集成改进KNN算法预测蛋白质亚细胞定位

基于Adaboost算法对多个相似性比对K最近邻(K-nearest neighbor,KNN)分类器集成实现蛋白质的亚细胞定位预测。相似性比对KNN算法分别以氨基酸组成、二肽、伪氨基酸组成为蛋白序列特征,在KNN的决策阶段使用Blast比对决定蛋白质的亚细胞定位。在Jackknife检验下,Adaboost集成分类算法提取3种蛋白序列特征,3种特征在数据集CH317和Gram1253的最高预测成功率分别为92.4%和93.1%。结果表明Adaboost集成改进KNN分类预测方法是一种有效的蛋白质亚细胞定位预测方法。     

Using optimized evidence-theoretic K-nearest neighbor classifier and pseudo-amino acid composition to predict membrane protein types

Knowledge of membrane protein type often provides crucial hints toward determining the function of an uncharacterized membrane protein. With the avalanche of new protein sequences emerging during the post-genomic era, it is highly desirable to develop an automated method that can serve as a high throughput tool in identifying the types of newly found membrane proteins according to their primary sequences, so as to timely make the relevant annotations on them for the reference usage in both basic research and drug discovery. Based on the concept of pseudo-amino acid composition [K.C. Chou, Proteins: Struct. Funct. Genet. 43 (2001) 246-255; Erratum: Proteins: Struct. Funct. Genet. 44 (2001) 60] that has made it possible to incorporate a considerable amount of sequence-order effects by representing a protein sample in terms of a set of discrete numbers, a novel predictor, the so-called "optimized evidence-theoretic K-nearest neighbor" or "OET-KNN" classifier, was proposed. It was demonstrated via the self-consistency test, jackknife test, and independent dataset test that the new predictor, compared with many previous ones, yielded higher success rates in most cases. The new predictor can also be used to improve the prediction quality for, among many other protein attributes, structural class, subcellular localization, enzyme family class, and G-protein coupled receptor type. The OET-KNN classifier will be available as a web-server at http://www.pami.sjtu.edu.cn/kcchou.     

RNA secondary structure prediction from sequence alignments using a network of k-nearest neighbor classifiers

We present a machine learning method (a hierarchical network of k-nearest neighbor classifiers) that uses an RNA sequence alignment in order to predict a consensus RNA secondary structure. The input to the network is the mutual information, the fraction of complementary nucleotides, and a novel consensus RNAfold secondary structure prediction of a pair of alignment columns and its nearest neighbors. Given this input, the network computes a prediction as to whether a particular pair of alignment columns corresponds to a base pair. By using a comprehensive test set of 49 RFAM alignments, the program KNetFold achieves an average Matthews correlation coefficient of 0.81. This is a significant improvement compared with the secondary structure prediction methods PFOLD and RNAalifold. By using the example of archaeal RNase P, we show that the program can also predict pseudoknot interactions.     

Building multiclass classifiers for remote homology detection and fold recognition

Background  

Protein remote homology detection and fold recognition are central problems in computational biology. Supervised learning algorithms based on support vector machines are currently one of the most effective methods for solving these problems. These methods are primarily used to solve binary classification problems and they have not been extensively used to solve the more general multiclass remote homology prediction and fold recognition problems.     

Life-threatening ventricular arrhythmia recognition by nonlinear descriptor

Background  

Ventricular tachycardia (VT) and ventricular fibrillation (VF) are ventricular cardiac arrhythmia that could be catastrophic and life threatening. Correct and timely detection of VT or VF can save lives.     

Preconditioning by hypoventilation increases ventricular arrhythmia threshold in Wistar rats

Hypoventilation, as one of ventilatory disorders, decreases the electrical stability of the heart similarly as ischemia. If preconditioning by short cycles of ischemia has a cardioprotective effect against harmful influences of a prolonged ischemic period, then preconditioning by hypoventilation (HPC) can also have a similar effect. Anesthetized rats (ketamine 100 mg/kg + xylasine 15 mg/kg i.m., open chest experiments) were subjected to 20 min of hypoventilation followed by 20 min of reoxygenation (control group). The preconditioning (PC) was induced by one (1PC), two (2PC) or three (3PC) cycles of 5-min hypoventilation followed by 5-min reoxygenation. The electrical stability of the heart was measured by a ventricular arrhythmia threshold (VAT) tested by electrical stimulation of the right ventricle. Twenty-minute hypoventilation significantly decreased the VAT in the control and 1PC groups (p<0.05) and non-significantly in 2PC vs. the initial values. Reoxygenation reversed the VAT values to the initial level only in the control group. In 3PC, the VAT was increased from 2.32+/-0.69 mA to 4.25+/-1.31 mA. during hypoventilation (p<0.001) and to 4.37+/-1.99 mA during reoxygenation (p<0.001). It is concluded that cardioprotection against the hypoventilation/ reoxygenation-induced decrease of VAT proved to be effective only after three cycles of HPC.     

