基于决策森林特征基因的两种识别方法

应用DNA芯片可获得成千上万个基因的表达谱数据。寻找对疾病有鉴别力的特征基因 ,滤掉与疾病无关的基因是基因表达谱数据分析的关键问题。利用决策森林方法的集成优势 ,提出基于决策森林的两种特征基因识别方法。该方法先由决策森林按照一定的显著性水平滤掉大部分与疾病类别无关的基因 ,然后采用统计频数法和扰动法 ,根据所选特征对分类的贡献程度对初选的特征基因作更加精细地选择。最后 ,选用神经网络作为外部分类器对所选的特征基因子集进行评价 ,将提出的方法应用于 4 0例结肠癌组织与 2 2例正常组织中 2 0 0 0个基因的表达谱实验数据。结果表明 :上述两种方法选出的特征基因均具有较高的疾病鉴别能力 ,均可获得最优特征基因子集 ,基于决策森林的统计频数法优于扰动法。     

随机森林：一种重要的肿瘤特征基因选择法

特征选择技术已经被广泛地应用于生物信息学科,随机森林（random forests,RF）是其中一种重要的特征选择方法。利用RF对胃癌、结肠癌和肺癌等5组基因表达谱数据进行特征基因选择,将选择结果与支持向量机（support vector machine,SVM）结合对原数据集分类,并对特征基因选择及分类结果进行初步的分析。同时使用微阵列显著性分析（significant analysis of microarray,SAM）和ReliefF法与RF比较,结果显示随机森林选择的特征基因包含更多分类信息,分类准确率更高。结合该方法自身具有的分类方面的诸多优势,随机森林可以作为一种可靠的基因表达谱数据分析手段被广泛使用。     

基于CFS-mRMR特征筛选方法和Adaboost算法的胶质瘤 相关基因筛选及预测模型的建立

目的:找出胶质瘤病变发生机制相关的基因群,并在此基础上建立预测胶质瘤病变发生的预测模型。方法:收集GEO中胶质瘤芯片数据,使用关联特征选择(Correlation-based Feature Subset, CFS)和最小冗余最大相关性(Minimum Redundancy MaximumRelevance, mRMR)特征选择方法筛选出差异基因,分析这些差异基因的功能,然后使用Adaboost算法建立胶质瘤的预测模型,并对模型的预测能力进行评估。结果:通过特征筛选,得到了19个和胶质瘤病变相关的的基因;以该19个基因建组成特征子集,结合AdaBoost算法建立了胶质瘤的预测模型,经验证,模型的预报准确率可以达到95.59%。通过对19个差异基因的GO和KEGG分析,发现这些基因和肿瘤的发生发展有一定作用。结论:CFS-mRMR特征筛选方法可以有效地发现与胶质瘤疾病有关的基因,所筛选的19个差异基因具有生物学意义,且以此构建的胶质瘤预测模型,可以有效地对预测胶质瘤的发生。     

SC-BPSO:肝癌分类中一种融合过滤器的二进制粒子群算法特征的选择方法

癌症的早期诊断能够显著提高癌症患者的存活率,在肝细胞癌患者中这种情况更加明显。机器学习是癌症分类中的有效工具。如何在复杂和高维的癌症数据集中,选择出低维度、高分类精度的特征子集是癌症分类的难题。本文提出了一种二阶段的特征选择方法SC-BPSO:通过组合Spearman相关系数和卡方独立检验作为过滤器的评价函数,设计了一种新型的过滤器方法——SC过滤器,再组合SC过滤器方法和基于二进制粒子群算法(BPSO)的包裹器方法,从而实现两阶段的特征选择。并应用在高维数据的癌症分类问题中,区分正常样本和肝细胞癌样本。首先,对来自美国国家生物信息中心(NCBI)和欧洲生物信息研究所(EBI)的130个肝组织microRNA序列数据(64肝细胞癌,66正常肝组织)进行预处理,使用MiRME算法从原始序列文件中提取microRNA的表达量、编辑水平和编辑后表达量3类特征。然后,调整SC-BPSO算法在肝细胞癌分类场景中的参数,选择出关键特征子集。最后,建立分类模型,预测结果,并与信息增益过滤器、信息增益率过滤器、BPSO包裹器特征选择算法选出的特征子集,使用相同参数的随机森林、支持向量机、决策树、KNN四种分类器分类,对比分类结果。使用SC-BPSO算法选择出的特征子集,分类准确率高达98.4%。研究结果表明,与另外3个特征选择算法相比,SC-BPSO算法能有效地找到尺寸较小和精度更高的特征子集。这对于少量样本高维数据的癌症分类问题可能具有重要意义。     

