The forecast of the postoperative survival time of patients suffered from non-small cell lung cancer based on PCA and extreme learning machine

In this paper, a new effective model is proposed to forecast how long the postoperative patients suffered from non-small cell lung cancer will survive. The new effective model which is based on the extreme learning machine (ELM) and principal component analysis (PCA) can forecast successfully the postoperative patients' survival time. The new model obtains better prediction accuracy and faster convergence rate which the model using backpropagation (BP) algorithm and the Levenberg-Marquardt (LM) algorithm to forecast the postoperative patients' survival time can not achieve. Finally, simulation results are given to verify the efficiency and effectiveness of our proposed new model.     

Prediction of protein B-factors using multi-class bounded SVM

In this paper, we propose the adoption of the bounded support vector machine (BSVM) to predict the B-factors of residues based on a number of distinctive properties of residues. Due to the ability of multi-class classification of the BSVM, we can elaborately distinguish our targets and obtain relatively higher accuracy.     

Molecular pattern discovery based on penalized matrix decomposition

A reliable and precise identification of the type of tumors is crucial to the effective treatment of cancer. With the rapid development of microarray technologies, tumor clustering based on gene expression data is becoming a powerful approach to cancer class discovery. In this paper, we apply the penalized matrix decomposition (PMD) to gene expression data to extract metasamples for clustering. The extracted metasamples capture the inherent structures of samples belong to the same class. At the same time, the PMD factors of a sample over the metasamples can be used as its class indicator in return. Compared with the conventional methods such as hierarchical clustering (HC), self-organizing maps (SOM), affinity propagation (AP) and nonnegative matrix factorization (NMF), the proposed method can identify the samples with complex classes. Moreover, the factor of PMD can be used as an index to determine the cluster number. The proposed method provides a reasonable explanation of the inconsistent classifications made by the conventional methods. In addition, it is able to discover the modules in gene expression data of conterminous developmental stages. Experiments on two representative problems show that the proposed PMD-based method is very promising to discover biological phenotypes.     

Non-linear cancer classification using a modified radial basis function classification algorithm

Summary This paper proposes a modified radial basis function classification algorithm for non-linear cancer classification. In the algorithm, a modified simulated annealing method is developed and combined with the linear least square and gradient paradigms to optimize the structure of the radial basis function (RBF) classifier. The proposed algorithm can be adopted to perform non-linear cancer classification based on gene expression profiles and applied to two microarray data sets involving various human tumor classes: (1) Normal versus colon tumor; (2) acute myeloid leukemia (AML) versus acute lymphoblastic leukemia (ALL). Finally, accuracy and stability for the proposed algorithm are further demonstrated by comparing with the other cancer classification algorithms.     

Gene expression data classification using consensus independent component analysis

We propose a new method for tumor classification from gene expression data, which mainly contains three steps. Firstly, the original DNA microarray gene expression data are modeled by independent component analysis （ICA）. Secondly, the most discriminant eigenassays extracted by ICA are selected by the sequential floating forward selection technique. Finally, support vector machine is used to classify the modeling data. To show the validity of the proposed method, we applied it to classify three DNA microarray datasets involving various human normal and tumor tissue samples. The experimental results show that the method is efficient and feasible.     

大白菜霜霉菌诱导抑制性消减杂交cDNA文库的构建和分析

以抗霜霉病大白菜双单倍体系（DH）‘T12-19’为材料,构建了霜霉病诱导表达的正向抑制性消减文库,并利用反向Northern斑点杂交技术对768个阳性克隆进行了筛选,共获得57个病原菌诱导上调表达的克隆。测序后得到55条通读表达序列标签（ESTs）,对这些ESTs序列进行聚类和拼接分析,共获得50个unigenes。Blast分析表明,37个unigenes与已知基因高度同源,占全部非重复序列的67.3%。对已知功能基因按MIPS的分类方法进行功能分类,发现这些基因的功能主要涉及物质与能量代谢、转录调控、蛋白质合成与代谢、膜及转运、信号转导、抗病防御等。为了验证文库筛选结果的可靠性,采用实时荧光定量PCR技术分析了其中2个克隆BFCH10和BFIA7的表达谱。结果表明,这2个克隆在接种病菌6h后明显上调表达,与反向Northern斑点杂交结果基本一致。     