卡托普利对急性缺血心肌早期室性心律失常的影响

目的:观察卡托普利对急性心肌缺血早期在体电生理指标的改变,探讨卡托普利对急性心肌梗塞(AMI)早期心律失常的影响。方法:采用S1-S2程控电刺激方法同时测定无心肌缺血对照组(假手术对照组)、AMI早期缺血组(AMI组)和用卡托普利(浓度0.1mg·kg-1·min-1)灌流AMI早期缺血的卡托普利组对家兔心室易损期(VVP)、室颤阈(VFT)、舒张阈(DT)、有效不应期(ERP)、T波顶点与VVP外缘处的时间关系(TT-VVP)等电生理指标。结果:VVP、VFT、DT、ERP和TT-VVP在假手术对照组与AMI组和卡托普利组比较均有显著差异(P<0.01),AMI组与卡托普利组比较亦有显著差异(P<0.01),相对于假手术对照组,AMI组VVP延长,VFT和DT降低,ERP缩短,心室易损期外缘向T波方向延伸增加;相对于AMI组,卡托普利组早期VVP缩短,VFT相对升高,ERP相对延长,心室易损期外缘向T波方向延伸相对减少。结论:卡托普利对急性心肌梗塞早期室性心动过速和/或心室颤动的产生有抑制作用。     

Discriminating outer membrane proteins with Fuzzy K-nearest Neighbor algorithms based on the general form of Chou's PseAAC

Outer membrane proteins (OMPs) play important roles in cell biology. In addition, OMPs are targeted by multiple drugs. The identification of OMPs from genomic sequences and successful prediction of their secondary and tertiary structures is a challenging task due to short membrane-spanning regions with high variation in properties. Therefore, an effective and accurate silico method for discrimination of OMPs from their primary sequences is needed. In this paper, we have analyzed the performance of various machine learning mechanisms for discriminating OMPs such as: Genetic Programming, K-nearest Neighbor, and Fuzzy K-nearest Neighbor (Fuzzy K-NN) in conjunction with discrete methods such as: Amino acid composition, Amphiphilic Pseudo amino acid composition, Split amino acid composition (SAAC), and hybrid versions of these methods. The performance of the classifiers is evaluated by two datasets using 5-fold crossvalidation. After the simulation, we have observed that Fuzzy K-NN using SAAC based-features makes it quite effective in discriminating OMPs. Fuzzy K-NN achieves the highest success rates of 99.00% accuracy for discriminating OMPs from non-OMPs and 98.77% and 98.28% accuracies from α-helix membrane and globular proteins, respectively on dataset1. While on dataset2, Fuzzy K-NN achieves 99.55%, 99.90%, and 99.81% accuracies for discriminating OMPs from non- OMPs, α-helix membrane, and globular proteins, respectively. It is observed that the classification performance of our proposed method is satisfactory and is better than the existing methods. Thus, it might be an effective tool for high throughput innovation of OMPs.     

Fuzzy ART-based place recognition for visual loop closure detection

The automatic place recognition problem is one of the key challenges in SLAM approaches for loop closure detection. Most of the appearance-based solutions to this problem share the idea of image feature extraction, memorization, and matching search. The weakness of these solutions is the storage and computational costs which increase drastically with the environment size. In this regard, the major constraints to overcome are the required visual information storage and the complexity of similarity computation. In this paper, a novel formulation is proposed that allows the computation time reduction while no visual information are stored and matched explicitly. The proposed solution relies on the incremental building of a bio-inspired visual memory using a Fuzzy ART network. This network considers the properties discovered in primate brain. The performance evaluation of the proposed method has been conducted using two datasets representing different large scale outdoor environments. The method has been compared with RatSLAM and FAB-MAP approaches and has demonstrated a decreased time and storage costs with broadly comparable precision recall performance.     

Robust two-gene classifiers for cancer prediction

Two-gene classifiers have attracted a broad interest for their simplicity and practicality. Most existing two-gene classification algorithms were involved in exhaustive search that led to their low time-efficiencies. In this study, we proposed two new two-gene classification algorithms which used simple univariate gene selection strategy and constructed simple classification rules based on optimal cut-points for two genes selected. We detected the optimal cut-point with the information entropy principle. We applied the two-gene classification models to eleven cancer gene expression datasets and compared their classification performance to that of some established two-gene classification models like the top-scoring pairs model and the greedy pairs model, as well as standard methods including Diagonal Linear Discriminant Analysis, k-Nearest Neighbor, Support Vector Machine and Random Forest. These comparisons indicated that the performance of our two-gene classifiers was comparable to or better than that of compared models.     

Effective detection of Parkinson's disease using an adaptive fuzzy k-nearest neighbor approach

In this paper, we present an effective and efficient diagnosis system based on particle swarm optimization (PSO) enhanced fuzzy k-nearest neighbor (FKNN) for Parkinson's disease (PD) diagnosis. In the proposed system, named PSO–FKNN, both the continuous version and binary version of PSO were used to perform the parameter optimization and feature selection simultaneously. On the one hand, the neighborhood size k and the fuzzy strength parameter m in FKNN classifier are adaptively specified by the continuous PSO. On the other hand, binary PSO is utilized to choose the most discriminative subset of features for prediction. The effectiveness of the PSO–FKNN model has been rigorously evaluated against the PD data set in terms of classification accuracy, sensitivity, specificity and the area under the receiver operating characteristic (ROC) curve (AUC). Compared to the existing methods in previous studies, the proposed system has achieved the highest classification accuracy reported so far via 10-fold cross-validation analysis, with the mean accuracy of 97.47%. Promisingly, the proposed diagnosis system might serve as a new candidate of powerful tools for diagnosing PD with excellent performance.