基于CNV区域网络识别神经胶质瘤的重要功能区域

目的：基于基因拷贝数变异（CNV）区域网络识别神经胶质瘤的重要功能区域。方法：运用独特的计算样本的共相关性值的方法,使CNV数据与基因数据产生联系;基于蛋白质互作关系,在CNV区域与基因之间搭建桥梁,构建CNV区域网络;分析网络拓扑性质,识别出神经胶质瘤的重要功能CNV区域。结果：本文共识别出了11个与神经胶质瘤相关的候选重要功能CNV区域,通过功能注释和通路分析,确认了识别到的区域与神经胶质瘤有重要联系。结论：通过基因与表型之间的联系,利用已知表型基因在同源、功能、互作、结构域上的特征将CNV区域与基因联系起来,通过基因的功能可以了解到CNV区域的功能,对于疾病的预测和诊断有重要的意义。     

基于逐步提取偏最小二乘主成分的特征选择方法

特征选择技术被广泛应用于生物信息学中。通过重复利用偏最小二乘（partial least square,PLS）方法提取主成分,通过逐次选择在主成分中权重较大的基因,将PLS应用于特征选择中。将这种方法用于对肿瘤基因表达谱数据的特征基因选择中,并用提取的特征基因分类,用8个特征基因进行分类时,能达到92.5%的正确率。     

结合基因功能分类体系Gene Ontology筛选聚类特征基因

使用两套基因表达谱数据,按各基因的表达值方差,选择表达变异基因对样本聚类,发现一般使用方差较大的前10％的基因作为特征基因,就可以较好地对疾病样本聚类。对不同的疾病,包含聚类信息的特征基因有不同的分布特点。在此基础上,结合基因功能分类体系(Gene Ontology,GO),进一步筛选聚类的特征基因。通过检验在Gene Ontology中的每个功能类中的表达变异基因是否非随机地聚集,寻找疾病相关功能类,再根据相关功能类中的表达变异基因进行聚类分析。实验结果显示：结合基因功能体系进一步筛选表达变异基因作为聚类特征基因,可以保持或提高聚类准确性,并使得聚类结果具有明确的生物学意义。另外,发现了一些可能和淋巴瘤和白血病相关的基因。     

基于基因表达谱的肿瘤分型和特征基因选取

在分析基因表达谱数据特性的基础上,提出了一个将之用于肿瘤分子分型和选型和选取相应亚型特征基因的策略。该策略包括三个步骤：首先采用一个无监督的基因过滤算法以降低用于分型计算的数据的噪声,其次提出了一个概率模型对样本中的分类结构进行建模,最后基于聚类的结果采用相对熵的方法获得对分类贡献大的基因作为特征基因,应用该策略对两个公开发表的数据集进行了再挖掘,结果表明不但获得了其他方法可以得到的信息,而且还提供了更精细、更具有显著生物学意义的信息,具有明显的优越性。     

基于贝叶斯网潜类模型的高维SNPs分析

采用贝叶斯(Bayesian)网的潜类模型对GAW17高维SNPs数据进行分析,为复杂性状疾病遗传以及基因定位等方面的研究提供新的方法支持。本研究从GAW17提供的包含697个个体22条常染色体的上万个SNP中,随机挑选出1号染色体上12个基因的29个SNPs作为研究对象。按照累计信息贡献率达到95%的原则,应用贝叶斯网潜变量模型选出C1S11408,C1S3201,C1S1786等15个与X0互信息量大的SNPs位点来对研究人群进行分类与解释。结果表明697个个体总的被分为2个潜在类别,各类别的概率分别为0.68和0.32。对两类人群的疾病分布状况进行分析,结果表明二者不一致,第二个类别人群患病率(38.64%)明显高于第一个类别人群(25.99%)(χ2=11.46,P=0.001)。由此可见,两类人群疾病患病率的差别正是由选出的15个SNPs造成的,从而有理由认为这些SNPs为可疑致病位点,为进一步的研究提供明确的思路。     

特征代谢通路上的基因表达相关性及共调控表达模式

利用随机森林-通路分析法,通过袋外样本OOB的分类错误率筛选特征代谢通路,在特征通路上作基因表达相关性研究并对通路上的基因采用MAP(Mining attribute profile)算法挖掘不同实验条件下基因的共调控表达模式,对共调控表达模式进行聚类.分析结果显示同一特征代谢通路上的基因表达倾向相似,有2条特征代谢通路存在共表达模式.其中一条通路含108个表达模式,对这些模式进行聚类,其最低聚类的相似系数仍高达0.623.说明同一特征代谢通路上的基因共表达模式在不同实验条件下仍具有高度的相似性.对以通路作为基因模块进行复杂疾病的研究具有借鉴意义.     

Assessment of repeatability of surface electromyography signals by singular value decomposition

Singular value decomposition (SVD) of full-wave rectified surface electromyography (sEMG) signals was investigated for repeatability indices of sEMG linear envelopes (LE) during biceps curl. The SVD based repeatability indices were compared with a well-known method, the variance ratio (VR). The usefulness of the offered indices was examined by a simulation and it was applied to the sEMG LEs. The results have shown that the VRs were correlated with the SVD based indices significantly. The usefulness of the offered indices on real world EMG signals practically comes from decreasing amplitudes of the first few singular values of EMG LE matrix. If repeatability is high, singular values decay fast and vice versa.If justified by further researches, the offered indices may be used practically for repeatability measurement of sEMG LEs.     

