academic researcher from Dartmouth College. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 1, co-authored 1 publications.
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Papers
An unbiased kinship estimation method for genetic data analysis
TL;DR: UKin this article proposed an unbiased estimation method, UKin, which can reduce kinship estimation bias by using the observed allele frequencies to calculate both the expectations and variances of genotypes.
Abstract P038: Transcriptome-wide association study identifies novel genes associated with bladder cancer risk
TL;DR: Li et al. as discussed by the authors used PrediXcan to predict gene expression in whole blood using reference data from the Depression Genes and Networks (DGN), and used a logistic regression to estimate the associations between gene expression and bladder cancer risk.
Research Progress on the Relationship Between Periodontitis and Hypertension
TL;DR: In this paper , the authors reviewed the research progress of the relationship between periodontitis and hypertension from the aspects of the correlation between periods and hypertension, and the related mechanism between them.
Adaptive-mixture-categorization (AMC)-based g-computation and its application to trace element mixtures and bladder cancer risk
TL;DR: In this paper , an adaptive mixture-categorization (AMC)-based g-computation approach was proposed to assess the association between a mixture of 12 trace element concentrations measured from toenails and the risk of nonmuscle invasive bladder cancer.
A new efficient method to detect genetic interactions for lung cancer GWAS
Jennifer Luyapan,Xuemei Ji,Siting Li,Xiangjun Xiao,Dakai Zhu,Dakai Zhu,Eric J. Duell,David C. Christiani,Matthew B. Schabath,Susanne M. Arnold,Shanbeh Zienolddiny,Hans Brunnström,Olle Melander,Mark D. Thornquist,Todd A. MacKenzie,Christopher I. Amos,Christopher I. Amos,Jiang Gui +17 more
TL;DR: This work developed a novel algorithm, called the Efficient Survival Multifactor Dimensionality Reduction (ES-MDR) method, which used Martingale Residuals as the outcome parameter to estimate survival outcomes, and implemented the Quantitative Multifact Dimensionality reduction method to identify significant interactions associated with age of disease-onset.