-
公开(公告)号：US09619624B2
公开(公告)日：2017-04-11
申请号：US14849211
申请日：2015-09-09
CPC分类号： G06F19/345 , G06F19/00 , G06F19/18 , G06F19/326 , G06F19/3437 , G16H50/20 , G16H50/50
摘要： The present disclosure is directed to systems and methods for identifying unknown drug targets via adverse event data. An analyzer receives an identification of a first drug having one or more unknown target proteins and identifies a second drug related to the first drug. The analyzer retrieves, from an adverse event database, a first side effect profile associated with the first drug, and a second side effect profile associated with the second drug. The analyzer generates a third side effect profile comprising a subset of the first side effect profile not shared by the second side effect profile, and identifies a third drug having a fourth side effect profile comprising the third side effect profile. The analyzer retrieves a list of one or more target proteins of the third drug not targeted by the second drug, and presents the retrieved list as potential target proteins of the first drug.
-
公开(公告)号：US20150363559A1
公开(公告)日：2015-12-17
申请号：US14763015
申请日：2013-07-10
IPC分类号： G06F19/00
摘要： Systems and methods are described herein for the prioritization of possible treatment options based on biomarkers, such as (but not limited to) tumor and germline-based genomic variants. The system and methods may thereby identify patient and the status of a biomarker in the treatment options tailored to a patient, in particular to his/her clinical, molecular, and/or genetic condition. Furthermore, the system and method provides a means for prioritizing the possible treatment options based on the extraction and contextualization of clinical and molecular knowledge. The system gathers and/or accesses biomarker information and transforms the information into prioritized, clinically actionable options identified for a specific patient case.
摘要翻译： 本文描述了基于生物标志物（例如（但不限于））基于肿瘤和基于种系的基因组变体的可能的治疗选择的优先级的系统和方法。 因此，该系统和方法可以鉴定患者以及针对患者，特别是其临床，分子和/或遗传状况而定制的治疗方案中的患者和生物标志物的状态。 此外，系统和方法提供了一种基于临床和分子知识的提取和语境化来优先考虑可能的治疗选择的手段。 系统收集和/或访问生物标志物信息，并将信息转换为针对特定患者病例识别的优先的临床可行选项。
-
3.发明申请SYSTEMS AND METHODS FOR DISEASE KNOWLEDGE MODELING AND CLINICAL DECISION SUPPORT 审中-公开疾病知识建模与临床决策支持系统与方法公开(公告)号：US20150081323A1
公开(公告)日：2015-03-19
申请号：US14390019
申请日：2013-04-02
发明人： David B. Jackson , Alexander Zien , Stephan Brock , Alexander Picker , Guillaume Taglang , Bernhard Sulzer , Martin Stein
IPC分类号： G06F19/00
CPC分类号： G16H50/20 , G06F19/30 , G06F19/3481 , G16B20/00 , G16B50/00
摘要： Systems and methods are described herein for disease knowledge modeling and clinical treatment decision support. Disease or indication information, including identification of biomolecular entities associated with the indication may be culled through data mining to create a knowledge model of the indication. In some embodiments, the knowledge model may comprise a network of associations between molecular entities, including drug targets and biomarkers, genes, pathways. The model is used for prioritizing treatment decisions, for treatments comprising one or more medications associated with one or more molecular entities in the model. The priority of a suggested treatment depends on at least one property of one or more medications of the suggested treatment.
摘要翻译： 本文描述了用于疾病知识建模和临床治疗决策支持的系统和方法。 疾病或指示信息，包括与指示相关联的生物分子实体的识别可以通过数据挖掘来剔除，以创建指示的知识模型。 在一些实施方案中，知识模型可以包括分子实体之间的关联网络，包括药物靶标和生物标志物，基因，途径。 该模型用于优先考虑治疗决定，包括与模型中的一个或多个分子实体相关联的一种或多种药物的治疗。 建议治疗的优先级取决于建议治疗的一种或多种药物的至少一种性质。
-
公开(公告)号：US10672514B2
公开(公告)日：2020-06-02
申请号：US14763027
申请日：2013-10-01
发明人： Alexander Zien , David B. Jackson , Martin Stein , Guillaume Taglang , Stephan Brock , Alexander Picker , Theodoros Soldatos , Bernhard Sulzer
摘要： The present disclosure relates to systems and methods for bioinformatics and data processing. In particular, in a first aspect, the present disclosure relates to methods and systems for generating a personalized treatment guideline for a patient and for selecting a treatment for a patient. In another aspect, the present disclosure relates to methods and systems for selecting patients for a clinical trial of a treatment. The invention resolves cases in which patients have more than one “actionable” aberration by combining the patient-specific molecular information and the treatment-specific molecular information further with a clinico-molecular disease model, specifically a scoring of genes and/or proteins that represents several aspects of their involvement into the disease. In this way, treatments and patients can be prioritized that are most likely to impact or impacted by the disease mechanism, respectively.
-
5.发明申请SYSTEMS AND METHODS FOR IDENTIFYING UNKNOWN DRUG TARGETS VIA ADVERSE EVENT DATA 有权通过不良事件数据识别未知药物目标的系统和方法公开(公告)号：US20150379219A1
公开(公告)日：2015-12-31
申请号：US14849211
申请日：2015-09-09
IPC分类号： G06F19/00
CPC分类号： G06F19/345 , G06F19/00 , G06F19/18 , G06F19/326 , G06F19/3437 , G16H50/20 , G16H50/50
摘要： The present disclosure is directed to systems and methods for identifying unknown drug targets via adverse event data. An analyzer receives an identification of a first drug having one or more unknown target proteins and identifies a second drug related to the first drug. The analyzer retrieves, from an adverse event database, a first side effect profile associated with the first drug, and a second side effect profile associated with the second drug. The analyzer generates a third side effect profile comprising a subset of the first side effect profile not shared by the second side effect profile, and identifies a third drug having a fourth side effect profile comprising the third side effect profile. The analyzer retrieves a list of one or more target proteins of the third drug not targeted by the second drug, and presents the retrieved list as potential target proteins of the first drug.
摘要翻译： 本公开涉及用于通过不良事件数据识别未知药物靶标的系统和方法。 分析仪接收具有一种或多种未知目标蛋白质的第一种药物的鉴定，并识别与第一种药物相关的第二种药物。 分析仪从不良事件数据库检索与第一药物相关联的第一副作用谱，以及与第二药物相关联的第二副作用谱。 分析仪产生第三副作用轮廓，其包括不由第二副作用轮廓共享的第一副作用轮廓的子集，并且识别具有包括第三副作用轮廓的第四副作用轮廓的第三药物。 分析仪检索第二种药物未靶向的第三种药物的一种或多种靶蛋白的列表，并将检索到的列表作为第一种药物的潜在靶蛋白。
-
-
-
-
搜索结果
5 条记录 (耗时 0.036 秒)
