전자의무기록과 약물/질환 네트워크 정보 기반의 신약 재창출 후보 예측 방법
    1.
    发明授权
    전자의무기록과 약물/질환 네트워크 정보 기반의 신약 재창출 후보 예측 방법 有权
    使用电子医疗记录在网络框架方法中的新型药物适应症的系统鉴定方法

    公开(公告)号:KR101450784B1

    公开(公告)日:2014-10-23

    申请号:KR1020130077255

    申请日:2013-07-02

    Inventor: 이기영 백효정

    CPC classification number: G06F19/36 G06F17/10 G06F19/326

    Abstract: The present invention relates to a method for estimating a novel drug regeneration candidate based on EMR and drug/disease network information by using a novel drug regeneration candidate estimation system including a computer-readable recording medium. The method according to an embodiment of the present invention includes the steps of: a) configuring a binary drug/disease network based on the information regarding a target disease of a specific drug; b) extracting relevant information based on EMR or clinical physiomics and genomic signatures; c) configuring a drug-drug/disease-disease similarity matrix according to the EMR or clinical physiomics; d) computing a drug-disease edge score (P_ c) based on the EMR or clinical physiomics according to the similarity matrix; e) extracting the information genomically relevant to the drugs and the diseases based on the EMR or clinical physiomics and genomic signatures; f) configuring the drug-drug/disease-disease similarity matrix according to the the genomic signatures; g) calculating another drug-disease edge score (P_g) based on the genomic signatures according to the similarity matrix; h) calculating the final estimation score of the edge f(e_ij) by using the P_c and P_g; and i) determining whether the label value of the f(e_ij) is true or false based on a cut-off value.

    Abstract translation: 本发明涉及一种通过使用包括计算机可读记录介质的新型药物再生候选估计系统,基于EMR和药物/疾病网络信息估计新型药物再生候选物的方法。 根据本发明的实施方案的方法包括以下步骤:a)基于关于特定药物的目标疾病的信息配置二元药物/疾病网络; b)根据EMR或临床物理学和基因组签名提取相关信息; c)根据EMR或临床物理学配置药物/疾病 - 疾病相似性矩阵; d)根据相似性矩阵,基于EMR或临床物理学计算药物 - 疾病边缘评分(P_c); e)基于EMR或临床物理学和基因组签名提取与药物和疾病基本相关的信息; f)根据基因组签名配置药物/疾病 - 疾病相似性矩阵; g)根据相似性矩阵,基于基因组签名计算另一种药物 - 疾病边缘评分(P_g); h)使用P_c和P_g计算边缘f(e_ij)的最终估计分数; 以及i)基于截止值来确定f(e_ij)的标签值是真还是假。

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