Linkage Map Construction and Quantitative Trait Loci Analysis for Bolting Based on a Double Haploid Population of Brassica rapa

Early bolting of Chinese cabbage （Brassica rapa L.） during spring cultivation often has detrimental effects on the yield and quality of the harvested products. Breeding late bolting varieties is a major objective of Chinese cabbage breeding programs. In order to analyze the genetic basis of bolting traits, a genetic map of B. rapa was constructed based on amplified fragment-length poiymorphism （AFLP）, sequence-related amplified poiymorphism （SRAP）, simple sequence repeat （SSR）, random amplification of polymorphic DNA （RAPD）, and isozyme markers. Marker analysis was carried out on 81 double haploid （DH） lines obtained by microspore culture from F1 progeny of two homozygous parents： B. rapa L. ssp. pekinensis （BY） （an extra-early bolting Chinese cabbage line） and B. rapa L. ssp. rapifera （MM） （an extra-late bolting European turnip line）. A total of 326 markers including 130 AFLPs, 123 SRAPs, 16 SSRs, 43 RAPDs and 14 isozymes were used to construct a linkage map with 10 linkage groups covering 882 cM with an average distance of 2.71 cM between loci. The bolting trait of each DH line was evaluated by the bolting index under controlled conditions. Quantitative trait loci （QTL） analysis was conducted using multiple QTL model mapping with MapQTL5.0 software. Eight QTLs controlling bolting resistance were identified. These QTLs, accounting for 14.1% to 25.2% of the phenotypic variation with positive additive effects, were distributed into three linkage groups. These results provide useful information for molecular marker-assisted selection of late bolting traits in Chinese cabbage breeding programs.     

Inferring protein-protein interacting sites using residue conservation and evolutionary information

This paper proposes a novel method using protein residue conservation and evolution information, i.e., spatial sequence profile, sequence information entropy and evolution rate, to infer protein binding sites. Some predictors based on support vector machines (SVMs) algorithm are constructed to predict the role of surface residues in protein-protein interface. By combining protein residue characters, the prediction performance can be improved obviously. We then made use of the predicted labels of neighbor residues to improve the performance of the predictors. The efficiency and the effectiveness of our proposed approach are verified by its better prediction performance based on a non-redundant data set of heterodimers.     

Predicting protein–protein interactions from sequence using correlation coefficient and high-quality interaction dataset

Identifying protein–protein interactions (PPIs) is critical for understanding the cellular function of the proteins and the machinery of a proteome. Data of PPIs derived from high-throughput technologies are often incomplete and noisy. Therefore, it is important to develop computational methods and high-quality interaction dataset for predicting PPIs. A sequence-based method is proposed by combining correlation coefficient (CC) transformation and support vector machine (SVM). CC transformation not only adequately considers the neighboring effect of protein sequence but describes the level of CC between two protein sequences. A gold standard positives (interacting) dataset MIPS Core and a gold standard negatives (non-interacting) dataset GO-NEG of yeast Saccharomyces cerevisiae were mined to objectively evaluate the above method and attenuate the bias. The SVM model combined with CC transformation yielded the best performance with a high accuracy of 87.94% using gold standard positives and gold standard negatives datasets. The source code of MATLAB and the datasets are available on request under smgsmg@mail.ustc.edu.cn.     

A novel hybrid GA/RBFNN technique for protein sequences classification

A novel hybrid genetic algorithm (GA)/radial basis function neural network (RBFNN) technique, which selects features from the protein sequences and trains the RBF neural network simultaneously, is proposed in this paper. Experimental results show that the proposed hybrid GA/RBFNN system outperforms the BLAST and the HMMer.