SVDMAN--singular value decomposition analysis of microarray data

SUMMARY: We have developed two novel methods for Singular Value Decomposition analysis (SVD) of microarray data. The first is a threshold-based method for obtaining gene groups, and the second is a method for obtaining a measure of confidence in SVD analysis. Gene groups are obtained by identifying elements of the left singular vectors, or gene coefficient vectors, that are greater in magnitude than the threshold W N(-1/2), where N is the number of genes, and W is a weight factor whose default value is 3. The groups are non-exclusive and may contain genes of opposite (i.e. inversely correlated) regulatory response. The confidence measure is obtained by systematically deleting assays from the data set, interpolating the SVD of the reduced data set to reconstruct the missing assay, and calculating the Pearson correlation between the reconstructed assay and the original data. This confidence measure is applicable when each experimental assay corresponds to a value of parameter that can be interpolated, such as time, dose or concentration. Algorithms for the grouping method and the confidence measure are available in a software application called SVD Microarray ANalysis (SVDMAN). In addition to calculating the SVD for generic analysis, SVDMAN provides a new means for using microarray data to develop hypotheses for gene associations and provides a measure of confidence in the hypotheses, thus extending current SVD research in the area of global gene expression analysis.     

Singular value decomposition analysis of protein sequence alignment score data.

One of the standard tools for the analysis of data arranged in matrix form is singular value decomposition (SVD). Few applications to genomic data have been reported to date mainly for the analysis of gene expression microarray data. We review SVD properties, examine mathematical terms and assumptions implicit in the SVD formalism, and show that SVD can be applied to the analysis of matrices representing pairwise alignment scores between large sets of protein sequences. In particular, we illustrate SVD capabilities for data dimension reduction and for clustering protein sequences. A comparison is performed between SVD-generated clusters of proteins and annotation reported in the SWISS-PROT Database for a set of protein sequences forming the calycin superfamily, entailing all entries corresponding to the lipocalin, cytosolic fatty acid-binding protein, and avidin-streptavidin Prosite patterns.     

Unsupervised feature selection via two-way ordering in gene expression analysis

MOTIVATION: Selection of genes most relevant and informative for certain phenotypes is an important aspect in gene expression analysis. Most current methods select genes based on known phenotype information. However, certain set of genes may correspond to new phenotypes which are yet unknown, and it is important to develop novel effective selection methods for their discovery without using any prior phenotype information. RESULTS: We propose and study a new method to select relevant genes based on their similarity information only. The method relies on a mechanism for discarding irrelevant genes. A two-way ordering of gene expression data can force irrelevant genes towards the middle in the ordering and thus can be discarded. Mechanisms based on variance and principal component analysis are also studied. When applied to expression profiles of colon cancer and leukemia, the unsupervised method outperforms the baseline algorithm that simply uses all genes, and it also selects relevant genes close to those selected using supervised methods. SUPPLEMENT: More results and software are online: http://www.nersc.gov/~cding/2way.     

Gene selection and classification of microarray data using random forest

Background  

Selection of relevant genes for sample classification is a common task in most gene expression studies, where researchers try to identify the smallest possible set of genes that can still achieve good predictive performance (for instance, for future use with diagnostic purposes in clinical practice). Many gene selection approaches use univariate (gene-by-gene) rankings of gene relevance and arbitrary thresholds to select the number of genes, can only be applied to two-class problems, and use gene selection ranking criteria unrelated to the classification algorithm. In contrast, random forest is a classification algorithm well suited for microarray data: it shows excellent performance even when most predictive variables are noise, can be used when the number of variables is much larger than the number of observations and in problems involving more than two classes, and returns measures of variable importance. Thus, it is important to understand the performance of random forest with microarray data and its possible use for gene selection.     

Identification of conserved modes of expression profiles during hippocampal development and neuronal differentiation in vitro

Gene expression profiles can be regarded as sums of simpler modes, analogous to the modes of a vibrating violin string. Decomposition of temporal gene expression profiles into modes by singular value decomposition (SVD) was reported before, but the question as to what degree the SVD modes can be interpreted in terms of biology remains open. We report and compare the results of SVD of published datasets from hippocampal development, neuronal differentiation in vitro, and a control time-series hippocampal dataset. We demonstrate that the first SVD mode reflects the magnitude of expression, interpretable on the Affymetrix platform. In the datasets from gene profiling of hippocampal development and neuronal differentiation, the second mode reflects a monotonous change in expression, either up- or down-regulation, in the time course of experiment. We demonstrate that the top two SVD modes are conserved between datasets and therefore, likely reflect properties of the underlying system (gene expression in hippocampus) rather than of a particular experiment or dataset. Our results also indicate that the magnitude of expression, and the direction of change in expression during hippocampal development, are uncorrelated, suggesting that they are regulated by largely independent mechanisms.