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    Network-based approaches to identifying significant molecules based on high-throughput data analysis
    31.
    发明申请
    Network-based approaches to identifying significant molecules based on high-throughput data analysis 审中-公开
    基于网络的基于高通量数据分析识别重要分子的方法

    公开(公告)号：US20070174019A1

    公开(公告)日：2007-07-26

    申请号：US11264259

    申请日：2005-10-31

    申请人： Aditya Vailaya ,  Allan Kuchinsky ,  Euan Ashley ,  Jennifer King ,  Rossella Ferrara ,  Thomas Quertermous

    发明人： Aditya Vailaya ,  Allan Kuchinsky ,  Euan Ashley ,  Jennifer King ,  Rossella Ferrara ,  Thomas Quertermous

    IPC分类号： G06F17/18

    CPC分类号： G16B5/00 ,  G16B20/00

    摘要： Methods, systems and computer readable media for network-based identification of significant molecules, for which at least one biological network is provided to include significant molecules to be identified. A node in the network is identified. A member-specific sub-network containing nodes connected to the identified node is identified for L levels of nearest neighbors, wherein L is a positive integer, and a connectivity score is calculated for the molecule represented by the identified node based on significance scores of each node contained in the member-specific sub-network. These steps are repeated for other nodes in the network. Methods, systems and computer readable media for network-based identification of significant molecules, for which at least one biological network is provided to include significant molecules to be identified, a data set including data values characterizing molecules experimented on is provided, and an interesting list of molecules is provided as a subset of the molecules from the dataset, the interesting list including significance scores for the molecules in the list. Such identification includes identifying a node in the network; identifying a member-specific sub-network containing nodes connected to the identified node for L levels of nearest neighbors, wherein L is a positive integer; extracting the member-specific sub-network from the network; and repeating these steps for each of the other nodes in the network that corresponds to a molecule in the interesting list.

    摘要翻译： 用于基于网络识别重要分子的方法，系统和计算机可读介质，其中提供至少一个生物网络以包括要识别的重要分子。 识别网络中的节点。 针对L个最近邻的L个级别识别包含连接到所识别节点的节点的成员特定子网络，其中L是正整数，并且基于由所识别的节点表示的分子计算连通性得分，基于每个 节点包含在成员特定子网中。 对网络中的其他节点重复这些步骤。 提供用于基于网络识别显着分子的方法，系统和计算机可读介质，其中提供至少一个生物网络以包括要识别的重要分子，提供包括表征实验分子的数据值的数据集，以及有趣的列表 的分子作为来自数据集的分子的子集提供，有趣的列表包括列表中分子的显着性得分。 这种识别包括识别网络中的节点; 识别包含连接到所识别的节点的节点的成员特定子网络用于最近邻的L级，其中L是正整数; 从网络中提取成员专用子网; 并且对于与有趣列表中的分子相对应的网络中的每个其他节点重复这些步骤。

    Systems, methods and computer readable media for performing a domain-specific metasearch, and visualizing search results therefrom
    32.
    发明申请
    Systems, methods and computer readable media for performing a domain-specific metasearch, and visualizing search results therefrom 失效
    用于执行域特定元搜索的系统，方法和计算机可读介质，以及从其中可视化搜索结果

    公开(公告)号：US20050278321A1

    公开(公告)日：2005-12-15

    申请号：US11166696

    申请日：2005-06-24

    申请人： Aditya Vailaya ,  Allan Kuchinsky ,  Robert Kincaid

    发明人： Aditya Vailaya ,  Allan Kuchinsky ,  Robert Kincaid

    IPC分类号： G06F7/00 ,  G06F19/28

    CPC分类号： G06F17/30864 ,  G06F17/30991 ,  G06F19/28 ,  Y10S707/99935 ,  Y10S707/99943

    摘要： Systems, methods and computer readable media for performing a domain-specific metasearch, and obtaining search results therefrom. A metasearch engine capable of accessing generic, web-based search engines and domain-relevant search engines is provided to receive one or more queries inputted by a user, and to search for documents on at least one the generic, web-based search engines and domain-relevant search engines which are relevant to the queries. Raw data search results are fetched in the form of text documents. Relevant data including semantic information are extracted from the raw data search results, and converted to a local format. The relevant data having been converted to the local format may be visualized as a network visualization. Additionally or alternatively, the raw data search results may be ranked and/or filtered based on the linking of the relevant data. Visualization of the raw data having been ranked and/or filtered may be performed in addition to, or alternative to visualization of the network.

