关键词： CIS-REGULATORY MODULES   FACTOR-BINDING SITES   GENE-EXPRESSION   GENOME SEQUENCE   IDENTIFICATION   DISCOVERY   NETWORKS   CLASSIFICATION   PREDICTION   LOGIC  

摘要： Gene expression is modulated by transcription factors (TFs), which are proteins that generally hind to DNA adjacent to coding regions and initiate transcription. Each target gene can be regulated by more than one TF, and each TF can regulate many targets. For a complete molecular understanding of transcriptional regulation, researchers must first associate each TF with the set of genes that it regulates. Here we present a summary of completed work on the ability to associate 104 TFs with their binding sites using support vector machines (SVMs), which are classfication algorithms based in statistical learning theory. We use several types of genomic datasets to train classifiers in order to predict TF binding in the yeast genome. We consider motif matches, subsequence counts, motif conservation, functional annotation, and expression profiles. A simple weighting scheme varies the contribution of each type of genomic data when building a final SVM classifier, which we evaluate using known binding sites published in the literature and in online databases. The SVM algorithm works best when all datasets are combined, producing 73% coverage of known interactions, with a prediction accuracy of almost 0.9. We discuss new ideas and preliminary work for improving SVM classification of biological data.

关键词： mean opinion score (MOS)   objective quality measurement   quality model   single-ended measurement   speech communication   speech distortions   speech enhancement   speech quality   subjective quality   OBJECTIVE ESTIMATION  

摘要： We describe a novel single-ended algorithm constructed from models of speech signals, including clean and degraded speech, and speech corrupted by multiplicative noise and temporal discontinuities. Machine learning methods are used to design the models, including Gaussian mixture models, support vector machines, and random forest classifiers. Estimates of the subjective mean opinion score (MOS) generated by the models are combined using hard or soft decisions generated by a classifier which has learned to match the input signal with the models. Test results show the algorithm outperforming ITU-T P.563, the current "state-of-art" standard single-ended algorithm. Employed in a distributed double-ended measurement configuration, the proposed algorithm is found to be more effective than P.563 in assessing the quality of noise reduction systems and can provide a functionality not available with P.862 PESQ, the current double-ended standard algorithm.

关键词： BIOMEDICAL LITERATURE   GENE   IDENTIFICATION   RANKING  

摘要： Motivation: The use or study of chemical compounds permeates almost every scientific field and in each of them, the amount of textual information is growing rapidly. There is a need to accurately identify chemical names within text for a number of informatics efforts such as database curation, report summarization, tagging of named entities and keywords, or the development/curation of reference databases. Results: A first-order Markov Model (MM) was evaluated for its ability to distinguish chemical names from words, yielding similar to 93% recall in recognizing chemical terms and similar to 99% precision in rejecting non-chemical terms on smaller test sets. However, because total false-positive events increase with the number of words analyzed, the scalability of name recognition was measured by processing 13.1 million MEDLINE records. The method yielded precision ranges from 54.7% to 100%, depending upon the cutoff score used, averaging 82.7% for approximately 1.05 million putative chemical terms extracted. Extracted chemical terms were analyzed to estimate the number of spelling variants per term, which correlated with the total number of times the chemical name appeared in MEDLINE. This variability in term construction was found to affect both information retrieval and term mapping when using PubMed and Ovid.

关键词： 非线性配准   机器学习   最优属性向量  

摘要： 针对基于属性向量的非线性配准算法,提出用机器学习的方法寻找脑图像中各个点上的最优几何特征向量。通过定义一个能量函数把寻找最优属性向量的过程归结为一个最优化问题。把训练得到的最优属性向量与HAMMER(一种基于属性向量的非线性配准算法)相结合,对模拟的MR脑图像进行了实验．与HAMMER相比,位移场的精度提高了10%。改进后的算法对真实的MR脑图像的配准结果,也有很大的改善。

关键词： 机器学习   生物信息学   学习方法   数据库   基因  

摘要： 分析了机器学习技术的内容,介绍了生物信息学的内涵和新的应用技术,同时探索了机器学习技术对生物信息挖掘应用的途径.这些方法有助于加速生物分子结构预测、基因发现、基因组学和蛋白组学等方面研究的进展.

关键词： 机器学习方法   中医理论   复杂系统性  

摘要： 论述了机器学习方法的产生和基本内容,论述了中医药的复杂性,从事例到理论上说明两者之间的内在联系性,特别论述了现代基因决定论下中医理论的发展前景。

关键词： 遗传算法   机器学习   多目标优化   Pareto最优解  

摘要： 将遗传算法与机器学习相结合,在分类器系统的基础上,引入增强因子、排挤因子、合并因子等改进因子,完善信度分配机制,提出了改进的遗传机器学习方法.并将算法应用于投资的收益与风险双目标优化模型,数值结果表明,改进算法能够寻求到数量更多、分布更广的Pareto最优解,并且具有较好的稳定性,避免了非成熟收敛.

