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    • 4. 发明申请
    • NORMALIZING SPECTROSCOPY DATA WITH MULTIPLE INTERNAL STANDARDS
    • 通过多种内部标准对光谱数据进行正规化处理
    • WO2007147938A1
    • 2007-12-27
    • PCT/FI2007/050356
    • 2007-06-14
    • VALTION TEKNILLINEN TUTKIMUSKESKUSORESIC, Matej
    • ORESIC, Matej
    • G01N30/86G01N30/72G06F17/18
    • G01N30/8624G01N30/7233
    • Normalization of spectra, comprising: preparing (1-2) experiment runs; processing (1-4) them in an LC/MS spectrometer to obtain a spectrum for each experiment run; internally representing (1-10) each spectrum as mass/charge (m/z) versus retention time (rt); performing a peak detection (1-12) of each spectrum; internally aligning (1-14) the detected peaks; and normalizing (1-18) the spectra, which comprises modelling variation of Y i j , denoted δY ij , as a function of variability of Ω , denoted f(δΩ) . δ denotes variability of a quantity, (the quantity's deviation from an average value of the quantity over the sample runs); X = X ij = intensity matrix for ail peaks, mapped to Y via a first transformation function / such that Y = f 1 (X); Z = Z ij = intensity matrix for internal standard peaks (IS 1 - IS 4 ), mapped to Ω via a second transformation function t such that Ω = f 1 (Z). i denotes peaks: i → {m/z, rt} and i = 1... N ; and j denotes experiment runs.
    • 光谱的归一化,包括:制备(1-2)实验运行; 在LC / MS光谱仪中处理(1-4)它们以获得每个实验运行的光谱; 内部表示(1-10)每个光谱作为质量/电荷(m / z)对保留时间(rt); 执行每个频谱的峰值检测(1-12); 内部校准(1-14)检测到的峰; 并且归一化(1-18)光谱,其包括作为表示为f(SO)的O的变异性的函数的表示为Y ij ij的Y Y ij的建模变化。 d表示量的变异性(数量与样品运行数量的平均值的偏差); X = X ij =用于通过第一变换函数映射到Y的所述峰的强度矩阵,使得Y = f 1(X); Z = Z ij ij =用于内部标准峰(IS 1 -I 4)的强度矩阵,经由第二变换函数t映射到O 那么O = f 1(Z)。 我表示峰:i? {m / z,rt}和i = 1 ... N; j表示实验运行。
    • 5. 发明申请
    • INFORMATION MANAGEMENT TECHNIQUES FOR METABOLISM-RELATED DATA
    • 代谢相关数据的信息管理技术
    • WO2007128882A1
    • 2007-11-15
    • PCT/FI2007/050261
    • 2007-05-09
    • VALTION TEKNILLINEN TUTKIMUSKESKUSORESIC, Matej
    • ORESIC, Matej
    • G06F19/00G01N30/72G01N33/50G01N33/92G01N33/487
    • G06F19/28G06F19/12G06F19/26G06F19/703G06F19/708G06F19/709
    • A method for processing information on compounds of molecular classes sharing common building blocks. The method comprises maintaining pathway information on the compounds at individual compound level and/or generic class level (13-1); generating a diversity of the compounds based on a set of seed structures, each seed structure describing a lipid compound having a higher- than-average likelihood to occur in nature (13-2); using a formal description language to express the seed structures (13-3); using the structural elements to generate expected spectra for each compound, by using known experimental conditions for mass spectrometry (13-4); performing one or more spectroscopy experiments to obtain compound information (13-5); and linking the obtained compound information to existing information on the molecular classes (13-6).
