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How bioinformatics influences health informatics: usage of biomolecular sequences expression profiles and automated microscopic image analyses for clinical needs and public health

机译:生物信息学如何影响健康信息学:利用生物分子序列表达谱和自动显微图像分析来满足临床需求和公共卫生

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摘要

AbstractThe currently hyped expectation of personalized medicine is often associated with just achieving the information technology led integration of biomolecular sequencing, expression and histopathological bioimaging data with clinical records at the individual patients’ level as if the significant biomedical conclusions would be its more or less mandatory result. It remains a sad fact that many, if not most biomolecular mechanisms that translate the human genomic information into phenotypes are not known and, thus, most of the molecular and cellular data cannot be interpreted in terms of biomedically relevant conclusions. Whereas the historical trend will certainly be into the general direction of personalized diagnostics and cures, the temperate view suggests that biomedical applications that rely either on the comparison of biomolecular sequences and/or on the already known biomolecular mechanisms have much greater chances to enter clinical practice soon. In addition to considering the general trends, we exemplarily review advances in the area of cancer biomarker discovery, in the clinically relevant characterization of patient-specific viral and bacterial pathogens (with emphasis on drug selection for influenza and enterohemorrhagic E. coli) as well as progress in the automated assessment of histopathological images. As molecular and cellular data analysis will become instrumental for achieving desirable clinical outcomes, the role of bioinformatics and computational biology approaches will dramatically grow.
机译:摘要当前对个性化医学的期望常常与仅实现信息技术导致的将生物分子测序,表达和组织病理学生物影像学数据与个别患者水平的临床记录相集成有关,好像重要的生物医学结论将是其或多或少的强制性结果。令人遗憾的事实是,将人类基因组信息转化为表型的许多(如果不是大多数)生物分子机制尚不清楚,因此,大多数分子和细胞数据无法根据生物医学相关结论进行解释。历史趋势肯定会朝着个性化诊断和治疗的总体方向发展,但温和的观点表明,依靠生物分子序列比较和/或已知生物分子机制的生物医学应用有更大的机会进入临床实践不久。除了考虑总体趋势外,我们还示例性地回顾了癌症生物标志物发现领域的进展,患者特异性病毒和细菌病原体的临床相关特征(重点是针对流感和大肠埃希氏大肠杆菌的药物选择)以及组织病理学图像自动评估方面的进展。随着分子和细胞数据分析将成为实现理想的临床结果的工具,生物信息学和计算生物学方法的作用将急剧增长。

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