关键词： 互联网   入侵检测   机器学习   计算复杂度   信号分析  

摘要： 随着互联网的飞速发展，信息安全成为影响网络应用的最严重的问题之一。入侵检测是信息安全防护体系中的一个必不可少的环节，研究互联网入侵检测对促进网络技术的进步、进一步提高网络利用效率等都具有极为重要的意义。\n 互联网入侵检测的任务就是要将互联网上的正常网络行为和攻击行为区分开来。更进一步的，要将不同种类的网络攻击行为区分开来。传统的入侵检测主要是采用信号分析或者统计学中的方法，但这种传统的入侵检测思路不适应网络行为的多样性和攻击手段的迅速发展。近几年来，利用机器学习中的理论与方法来解决入侵检测成为一个热点。\n 本文面向入侵检测领域，对目前国际上流行的一些机器学习的方法进行了研究，涉及到有监督学习、非监督学习和主动学习等多个方面。总体来说，在本文中，主要的工作和贡献有：\n 1.利用离散Adaboost算法设计并实现了基于有监督学习的入侵检测系统。采用了改进的初始权值设置方式和简单的避免过学习策略，使得我们的基于Ababoost算法的入侵检测算法在保持较高的检测率的情况下，大幅度降低虚警率。同时，该入侵检测算法计算复杂度低，能够频繁重训练以应付复杂多变的网络环境，因而很有希望走向实用化。\n 2.分别利用多类谱聚类算法和优势集聚类算法设计并实现了基于非监督学习的入侵检测算法。我们采用了多个聚类容器投票的策略来部分抵消聚类容器容量不能太大从而导致准确性低的问题。但实验结果表明，单独使用这两种算法都无法取得令人满意的检测结果。\n 3.提出了非监督主动学习框架，并设计实现了层次图论聚类主动学习入侵检测算法，将优势集聚类和谱聚类进行互补，在大幅度减轻人工标记负担的情况下，保持较好的检测效果。我们的主动学习框架具有扩展性强、灵活性高、能在很大程度上解决语义鸿沟问题、模拟了人类的认知过程等优良的特性，有继续深入研究的价值。\n 总的说来，本文在机器学习方法本身及其在互联网入侵检测领域中的应用等方面做了一些有益的探索。

关键词： SERIAL ANALYSIS   RETINA   MOUSE  

摘要： Background: Retinal photoreceptors are highly specialised cells, which detect light and are central to mammalian vision. Many retinal diseases occur as a result of inherited dysfunction of the rod and cone photoreceptor cells. Development and maintenance of photoreceptors requires appropriate regulation of the many genes specifically or highly expressed in these cells. Over the last decades, different experimental approaches have been developed to identify photoreceptor enriched genes. Recent progress in RNA analysis technology has generated large amounts of gene expression data relevant to retinal development. This paper assesses a machine learning methodology for supporting the identification of photoreceptor enriched genes based on expression data. Results: Based on the analysis of publicly-available gene expression data from the developing mouse retina generated by serial analysis of gene expression ( SAGE), this paper presents a predictive methodology comprising several in silico models for detecting key complex features and relationships encoded in the data, which may be useful to distinguish genes in terms of their functional roles. In order to understand temporal patterns of photoreceptor gene expression during retinal development, a two-way cluster analysis was firstly performed. By clustering SAGE libraries, a hierarchical tree reflecting relationships between developmental stages was obtained. By clustering SAGE tags, a more comprehensive expression profile for photoreceptor cells was revealed. To demonstrate the usefulness of machine learning-based models in predicting functional associations from the SAGE data, three supervised classification models were compared. The results indicated that a relatively simple instance-based model (KStar model) performed significantly better than relatively more complex algorithms, e. g. neural networks. To deal with the problem of functional class imbalance occurring in the dataset, two data re-sampling techniques were

关键词： 贝叶斯网络   市场直销   响应模型   演化程序   决策分类树   机器学习方法  

摘要： 上一个十年中,市场直销在世界范围内有了显著增长。直销市场的扩大对定量研究提出了新的挑战。以目录直销为例。市场直销中的数据分析人员通过建立模型来识别最可能带来利润的客户,并向他们邮寄促销材料,使诉求获得最大回报。如果经费有限,那么只能向数据库中的一部分客户邮寄材料。因此,市场直销模型中最首要的目标就是判断哪些客户是最可能响应(购买产品)的。这就引入了响应预测问题。到目前为止,对该模型的研究主要来自传统的统计方法,比如逻辑回归和判别式分析。此外,机器学习方法也开始用来解决诸如破产预测和消费者选择模型等一些分类问题,较为常用的如神经网络(ANN)和决策分类树(CART)。虽然这些方法在建立预测模型上比较有效,但在市场研究的应用中尚有不足。在这篇论文中,我们提出了一种新的机器学习方法——用演化程序学习的贝叶斯网络方法,来给市场直销数据库建立消费者响应模型。贝叶斯网络在处理预测模型上有几个显著优点:在条件独立的假设基础上,贝叶斯网络模型把研究问题看成随机变量的联合概率分布,它可以处理混合数据类型和复杂的模型结构,并能够用概率推理来发掘变量之间的相互联系。应用大型市场直销数据库,本文首先把贝叶斯网络作为一种算子将数据表示成网络模型(DAG),然后应用演化程序作为随机搜索算法来寻找最优的模型。为了评估模型的适应度,平衡模型的复杂度和精确度之间的关系,本文选用最小描述长度矩阵(MDL)作为适应度函数。一旦最佳模型找到,用来描述变量之间关系的条件概率就能计算出来。被学习的贝叶斯网络给变量的每个分类均生成一个概率分数,用于交叉验证和预测。因变量的概率分数,用于预测建模。