基于氨基酸组成分布的嗜热和嗜冷蛋白随机森林分类模型

文献报道采用氨基酸组成分布提取特征值能有效提高预测分类精度, 本文采用该方法提取特征值, 使用一种新的组合分类器——随机森林, 从蛋白质一级结构对嗜热和嗜冷蛋白进行分类。通过10倍交叉验证和独立样本测试两种方法检测, 结果表明：当分段数量为1时, 其精度最优, 分别为92.9％和90.2％, 暗示使用基于氨基酸组成分布提取特征值在该算法中并不能有效提高识别精度, 这与报道结果不符, 而该提取方法在SVM中却能适当提高识别精度; 当引入6个新变量后, 其精度分别提高到93.2%和92.2%, ROC曲线下面积分别为0.9771和0.9696, 优于其它组合分类器。     

支持向量机及组合预测在蛋白质四级结构分类中的应用

目的:基于支持向量机建立一个自动化识别新肽链四级结构的方法,提高现有方法的识别精度.方法:改进4种已有的蛋白质一级序列特征值提取方法,采用线性和非线性组合预测方法建立一个有效的组合预测模型.结果:以同源二聚体及非同源二聚体为例.对4种特征值提取方法进行改进后其分类精度均提升了2～3%;进一步实施线性与非线性组合预测后,其分类精度再次提高了2～3%,使独立测试集的分类精度达到了90%以上.结论:4种特征值提取方法均较好地反应出蛋白质一级序列包含四级结构信息,组合预测方法能有效地集多种特征值提取方法优势于一体.     

基于Boosting机制的决策树集成分类器识别嗜热和常温蛋白

采用Boosting机制的决策树集成分类器对嗜热和常温蛋白进行模式识别。通过自一致性检验、交叉验证和独立样本测试三种方法检测,其中作为Boosting算法中新的Logitboost算法表现更好,其识别的精度分别为100%、88.4%和89.5%,优于神经网络的识别效果。同时探讨了蛋白质分子大小对识别效果的影响。结果表明,将Boosting算法与其它单一分类器有效结合,有望提高研究者对生物分子相关特性的识别能力。     

基于氨基酸组成分布的蛋白质同源寡聚体分类研究

基于一种新的特征提取方法——氨基酸组成分布,使用支持向量机作为成员分类器,采用“一对一”的多类分类策略,从蛋白质一级序列对四类同源寡聚体进行分类研究。结果表明,在10-CV检验下,基于氨基酸组成分布,其总分类精度和精度指数分别达到了86.22%和67.12%,比基于氨基酸组成成分的传统特征提取方法分别提高了5.74和10.03个百分点,比二肽组成成分特征提取方法分别提高了3.12和5.63个百分点,说明氨基酸组成分布对于蛋白质同源寡聚体分类是一种非常有效的特征提取方法;将氨基酸组成分布和蛋白质序列长度特征组合,其总分类精度和精度指数分别达到了86.35%和67.23%,说明蛋白质序列长度特征含有一定的空间结构信息。     

基于修正的伪氨基酸组成预测氧化还原酶辅酶类型

从序列出发快速确定氧化还原酶的辅酶依赖类型对于了解其结构和功能、催化机制及构建辅酶再生体系具有重要指导作用。对Chou提出的伪氨基酸组成方法进行了修正并用于提取氧化还原酶序列特征值, 采用k-近邻算法预测其辅酶依赖类型。当λ=48, w=0.1时, 10倍交叉验证结果表明: 其ROC曲线下面积为0.9536, 预测精度达92.0%, 比最优条件下伪氨基酸组成预测精度提高了3.5%; 与其他7种常见特征值提取方法相比, 修正的伪氨基酸组成表现最好。结果表明从序列出发预测氧化还原酶辅酶依赖类型是可行的, 且修正的伪氨基酸组成可望成为一种新的有效提取蛋白质序列特征值方法。     

