Molecular classification of cancer types from microarray data using the combination of genetic algorithms and support vector machines

Simultaneous multiclass classification of tumor types is essential for future clinical implementations of microarray-based cancer diagnosis. In this study, we have combined genetic algorithms (GAs) and all paired support vector machines (SVMs) for multiclass cancer identification. The predictive features have been selected through iterative SVMs/GAs, and recursive feature elimination post-processing steps, leading to a very compact cancer-related predictive gene set. Leave-one-out cross-validations yielded accuracies of 87.93% for the eight-class and 85.19% for the fourteen-class cancer classifications, outperforming the results derived from previously published methods.     

Bayesian modeling of dynamic motion integration

The quality of the representation of an object's motion is limited by the noise in the sensory input as well as by an intrinsic ambiguity due to the spatial limitation of the visual motion analyzers (aperture problem). Perceptual and oculomotor data demonstrate that motion processing of extended objects is initially dominated by the local 1D motion cues, related to the object's edges and orthogonal to them, whereas 2D information, related to terminators (or edge-endings), takes progressively over and leads to the final correct representation of global motion. A Bayesian framework accounting for the sensory noise and general expectancies for object velocities has proven successful in explaining several experimental findings concerning early motion processing [Weiss, Y., Adelson, E., 1998. Slow and smooth: a Bayesian theory for the combination of local motion signals in human vision. MIT Technical report, A.I. Memo 1624]. In particular, these models provide a qualitative account for the initial bias induced by the 1D motion cue. However, a complete functional model, encompassing the dynamical evolution of object motion perception, including the integration of different motion cues, is still lacking. Here we outline several experimental observations concerning human smooth pursuit of moving objects and more particularly the time course of its initiation phase, which reflects the ongoing motion integration process. In addition, we propose a recursive extension of the Bayesian model, motivated and constrained by our oculomotor data, to describe the dynamical integration of 1D and 2D motion information. We compare the model predictions for object motion tracking with human oculomotor recordings.     

非线性递归差分方程的全局渐近稳定性

得到下列非线性差分方程xn 1=xnxn-1 xn-2 axn-1 xnxn-2 a,n=0,1,2,…，其中a∈[0,∞),初值x-2,x-1,x0∈（0,∞)，全局渐近稳定性的一个充分条件，并且证明了一个猜测。     

小分子RNA与肿瘤治疗

肿瘤是各种原因导致多基因突变累积和相互作用形成的基因网络调控的结果,通过干预致瘤因子可能达到治疗肿瘤或改变肿瘤特性的目的。小分子RNA因可调控基因表达而被作为基因治疗的有效武器。本文结合我们课题组在小分子RNA方面的研究成果,对外源性的siRNA和内源性的miRNA在肿瘤治疗、治疗前景,以及siRNA和miRNA的联系与区别等方面作一综述。     

Recursive Residuals on a Rectangular Lattice

A set of independent N(O, ρ²) recursive residuals is obtained for a model proposed by GLEESON and McGILCHRIST (1980) to describe spatial dependence among observations on a rectangular lattice. These residuals can be used to test model adequacy in a similar fashion to Box-Jenkins techniques for time series models.