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    • 3. 发明申请
    • METHOD FOR ESTIMATION OF INFORMATION FLOW IN BIOLOGICAL NETWORKS
    • 生物网络信息流估计方法
    • US20140040264A1
    • 2014-02-06
    • US13983651
    • 2012-01-30
    • Vinay VaradanPrateek MittalSitharthan KamalakaranNevenka DimitrovaAngel JanevskiNilanjana Banerjee
    • Vinay VaradanPrateek MittalSitharthan KamalakaranNevenka DimitrovaAngel JanevskiNilanjana Banerjee
    • G06F19/00
    • G06F19/324G16B5/00G16B25/00G16B40/00
    • The present invention relates to a method for stratifying a patient into a clinically relevant group comprising the identification of the probability of an alteration within one or more sets of molecular data from a patient sample in comparison to a database of molecular data of known phenotypes, the inference of the activity of a biological network on the basis of the probabilities, the identification of a network information flow probability for the patient via the probability of interactions in the network, the creation of multiple instances of network information flow for the patient sample and the calculation of the distance of the patient from other subjects in a patient database using multiple instances of the network information flow. The invention further relates to a biomedical marker or group of biomedical markers associated with a high likelihood of responsiveness of a subject to a cancer therapy wherein the biomedical marker or group of biomedical markers comprises altered biological pathway markers, as well as to an assay for detecting, diagnosing, graduating, monitoring or prognosticating a medical condition, or for detecting, diagnosing, monitoring or prognosticating the responsiveness of a subject to a therapy against said medical condition, in particular ovarian cancer. Furthermore, a corresponding clinical decision support system is provided.
    • 本发明涉及一种用于将患者分层为临床相关组的方法,其包括鉴定来自患者样品的一组或多组分子数据内的改变概率与已知表型的分子数据的数据库相比较, 基于概率推断生物网络的活动,通过网络中的交互概率识别患者的网络信息流概率,创建患者样本的多个网络信息流实例和 使用网络信息流的多个实例来计算患者与患者数据库中的其他受试者的距离。 本发明进一步涉及生物医学标记物或生物医学标记物组,其与受试者对癌症治疗的响应性的高可能性相关联,其中生物医学标记物或生物医学标记物组包括改变的生物学途径标志物,以及用于检测 ,诊断,毕业,监测或预测医学状况,或用于检测,诊断,监测或预测受试者对于针对所述医学病症,特别是卵巢癌的治疗的反应性。 此外,提供了相应的临床决策支持系统。
    • 4. 发明申请
    • IDENTIFICATION OF MULTI-MODAL ASSOCIATIONS BETWEEN BIOMEDICAL MARKERS
    • 鉴定生物医学标记之间的多模式协会
    • US20130196877A1
    • 2013-08-01
    • US13877346
    • 2011-10-04
    • Nilanjana BanerjeeAngel JanevskiSitharthan KamalakaranVinay VaradanNevenka DimitrovaRobert Lucito
    • Nilanjana BanerjeeAngel JanevskiSitharthan KamalakaranVinay VaradanNevenka DimitrovaRobert Lucito
    • G06F19/20
    • G16B25/00G16B5/00G16B20/00
    • The present invention relates to a method for identifying multi-modal associations between biomedical markers which allows for the determination of network nodes and/or high ranking network members or combinations thereof, indicative of having a diagnostic, prognostic or predictive value for a medical condition, in particular ovarian cancer. The present invention further relates to a biomedical marker or group of biomedical markers associated with a high likelihood of responsiveness of a subject to a cancer therapy, preferably a platinum based cancer therapy, wherein said bio-medical marker or group of biomedical markers comprises at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 8, 19, 20 or all markers selected from PKMYT1, SKIL, RAB8A, HIRIP3, CTNNB1, NGFR, ZCCHC11, LSP1, CD200, PAX8, CYBRD1, HOXC11, TCEAL1, FZD10,FZD1, BBS4, IRS2, TLX3, TSPAN2, TXN, and CFLAR. Furthermore, an assay for detecting, diagnosing, graduating, monitoring or prognosticating a medical condition, or for detecting, 1 diagnosing, monitoring or prognosticating the responsiveness of a subject to a therapy against said medical condition, in particular ovarian cancer, is provided, as well as a corresponding method for classifying a subject comprising and a medical decision support system.
    • 本发明涉及一种用于识别生物医学标记之间的多模式关联的方法,其允许确定网络节点和/或高排名网络成员或其组合,指示具有用于医疗状况的诊断,预后或预测值, 特别是卵巢癌。 本发明还涉及与受试者对癌症治疗(优选基于铂的癌症治疗)的高反应性的高可能性相关联的生物医学标记物或生物医学标记物组,其中所述生物医学标记物或生物医学标记物组至少包括 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,8,19,20或选自PKMYT1,SKIL,RAB8A, HIRIP3,CTNNB1,NGFR,ZCCHC11,LSP1,CD200,PAX8,CYBRD1,HOXC11,TCEAL1,FZD10,FZD1,BBS4,IRS2,TLX3,TSPAN2,TXN和CFLAR。 此外,提供了用于检测,诊断,毕业,监测或预测医学状况或用于检测1诊断,监测或预测受试者对于针对所述医学病症,特别是卵巢癌的治疗的反应性的测定法,作为 以及用于分类受试者的相应方法以及医疗决策支持系统。