    • 用于处理分享共同共同构件的分子类化合物信息的方法。 该方法包括在个体化合物水平和/或一般类别水平上维持化合物的途径信息(13-1); 基于一组种子结构产生多种化合物,每个种子结构描述了具有高于平均可能性的自然界中的脂质化合物(13-2); 使用正式的描述语言来表达种子结构(13-3); 通过使用已知的质谱实验条件(13-4),使用结构元件产生每种化合物的预期光谱; 执行一个或多个光谱实验以获得化合物信息(13-5); 并将获得的化合物信息与分子级别上的现有信息(13-6)连接。
    • 7. 发明申请
    • ANALYSIS TECHNIQUES FOR LIQUID CHROMATOGRAPHY/MASS SPECTROMETRY
    • 液相色谱/质谱分析技术
    • WO2006125863A1
    • 2006-11-30
    • PCT/FI2006/050208
    • 2006-05-24
    • VALTION TEKNILLINEN TUTKIMUSKESKUSORESIC, MatejKATAJAMAA, Mikko
    • ORESIC, MatejKATAJAMAA, Mikko
    • G01N30/72
    • G01N30/8675G01N30/7233G01N30/8631
    • A method for analyzing liquid chromatography/mass spectrometry [=”CL/MS”] data comprises: preparing (1-2) a plurality of sample runs; processing (1-4) each of the prepared sample runs in an LC/MS spectrometer to obtain a spectrum in respect of each processed sample run; internally representing (1-10) each spectrum as a layout of mass/charge versus retention time; performing a first peak detection (1-12) to detect peaks of each spectrum; visualizing peaks of each spectrum, wherein the visualizing step comprises: mapping (1-22) each peak to be visualized to a coordinate system in which a first coordinate indicates mass/charge ratio and a second coordinate indicates retention time; and assigning (1-24) a specific visual attribute to each peak to be visualized.
    • 分析液相色谱/质谱法[=“CL / MS”]数据的方法包括:制备(1-2)多个样品流程; 处理(1-4)每个所制备的样品在LC / MS光谱仪中运行以获得关于每个处理的样品运行的光谱; 内部代表(1-10)每个频谱作为质量/电荷与保留时间的布局; 执行第一峰值检测(1-12)以检测每个频谱的峰值; 每个光谱的可视化峰值,其中可视化步骤包括:将要显现的每个峰映射(1-22)到其中第一坐标表示质量/电荷比的坐标系,而第二坐标表示保留时间; 并为每个需要可视化的峰分配(1-24)特定的视觉属性。
    • 8. 发明申请
    • VISUALIZATION TECHNIQUE FOR BIOLOGICAL INFORMATION
    • 生物信息可视化技术
    • WO2006114479A1
    • 2006-11-02
    • PCT/FI2006/050163
    • 2006-04-26
    • VALTION TEKNILLINEN TUTKIMUSKESKUSORESIC, MatejLINDFORS, ErnoPEDDINTI, Gopalacharyulu
    • ORESIC, MatejLINDFORS, ErnoPEDDINTI, Gopalacharyulu
    • G06F19/00G06F17/30
    • G06F17/30545G06F19/12G06F19/26G06F19/28
    • Method/system for visualizing biological information. The system receives (2-2) a user query relating to biological information and determines (2-4) which database contains related biological information. A database query is sent (2-6) to the database and the result (2-8) indicates biological/chemical entities and relations. A network is created (2-10) based on the result; biological/chemical entities are mapped to network nodes and relations to network connections. A distance matrix (2-12) indicates a multi-dimensional distance for several pairs of network nodes. A dimensionality reduction function is adjusted (2- 14) based on a research context, to bias the search toward a relevant focus. The number of dimensions in the distance matrix is lowered (2-16) by the dimensionality reduction function. Neighbours of a selected node are searched (2-18) based on the distance matrix to elucidate a biological role of the selected node. A re-created network is visualised (2-20) based on the adjusted dimensionality reduction function.
    • 用于可视化生物信息的方法/系统 系统接收(2-2)与生物信息相关的用户查询,并确定(2-4)哪个数据库包含相关生物信息。 数据库查询(2-6)发送到数据库,结果(2-8)表示生物/化学实体和关系。 根据结果​​创建网络(2-10); 生物/化学实体被映射到网络节点和与网络连接的关系。 距离矩阵(2-12)表示多对网络节点的多维距离。 基于研究背景调整维数降低函数(2-14),将搜索偏向于相关焦点。 距离矩阵中的维数由维度降低函数降低(2-16)。 根据距离矩阵搜索所选节点的邻居(2-18),以阐明所选节点的生物学作用。 基于经调整的维数降低功能可重构网络(2-20)。