基于物化性质对嗜热蛋白的预测

嗜热蛋白在高温下能保持稳定性和活性,是研究蛋白质热稳定性的理想模型,开发一个蛋白质热稳定性识别的方法将对蛋白质工程和蛋白质的设计很有帮助。目前的研究中,氨基酸的组成及其物化性质一直被认为和蛋白质的热稳定性相关。本研究筛选出可靠的数据集,包括915个嗜热蛋白和793个非嗜热蛋白。利用蛋白质氨基酸的物化性质和氨基酸的组成表征嗜热蛋白,将二肽氨基酸组成整合到9组氨基酸物化性质中使蛋白序列公式化。支持向量机5折叠交叉验证表明:当gap=0时,290个特征产生的精度最高,为92.74%。因此说明对于分析蛋白质的热稳定性,所建立的预测模型将是一个很有效的工具。     

基于不同标度伪氨基酸组成预测脂肪酶的类型

从序列出发预测某蛋白质是否为脂肪酶以及属于哪种脂肪酶具有重要的理论和应用价值.提出了基于Z标度和T标度的伪氨基酸组成方法提取序列特征值,采用了k-近邻算法回答上述问题.经参数选择后,三种方法在各自最优运行参数下,其1倍交叉验证的结果为:对脂肪酶和非脂肪酶预测精度分别为92.8%、91.4%和91.3%;对脂肪酶类型预测的精度分别为92.3%、90.3%和89.7%.其中基于Z标度伪氨基酸组成效果最佳.基于T标度的次之,但均明显优于其他6种常见的特征值提取方法,并对其可能的原因进行了探讨.     

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。     

基于二级结构氨基酸组成识别酸性、中性及碱性酶

本研究系统分析了酸性、碱性和中性酶在二级结构氨基酸组成上的差异。结果发现在形成特定二级结构过程中,酸性酶和碱性酶有着不同的氨基酸使用偏向;同时,在酸性和碱性酶中,中性氨基酸和侧链微小的氨基酸含量明显较高,这可能是它们适应极端pH的普遍机制。基于此,提出了一种提取蛋白质序列特征值的新方法,其10倍交叉验证的精度可达80.3%。与其他常见特征值提取方法相比,其精度提高了9.4%到18.7%不等;而随机森林算法比其他机器学习算法识别精度也高出2.7%到21.8%不等。     

爬行动物消化道嗜银细胞的研究进展

对爬行动物消化道嗜银细胞的研究已经有不少报道,这些研究结果表明爬行动物胃肠嗜银细胞的类型和分布不仅与哺乳动物不同,且在爬行动物各个种间亦存在着较大的差异。本文总结了爬行动物消化道嗜银细胞的形态学特征和分布规律,概述了分类地位、食性和栖息地环境与细胞分布型的关系。     

基于Sentinel-1/2的大兴安岭木本沼泽信息提取方法

木本沼泽的遥感信息提取一直是湿地研究的难点之一,在复杂环境地区传统调查方法无法深入,而利用遥感影像提取湿地分布信息大大提高了研究效率,对于理解全球变化具有重要意义.本研究选取位于黑龙江省西北部大兴安岭地区额木尔河流域的一景影像为研究案例,融合应用Sentinel-1的雷达波段和Sen-tinel-2的红边波段,基于红边...     

Accelerated Active Case Detection of Visceral Leishmaniasis Patients in Endemic Villages of Bangladesh

Background

The visceral leishmaniasis (VL) elimination program in Bangladesh is in its attack phase. The primary goal of this phase is to decrease the burden of VL as much as possible. Active case detection (ACD) by the fever camp method and an approach using past VL cases in the last 6–12 months have been found useful for detection of VL patients in the community. We aimed to explore the yield of Accelerated Active Case Detection (AACD) of non-self reporting VL as well as the factors that are associated with non-self reporting to hospitals in endemic communities of Bangladesh.

Methods

Our study was conducted in the Trishal sub-district of Mymensingh, a highly VL endemic region of Bangladesh. We used a two-stage sampling strategy from 12 VL endemic unions of Trishal. Two villages from each union were selected at random. We looked for VL patients who had self-reported to the hospital and were under treatment from these villages. Then we conducted AACD for VL cases in those villages using house-to-house visit. Suspected VL cases were referred to the Trishal hospital where diagnosis and treatment of VL was done following National Guidelines for VL case management. We collected socio-demographic information from patients or a patient guardian using a structured questionnaire.

