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3D Protein-Structure-Oriented Discovery of Clinical Relation Across Chronic Lymphocytic Leukemia Patients

机译:面向3D蛋白质结构的慢性淋巴细胞白血病患者临床关系的发现

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Chronic lymphocytic leukemia (CLL) is the most common adult leukemia with still unclear etiology. Indications of antigenic pressure have been hinted, using sequence and structure-based reasoning. The accuracy of such approaches, and in particular of the ones derived from 3D models obtained from the patients' antibody amino acid sequences, is intimately connected to both the reliability of the models and the quality of the methods used to compare and group them. The proposed work provides a sophisticated method for the classification of CLL patients based on clustering the amino acid sequences of the clonotypic B-cell receptor immunoglobulin, which is the ideal clone-specific marker, critical for clonal behavior and patient outcome. A novel CLL patient clustering method is hereby proposed, combining bioinformatics methods with the extraction of 3D object descriptors, used in machine learning applications. The proposed methodology achieved an efficient and highly informative grouping of CLL patients in accordance to their biological and clinical properties.
机译:慢性淋巴细胞性白血病(CLL)是最常见的成人白血病,其病因仍不清楚。使用基于序列和结构的推理已经暗示了抗原压力的指示。此类方法的准确性,尤其是从患者抗体氨基酸序列获得的3D模型中衍生的方法的准确性,与模型的可靠性以及用于比较和分组的方法的质量密切相关。拟议的工作为聚类克隆型B细胞受体免疫球蛋白的氨基酸序列提供了一种用于CLL患者分类的精密方法,这是理想的克隆特异性标记,对克隆行为和患者预后至关重要。因此,提出了一种新颖的CLL患者聚类方法,该方法将生物信息学方法与3D对象描述符的提取相结合,用于机器学习应用程序。所提出的方法根据其生物学和临床特性实现了对CLL患者的有效且高度有用的分组。

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