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1.
《Journal of pharmaceutical sciences》2019,108(11):3630-3639
Absorption of drugs is the first step after dosing, and it largely affects drug bioavailability. Hence, estimating the fraction of absorption (Fa) in humans is important in the early stages of drug discovery. To achieve correct exclusion of low Fa compounds and retention of potential compounds, we developed a freely available model to classify compounds into 3 levels of Fa capacity using only the chemical structure. To improve Fa prediction, we added predicted binary classification results of membrane permeability measured using Caco-2 cell line (Papp) and dried–dimethyl sulfoxide solubility (accuracy, 0.836; kappa, 0.560). The constructed models can be accessed via a web application. 相似文献
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Advanced technology in whole-genome sequencing has offered the opportunity to comprehensively investigate the genetic contribution, particularly rare variants, to complex traits. Several region-based tests have been developed to jointly model the marginal effect of rare variants, but methods to detect gene-environment (GE) interactions are underdeveloped. Identifying the modification effects of environmental factors on genetic risk poses a considerable challenge. To tackle this challenge, we develop a method to detect GE interactions for rare variants using generalized linear mixed effect model. The proposed method can accommodate either binary or continuous traits in related or unrelated samples. Under this model, genetic main effects, GE interactions, and sample relatedness are modeled as random effects. We adopt a kernel-based method to leverage the joint information across rare variants and implement variance component score tests to reduce the computational burden. Our simulation studies of continuous and binary traits show that the proposed method maintains correct type I error rates and appropriate power under various scenarios, such as genotype main effects and GE interaction effects in opposite directions and varying the proportion of causal variants in the model. We apply our method in the Framingham Heart Study to test GE interaction of smoking on body mass index or overweight status and replicate the Cholinergic Receptor Nicotinic Beta 4 gene association reported in previous large consortium meta-analysis of single nucleotide polymorphism-smoking interaction. Our proposed set-based GE test is computationally efficient and is applicable to both binary and continuous phenotypes, while appropriately accounting for familial or cryptic relatedness. 相似文献
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Many expression quantitative trait loci (eQTL) studies have been conducted to investigate the biological effects of variants in gene regulation. However, these eQTL studies may suffer from low or moderate statistical power and overly conservative false-discovery rate. In practice, most algorithms for eQTL identification do not model the joint effects of multiple genetic variants with weak or moderate influence. Here we present a novel machine-learning algorithm, lasso least-squares kernel machine (LSKM-LASSO) that model the association between multiple genetic variants and phenotypic traits simultaneously with the existence of nongenetic and genetic confounding. With a more general and flexible framework for the estimation of genetic confounding, LSKM-LASSO is able to provide a more accurate evaluation of the joint effects of multiple genetic variants. Our simulations demonstrate that our approach outperforms three state-of-the-art alternatives in terms of eQTL identification and phenotype prediction. We then apply our method to genotype and gene expression data of 11 tissues obtained from the Genotype-Tissue Expression project. Our algorithm was able to identify more genes with eQTL than other algorithms. By incorporating a regularization term and combining it with least-squares kernel machine, LSKM-LASSO provides a powerful tool for eQTL mapping and phenotype prediction. 相似文献
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目的:利用蒙特卡罗方法分析透射平面上散射光子的物理性质以及非均匀模体厚度对散射核的影响,为基于电子射野影像设备(EPID)的在体剂量验证研究提供基础。方法:利用EGSnrc建立笔形束散射核模型,并模拟获得X射线穿过非均匀模体(水肺水/水骨水模体)以及相应等效厚度水模后30 cm处透射平面上的多种散射线能量注量分布,并分析水肺水/水骨水模体与其等效厚度水模体在散射线能量注量分布上的差异。结果:散射核中一阶康普顿散射线最大能量注量在1×10-4 MeV·cm-2数量级,当离轴距离为8~12 cm时下降至最大值的一半,而散射核中其它散射线能量注量最大值在1×10-5 MeV·cm-2数量级附近或以下。对于水肺水/水骨水模体,散射核能量注量相对偏差变化为±1.2%~±11.5%,且随模体非均匀层厚度增大而增大。结论:散射核中一阶康普顿散射线占比最大,同时也贡献了大部分能量注量相对偏差,在通过散射核来重建非均匀模体后EPID平面上的射线分布时,应着重考虑一阶康普顿散射线对重建结果的影响,并对其进行有效的修正。 相似文献
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Huifang Huang Xingdan Liu Yan Jin Seong‐Whan Lee Chong‐Yaw Wee Dinggang Shen 《Human brain mapping》2019,40(3):833-854
Functional connectivity network provides novel insights on how distributed brain regions are functionally integrated, and its deviations from healthy brain have recently been employed to identify biomarkers for neuropsychiatric disorders. However, most of brain network analysis methods utilized features extracted only from one functional connectivity network for brain disease detection and cannot provide a comprehensive representation on the subtle disruptions of brain functional organization induced by neuropsychiatric disorders. Inspired by the principles of multi‐view learning which utilizes information from multiple views to enhance object representation, we propose a novel multiple network based framework to enhance the representation of functional connectivity networks by fusing the common and complementary information conveyed in multiple networks. Specifically, four functional connectivity networks corresponding to the four adjacent values of regularization parameter are generated via a sparse regression model with group constraint ( l2,1 ‐norm), to enhance the common intrinsic topological structure and limit the error rate caused by different views. To obtain a set of more meaningful and discriminative features, we propose using a modified version of weighted clustering coefficients to quantify the subtle differences of each group‐sparse network at local level. We then linearly fuse the selected features from each individual network via a multi‐kernel support vector machine for autism spectrum disorder (ASD) diagnosis. The proposed framework achieves an accuracy of 79.35%, outperforming all the compared single network methods for at least 7% improvement. Moreover, compared with other multiple network methods, our method also achieves the best performance, that is, with at least 11% improvement in accuracy. 相似文献
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选用了一些药物对离体蟾蜍心脏进行灌流,观察对离体心脏的影响,能否用在临床上,尚待作更多生理和药理方面的研究。 相似文献
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Background The classification of Alzheimer's disease (AD) from magnetic resonance imaging (MRI) has been challenged by lack of effective and reliable biomarkers due to inter-subject variability.This ar... 相似文献
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利用小波支持向量回归,实现了遥感多光谱图像分辨率的增强。首先采用非下采样Contourlet变换对低分辨率的多光谱图像和高分辨率的全色图像进行多分辨率分解,再利用小波支持向量回归对分解系数进行学习和预测,获得分辨率初步提高的多光谱图像,最后再与传统的插值方法得到的结果进行融合来实现多光谱图像分辨率增强。实验结果表明:此方法借遥感全色图像的辅助获得丰富的高频细节信息,使得分辨率增强结果无论是最小均方误差还是峰值信噪比都要优于仅依靠原图像本身放大的传统方法以及其他的分辨率增强方法。 相似文献