Results

The total number of VL cases was 51. Nineteen of 51 (37.3%) were identified by AACD. Poverty, female gender and poor knowledge about VL were independent factors associated with non self-reporting to the hospital.

Conclusion

Our primary finding is that AACD is a useful method for early detection of VL cases that would otherwise go unreported to the hospital in later stage due to poverty, poor knowledge about VL and gender inequity. We recommend that the National VL Program should consider AACD to strengthen its early VL case detection strategy.     

Prediction of neurotoxins based on their function and source

We have developed a method NTXpred for predicting neurotoxins and classifying them based on their function and origin. The dataset used in this study consists of 582 non-redundant, experimentally annotated neurotoxins obtained from Swiss-Prot. A number of modules have been developed for predicting neurotoxins using residue composition based on feed-forwarded neural network (FNN), recurrent neural network (RNN), support vector machine (SVM) and achieved maximum accuracy of 84.19%, 92.75%, 97.72% respectively. In addition, SVM modules have been developed for classifying neurotoxins based on their source (e.g., eubacteria, cnidarians, molluscs, arthropods have been and chordate) using amino acid composition and dipeptide composition and achieved maximum overall accuracy of 78.94% and 88.07% respectively. The overall accuracy increased to 92.10%, when the evolutionary information obtained from PSI-BLAST was combined with SVM module of source classification. We have also developed SVM modules for classifying neurotoxins based on functions using amino acid, dipeptide composition and achieved overall accuracy of 83.11%, 91.10% respectively. The overall accuracy of function classification improved to 95.11%, when PSI-BLAST output was combined with SVM module. All the modules developed in this study were evaluated using five-fold cross-validation technique. The NTXpred is available at www.imtech.res.in/raghava/ntxpred/ and mirror site at http://bioinformatics.uams.edu/mirror/ntxpred.     

从非同源蛋白质的一级序列预测其结构类

对基于氨基酸组成、自相关函数和自协方差函数提取特征的蛋白质结构类预测算法进行分析比较,对氨基酸组成和自相关函数相结合的方法,以及氨基酸组成和自协放差函数相结合的方法的预测算法进行了研究。结果表明：对非同源蛋白质,因氨基酸和自相关函数相结合的方法中,采用Miyazawa和Jernigan的疏水值时,训练的自检验的总精度为95．34％,其Jackknife检验的总精度为81．92％,检验加的他检验的总精工为86．61％。在氨基酸组成和自协方差函数相结合的方法中,采用Wold等的疏水值时,训练库的自检验的总精度为96．71％,其Jackknife检验的总精度为82．18％,检验加的他检验的总精工为86．88％。这说明氨基酸组成和自相关函数相结合的方法,以及氨基酸组成和自协方差函数相结合的方法可有效提高结构类预测精度,表明提取更多有效的序列信息是提高分类精度的关键。     

Improved sequence-based prediction of protein secondary structures by combining vacuum-ultraviolet circular dichroism spectroscopy with neural network

Synchrotron-radiation vacuum-ultraviolet circular dichroism (VUVCD) spectroscopy can significantly improve the predictive accuracy of the contents and segment numbers of protein secondary structures by extending the short-wavelength limit of the spectra. In the present study, we combined VUVCD spectra down to 160 nm with neural-network (NN) method to improve the sequence-based prediction of protein secondary structures. The secondary structures of 30 target proteins (test set) were assigned into alpha-helices, beta-strands, and others by the DSSP program based on their X-ray crystal structures. Combining the alpha-helix and beta-strand contents estimated from the VUVCD spectra of the target proteins improved the overall sequence-based predictive accuracy Q(3) for three secondary-structure components from 59.5 to 60.7%. Incorporating the position-specific scoring matrix in the NN method improved the predictive accuracy from 70.9 to 72.1% when combining the secondary-structure contents, to 72.5% when combining the numbers of segments, and finally to 74.9% when filtering the VUVCD data. Improvement in the sequence-based prediction of secondary structures was also apparent in two other indices of the overall performance: the correlation coefficient (C) and the segment overlap value (SOV). These results suggest that VUVCD data could enhance the predictive accuracy to over 80% when combined with the currently best sequence-prediction algorithms, greatly expanding the applicability of VUVCD spectroscopy to protein structural biology.     

