Evolutionary computation, machine learning and data mining in bioinformatics. EvoBIO (Conference)最新文献

Rarely occurring genetic variants are hypothesized to influence human diseases, but statistically associating these rare variants to disease is challenging due to a lack of statistical power in most feasibly sized datasets. Several statistical tests have been developed to either collapse multiple rare variants from a genomic region into a single variable (presence/absence) or to tally the number of rare alleles within a region, relating the burden of rare alleles to disease risk. Both these approaches, however, rely on user-specification of a genomic region to generate these collapsed or burden variables, usually an entire gene. Recent studies indicate that most risk variants for common diseases are found within regulatory regions, not genes. To capture the effect of rare alleles within non-genic regulatory regions for burden tests, we contrast a simple sliding window approach with a knowledge-guided k-medoids clustering method to group rare variants into statistically powerful, biologically meaningful windows. We apply these methods to detect genomic regions that alter expression of nearby genes.

很少发生的遗传变异被假设会影响人类疾病，但由于在大多数可行规模的数据集中缺乏统计能力，将这些罕见变异与疾病联系起来是具有挑战性的。已经开发了几种统计测试，要么将基因组区域的多个罕见变异分解为单个变量(存在/不存在)，要么统计一个区域内罕见等位基因的数量，将罕见等位基因的负担与疾病风险联系起来。然而，这两种方法都依赖于用户指定的基因组区域来生成这些崩溃或负担变量，通常是一个完整的基因。最近的研究表明，大多数常见疾病的风险变异是在调控区域内发现的，而不是在基因内。为了捕捉非基因调控区域中罕见等位基因对负荷测试的影响，我们将简单的滑动窗口方法与知识引导的k- medioids聚类方法进行对比，将罕见变异分组为统计上强大的、生物学上有意义的窗口。我们应用这些方法来检测改变附近基因表达的基因组区域。

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引用次数: 2

Biomedical text categorization with concept graph representations using a controlled vocabulary 使用受控词汇表的概念图表示的生物医学文本分类

Evolutionary computation, machine learning and data mining in bioinformatics. EvoBIO (Conference)

Pub Date : 2012-08-12 DOI: 10.1145/2350176.2350181

Meenakshi Mishra, Jun Huan, S. Bleik, Min Song

Recent work using graph representations for text categorization has shown promising performance over conventional bag-of-words representation of text documents. In this paper we investigate a graph representation of texts for the task of text categorization. In our representation we identify high level concepts extracted from a database of controlled biomedical terms and build a rich graph structure that contains important concepts and relationships. This procedure ensures that graphs are described with a regular vocabulary, leading to increased ease of comparison. We then classify document graphs by applying a set-based graph kernel that is intuitively sensible and able to deal with the disconnectedness of the constructed concept graphs. We compare this approach to standard approaches using non-graph, text-based features. We also do a comparison amongst different kernels that can be used to see which performs better.

最近使用图表示进行文本分类的工作显示出比传统的词袋表示文本文档更有希望的性能。本文研究了一种用于文本分类任务的文本图表示。在我们的表示中，我们从受控生物医学术语数据库中提取高级概念，并构建包含重要概念和关系的丰富图结构。此过程确保使用常规词汇表描述图，从而增加了比较的便利性。然后，我们通过应用基于集合的图核对文档图进行分类，该核是直观的，能够处理构建的概念图的不连接性。我们将这种方法与使用非图形、基于文本的特征的标准方法进行比较。我们还会对不同的内核进行比较，看看哪个性能更好。

引用次数: 21

Globalized bipartite local model for drug-target interaction prediction 药物-靶标相互作用预测的全球化二部局部模型

Evolutionary computation, machine learning and data mining in bioinformatics. EvoBIO (Conference)

Pub Date : 2012-08-12 DOI: 10.1145/2350176.2350178

Jianxiang Mei, C. Kwoh, Peng Yang, X. Li, Jie Zheng

In pharmacology, it is essential to identify the interactions between drug and targets to understand its effects. Supervised learning with Bipartite Local Model (BLM) recently has been shown to be effective for prediction of drug-target interactions by first predicting target proteins of a given known drug, then predicting drugs targeting a known protein. However, this pure "local" model is inapplicable to new drug or target candidates that currently have no known interactions. In this paper, we extend the existing BLM method by integrating a strategy for handling new drug and target candidates. Based on the assumption that similar drugs and targets have similar interaction profiles, we present a simple neighbor-based training data inferring procedure and integrate it into the frame work of BLM. This globalized BLM called bipartite local model with neighbor-based inferring (BLMN) then has an extended functionality for prediction interactions between new drug candidates and new target candidates. Good performance of BLMN has been observed in the experiment of predicting interactions between drugs and four important categories of targets. For the Nuclear Receptors dataset, where there are more chances for the presented strategy to be applied, 20% improvement in terms of AUPR was achieved. This demonstrates the effectiveness of BLMN and its potential in prediction of drug-target interactions.

在药理学中，确定药物与靶点之间的相互作用是了解其作用的必要条件。基于Bipartite Local Model (BLM)的监督学习最近被证明是预测药物-靶标相互作用的有效方法，它首先预测给定已知药物的靶蛋白，然后预测靶向已知蛋白质的药物。然而，这种纯粹的“局部”模型不适用于目前没有已知相互作用的新药或候选靶点。在本文中，我们通过整合处理新药和候选靶点的策略来扩展现有的BLM方法。基于相似药物和靶点具有相似的相互作用特征的假设，我们提出了一种简单的基于邻域的训练数据推断方法，并将其整合到BLM框架中。这种全球化的BLM被称为基于邻居推理的二部局部模型(bipartite local model with neighbor-based inference, BLMN)，具有预测新药候选物和新靶标候选物之间相互作用的扩展功能。在预测药物与四种重要靶点相互作用的实验中，已经观察到BLMN具有良好的性能。对于核受体数据集，所提出的策略有更多的机会被应用，AUPR方面提高了20%。这证明了BLMN的有效性及其在预测药物-靶标相互作用方面的潜力。

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引用次数: 5

2D similarity kernels for biological sequence classification 生物序列分类的二维相似核

Evolutionary computation, machine learning and data mining in bioinformatics. EvoBIO (Conference)

Pub Date : 2012-08-12 DOI: 10.1145/2350176.2350179

P. Kuksa

String kernel-based machine learning methods have yielded great success in practical tasks of structured/sequential data analysis. They often exhibit state-of-the-art performance on tasks such as document topic elucidation, biological sequence classification, or protein superfamily and fold prediction. However, typical string kernel methods rely on analysis of discrete 1D string data (e.g., DNA or amino acid sequences). This work introduces new 2D kernel methods for sequence data in the form of sequences of feature vectors (as in biological sequence profiles, or sequences of individual amino acid physico-chemical descriptors). On three protein sequence classification tasks proposed 2D kernels show significant 15-20% improvements compared to state-of-the-art sequence classification methods.

基于字符串核的机器学习方法在结构化/顺序数据分析的实际任务中取得了巨大的成功。它们通常在文档主题解释、生物序列分类或蛋白质超家族和折叠预测等任务上表现出最先进的性能。然而，典型的字符串核方法依赖于对离散的一维字符串数据(例如DNA或氨基酸序列)的分析。这项工作以特征向量序列的形式引入了新的二维核方法(如生物序列剖面，或单个氨基酸物理化学描述符的序列)。在三个蛋白质序列分类任务中，与最先进的序列分类方法相比，提出的2D核函数显示出15-20%的显著改进。

引用次数: 6

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