Dimension reduction in survival regressions with censored data via an imputed spline approach

Dimension reduction methods have been proposed for regression analysis with predictors of high dimension, but have not received much attention on the problems with censored data. In this article, we present an iterative imputed spline approach based on principal Hessian directions (PHD) for censored survival data in order to reduce the dimension of predictors without requiring a prespecified parametric model. Our proposal is to replace the right-censored survival time with its conditional expectation for adjusting the censoring effect by using the Kaplan-Meier estimator and an adaptive polynomial spline regression in the residual imputation. A sparse estimation strategy is incorporated in our approach to enhance the interpretation of variable selection. This approach can be implemented in not only PHD, but also other methods developed for estimating the central mean subspace. Simulation studies with right-censored data are conducted for the imputed spline approach to PHD (IS-PHD) in comparison with two methods of sliced inverse regression, minimum average variance estimation, and naive PHD in ignorance of censoring. The results demonstrate that the proposed IS-PHD method is particularly useful for survival time responses approximating symmetric or bending structures. Illustrative applications to two real data sets are also presented.     

基于信号稀疏表示的高光谱散射图像苹果粉质化分类

苹果的粉质化是指苹果果肉发软、汁液减少等一系列物理和生理变化现象,采用高光谱散射图像技术结合信号稀疏表示分类算法(SRSA)研究了苹果的粉质化分类问题。首先利用平均反射算法(MEAN)提取了600～1000 nm的高光谱散射图像特征;引入遗传算法(GA)解决分类样本的不均衡问题,在此基础上,把苹果的粉质化分类问题,转化为一个求解待识别样本对于整体训练样本的稀疏表示问题。仿真结果表明,基于信号稀疏表示分类算法的苹果粉质化分类精度为79.8%,高于偏最小二乘判别分析(PLSDA)的74.8%,为苹果的粉质化分类提供了一种新的有效的方法。     

筛选蛋白质结晶条件的试剂盒研发进展

蛋白质结晶条件筛选是蛋白质分子三维结构解析的主要限速环节之一,因此发展筛选效率较高的结晶条件筛选试剂盒有着十分重要的意义.目前广泛应用的结晶条件筛选试剂盒的设计方法主要有不完全因子法、稀疏矩阵法和系统筛选法.结晶学家基于这三种方法研发了一系列用于蛋白质结晶条件筛选的试剂盒.评述了20世纪以来筛选试剂盒的设计方法及应用进展,并展望了其未来的发展方向.     

TOUCHSTONEX: protein structure prediction with sparse NMR data

TOUCHSTONEX, a new method for folding proteins that uses a small number of long-range contact restraints derived from NMR experimental NOE (nuclear Overhauser enhancement) data, is described. The method employs a new lattice-based, reduced model of proteins that explicitly represents C(alpha), C(beta), and the sidechain centers of mass. The force field consists of knowledge-based terms to produce protein-like behavior, including various short-range interactions, hydrogen bonding, and one-body, pairwise, and multibody long-range interactions. Contact restraints were incorporated into the force field as an NOE-specific pairwise potential. We evaluated the algorithm using a set of 125 proteins of various secondary structure types and lengths up to 174 residues. Using N/8 simulated, long-range sidechain contact restraints, where N is the number of residues, 108 proteins were folded to a C(alpha)-root-mean-square deviation (RMSD) from native below 6.5 A. The average RMSD of the lowest RMSD structures for all 125 proteins (folded and unfolded) was 4.4 A. The algorithm was also applied to limited experimental NOE data generated for three proteins. Using very few experimental sidechain contact restraints, and a small number of sidechain-main chain and main chain-main chain contact restraints, we folded all three proteins to low-to-medium resolution structures. The algorithm can be applied to the NMR structure determination process or other experimental methods that can provide tertiary restraint information, especially in the early stage of structure determination, when only limited data are available.     

Automatic generation and evaluation of sparse protein signatures for families of protein structural domains

We identified key residues from the structural alignment of families of protein domains from SCOP which we represented in the form of sparse protein signatures. A signature-generating algorithm (SigGen) was developed and used to automatically identify key residues based on several structural and sequence-based criteria. The capacity of the signatures to detect related sequences from the SWISSPROT database was assessed by receiver operator characteristic (ROC) analysis and jack-knife testing. Test signatures for families from each of the main SCOP classes are described in relation to the quality of the structural alignments, the SigGen parameters used, and their diagnostic performance. We show that automatically generated signatures are potently diagnostic for their family (ROC50 scores typically >0.8), consistently outperform random signatures, and can identify sequence relationships in the "twilight zone" of protein sequence similarity (<40%). Signatures based on 15%-30% of alignment positions occurred most frequently among the best-performing signatures. When alignment quality is poor, sparser signatures perform better, whereas signatures generated from higher-quality alignments of fewer structures require more positions to be diagnostic. Our validation of signatures from the Globin family shows that when sequences from the structural alignment are removed and new signatures generated, the omitted sequences are still detected. The positions highlighted by the signature often correspond (alignment specificity >0.7) to the key positions in the original (non-jack-knifed) alignment. We discuss potential applications of sparse signatures in sequence annotation and homology modeling.     