Predicting the cofactors of oxidoreductases based on amino acid composition distribution and Chou's amphiphilic pseudo-amino acid composition

Predicting the cofactors of oxidoreductases plays an important role in inferring their catalytic mechanism. Feature extraction is a critical part in the prediction systems, requiring raw sequence data to be transformed into appropriate numerical feature vectors while minimizing information loss. In this paper, we present an amino acid composition distribution method for extracting useful features from primary sequence, and the k-nearest neighbor was used as the classifier. The overall prediction accuracy evaluated by the 10-fold cross-validation reached 90.74%. Comparing our method with other eight feature extraction methods, the improvement of the overall prediction accuracy ranged from 3.49% to 15.74%. Our experimental results confirm that the method we proposed is very useful and may be used for other bioinformatical predictions. Interestingly, when features extracted by our method and Chou's amphiphilic pseudo-amino acid composition were combined, the overall accuracy could reach 92.53%.     

The recognition of multi-class protein folds by adding average chemical shifts of secondary structure elements

The recognition of protein folds is an important step in the prediction of protein structure and function. Recently, an increasing number of researchers have sought to improve the methods for protein fold recognition. Following the construction of a dataset consisting of 27 protein fold classes by Ding and Dubchak in 2001, prediction algorithms, parameters and the construction of new datasets have improved for the prediction of protein folds. In this study, we reorganized a dataset consisting of 76-fold classes constructed by Liu et al. and used the values of the increment of diversity, average chemical shifts of secondary structure elements and secondary structure motifs as feature parameters in the recognition of multi-class protein folds. With the combined feature vector as the input parameter for the Random Forests algorithm and ensemble classification strategy, we propose a novel method to identify the 76 protein fold classes. The overall accuracy of the test dataset using an independent test was 66.69%; when the training and test sets were combined, with 5-fold cross-validation, the overall accuracy was 73.43%. This method was further used to predict the test dataset and the corresponding structural classification of the first 27-protein fold class dataset, resulting in overall accuracies of 79.66% and 93.40%, respectively. Moreover, when the training set and test sets were combined, the accuracy using 5-fold cross-validation was 81.21%. Additionally, this approach resulted in improved prediction results using the 27-protein fold class dataset constructed by Ding and Dubchak.     

Comparison of Hybrid Classifiers for Crop Classification Using Normalized Difference Vegetation Index Time Series: A Case Study for Major Crops in North Xinjiang,China

A range of single classifiers have been proposed to classify crop types using time series vegetation indices, and hybrid classifiers are used to improve discriminatory power. Traditional fusion rules use the product of multi-single classifiers, but that strategy cannot integrate the classification output of machine learning classifiers. In this research, the performance of two hybrid strategies, multiple voting (M-voting) and probabilistic fusion (P-fusion), for crop classification using NDVI time series were tested with different training sample sizes at both pixel and object levels, and two representative counties in north Xinjiang were selected as study area. The single classifiers employed in this research included Random Forest (RF), Support Vector Machine (SVM), and See 5 (C 5.0). The results indicated that classification performance improved (increased the mean overall accuracy by 5%~10%, and reduced standard deviation of overall accuracy by around 1%) substantially with the training sample number, and when the training sample size was small (50 or 100 training samples), hybrid classifiers substantially outperformed single classifiers with higher mean overall accuracy (1%~2%). However, when abundant training samples (4,000) were employed, single classifiers could achieve good classification accuracy, and all classifiers obtained similar performances. Additionally, although object-based classification did not improve accuracy, it resulted in greater visual appeal, especially in study areas with a heterogeneous cropping pattern.