    摘要翻译： 用于执行域特定元搜索的系统，方法和计算机可读介质，以及从其获得搜索结果。 提供了能够访问通用的，基于网络的搜索引擎和域相关搜索引擎的元搜索引擎，用于接收用户输入的一个或多个查询，并且在至少一个通用的基于web的搜索引擎上搜索文档，以及 与查询相关的域相关搜索引擎。 原始数据搜索结果以文本文档的形式提取。 从原始数据搜索结果中提取包括语义信息的相关数据，并将其转换为本地格式。 已经转换为本地格式的相关数据可以被可视化为网络可视化。 另外或替代地，可以基于相关数据的链接对原始数据搜索结果进行排名和/或过滤。 已经排序和/或过滤的原始数据的可视化可以除了网络的可视化之外或替代以进行。

    Methods and systems, for ontological integration of disparate biological data
    33.
    发明申请
    Methods and systems, for ontological integration of disparate biological data 审中-公开
    用于本体论整合不同生物数据的方法和系统

    公开(公告)号：US20050216459A1

    公开(公告)日：2005-09-29

    申请号：US11128896

    申请日：2005-05-12

    申请人： Aditya Vailaya ,  Robert Kincaid

    发明人： Aditya Vailaya ,  Robert Kincaid

    IPC分类号： G06F7/00 ,  G06F19/12

    CPC分类号： G16B50/00 ,  G16B5/00

    摘要： Methods, systems and computer readable media for correlating data from data sets to higher level categories of characterization of the data. Data from a first set of data is analyzed to determine where members of the first set map to an ontology. Data from a second set of data is analyzed to determine where members of the second set map to the ontology. From such analysis a subset of the first set of data is identified and a subset of the second set of data is identified. The subset of the first set of data is statistically analyzed with regard to its mapping to the ontology, and a first set of ontology terms are identified that are statistically differentiated by members of the subset of the first set of data. The subset of the second set of data is statistically analyzed with regard to its mapping to the ontology, and a second set of ontology terms is identified that are statistically differentiated by members of the subset of the second set of data. Correlation of the first set of ontology terms with the second set of ontology terms may further be performed.

    摘要翻译： 用于将来自数据集的数据与数据的更高级别表征相关联的方法，系统和计算机可读介质。 分析来自第一组数据的数据，以确定第一组的成员映射到本体的位置。 分析来自第二组数据的数据以确定第二集合的成员映射到本体的位置。 从这样的分析中，识别第一组数据的子集，并且识别第二组数据的子集。 第一组数据的子集关于其对本体的映射进行统计分析，并且识别由第一组数据的子集的成员统计地区分的第一组本体术语。 第二组数据的子集关于其与本体的映射进行统计分析，并且识别由第二组数据的子集的成员统计地区分的第二组本体术语。 可以进一步执行第一组本体项与第二组本体术语的相关性。

    Fast microarray expression data analysis method for network exploration
    35.
    发明授权
    Fast microarray expression data analysis method for network exploration 失效
    快速微阵列表达数据分析方法进行网络探索

    公开(公告)号：US07266473B1

    公开(公告)日：2007-09-04

    申请号：US11354386

    申请日：2006-02-15

    申请人： Valery Kanevsky ,  Aditya Vailaya

    发明人： Valery Kanevsky ,  Aditya Vailaya

    IPC分类号： G06F17/18

    CPC分类号： G06F19/24 ,  G06F19/20

    摘要： A method for feature selection is provided. The method includes the steps of selecting a predictor set of features, adding at least one complementary feature to the predictor set based on a quality of prediction, checking to see if all of the features of the predictor set are repeated, and if not, removing at least one feature from the predictor set. The algorithm and method repeats the steps of adding complements, checking the predictor set and removing features until the features of the predictor set are repeated. Once the features of. the predictor set are repeated the proper number of times, the algorithm and method terminate.

    摘要翻译： 提供了一种特征选择方法。 该方法包括以下步骤：选择特征的预测器集合，基于预测质量将至少一个互补特征添加到预测器集合，检查是否重复预测器集合的所有特征，以及如果不是，则移除 来自预测变量集的至少一个特征。 算法和方法重复添加补全，检查预测值集和删除特征的步骤，直到重复预测变量集的特征。 一旦功能。 预测器集合重复适当次数，算法和方法终止。

    FAST MICROARRAY EXPRESSION DATA ANALYSIS METHOD FOR NETWORK EXPLORATION
    36.
    发明申请
    FAST MICROARRAY EXPRESSION DATA ANALYSIS METHOD FOR NETWORK EXPLORATION 失效
    用于网络探索的快速微观表达数据分析方法

    公开(公告)号：US20070192061A1

    公开(公告)日：2007-08-16

    申请号：US11354386

    申请日：2006-02-15

    申请人： Valery Kanevsky ,  Aditya Vailaya

    发明人： Valery Kanevsky ,  Aditya Vailaya

    IPC分类号： G06F19/00

    CPC分类号： G06F19/24 ,  G06F19/20

    摘要： A method for feature selection is provided. The method includes the steps of selecting a predictor set of features, adding at least one complementary feature to the predictor set based on a quality of prediction, checking to see if all of the features of the predictor set are repeated, and if not, removing at least one feature from the predictor set. The algorithm and method repeats the steps of adding complements, checking the predictor set and removing features until the features of the predictor set are repeated. Once the features of. the predictor set are repeated the proper number of times, the algorithm and method terminate.

    摘要翻译： 提供了一种特征选择方法。 该方法包括以下步骤：选择特征的预测器集合，基于预测质量将至少一个互补特征添加到预测器集合，检查是否重复预测器集合的所有特征，以及如果不是，则移除 来自预测变量集的至少一个特征。 算法和方法重复添加补全，检查预测值集和删除特征的步骤，直到重复预测变量集的特征。 一旦功能。 预测器集合重复适当次数，算法和方法终止。