Robust Bayesian variable selection for gene–environment interactions

Gene–environment (G× E) interactions have important implications to elucidate the etiology of complex diseases beyond the main genetic and environmental effects. Outliers and data contamination in disease phenotypes of G× E studies have been commonly encountered, leading to the development of a broad spectrum of robust regularization methods. Nevertheless, within the Bayesian framework, the issue has not been taken care of in existing studies. We develop a fully Bayesian robust variable selection method for G× E interaction studies. The proposed Bayesian method can effectively accommodate heavy-tailed errors and outliers in the response variable while conducting variable selection by accounting for structural sparsity. In particular, for the robust sparse group selection, the spike-and-slab priors have been imposed on both individual and group levels to identify important main and interaction effects robustly. An efficient Gibbs sampler has been developed to facilitate fast computation. Extensive simulation studies, analysis of diabetes data with single-nucleotide polymorphism measurements from the Nurses' Health Study, and The Cancer Genome Atlas melanoma data with gene expression measurements demonstrate the superior performance of the proposed method over multiple competing alternatives.     

Integrative Protein Modeling in RosettaNMR from Sparse Paramagnetic Restraints

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基于SNIC-CNN-SVM模型的京津风沙源二期工程区土地利用/土地覆盖遥感识别研究

稀疏植被覆盖（草地、沙地、戈壁）演变能够直接表征区域生态环境和人类活动的动态影响变化。但由于大尺度稀疏植被区一般都具有地理跨度大,景观结构复杂多样,破碎化程度高,现有地表覆盖分类产品针对性不足等问题,使得该区域内林草沙的遥感提取难度较大,精度普遍偏低,直接制约生态效应评价模型的应用效果。因此,以典型大尺度稀疏植被区--京津风沙源治理二期工程区为研究区,研建了SNIC-CNN-SVM （SCS）模型,实现了大尺度稀疏植被区林草沙典型要素的信息自动提取和主要土地利用/土地覆盖类型识别。研究结果表明：1）引入惩罚性机制优化后的SNIC分割算法,有效提升了稀疏植被区与沙地区的边界区分度,有助于分类精度的提升;2）基于改进SNIC-CNN-SVM模型方案的研究区总体分类精度达89.41%,较优化前提高了11.17%,特别是乔、灌、草、沙地和戈壁的分类识别精度显著提升,表明该优化方案在以研究区为代表的稀疏植被区域分类中具有较好的应用效果和推广价值;3）分类结果显示,2020年工程区草地面积最大,占到了一半以上（51.52%）,沙地占比11.96%,稀疏植被覆盖（草地、沙地、戈壁）区域占比68.68%,表明工程区处在林地-稀疏植被-沙地的过渡地带,生态环境保护压力与防沙治沙形势依然严峻;4）近20年来,乔灌草等植被增加面积约占工程区20.64%,主要由沙化土地转化,沙化土地减少面积约占工程区的4.58%,表明研究区植被状况不断改善,实施的各项生态工程作用显著,能够更有效地服务于多维度生态系统服务功能评价。该研究以期能够为京津风沙源二期工程区的生态系统演变规律研究及生态工程评价等工作提供重要科学支撑。     

Integration of lipidomics and metabolomics for in-depth understanding of cellular mechanism and disease progression

Mass spectrometry(MS)-based omics technologies are now widely used to profile small molecules in multiple matrices to confer comprehensive snapshots of cellular metabolic phenotypes.The metabolomes of cells,tissues,and organisms comprise a variety of molecules including lipids,amino acids,sugars,organic acids,and so on.Metabolomics mainly focus on the hydrophilic classes,while lipidomics has emerged as an independent omics owing to the complexities of the organismal lipidomes.The potential roles of lipids and small metabolites in disease pathogenesis have been widely investigated in various human diseases,but system-level understanding is largely lacking,which could be partly attributed to the insufficiency in terms of metabolite coverage and quantitation accuracy in current analytical technologies.While scientists are continuously striving to develop high-coverage omics approaches,integration of metabolomics and lipidomics is becoming an emerging approach to mechanistic investigation.Integration of metabolome and lipidome offers a complete atlas of the metabolic landscape,enabling comprehensive network analysis to identify critical metabolic drivers in disease pathology,facilitating the study of interconnection between lipids and other metabolites in disease progression.In this review,we summarize omics-based findings on the roles of lipids and metabolites in the pathogenesis of selected major diseases threatening public health.We also discuss the advantages of integrating lipidomics and metabolomics for in-depth understanding of molecular mechanism in disease pathogenesis.