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Do Support Vector Machines Play a Role in Stratifying Patient Population Based on Cancer Biomarkers? 支持向量机在基于癌症生物标志物的患者群体分层中发挥作用吗?

Archives of proteomics and bioinformatics

Pub Date : 2020-11-02 DOI: 10.1101/2020.11.02.364612

Ben Lanza, D. Parashar

Biomarkers are known to be the key driver behind targeted cancer therapies by either stratifying the patients into risk categories or identifying patient subgroups most likely to benefit. However, the ability of a biomarker to stratify patients relies heavily on the type of clinical endpoint data being collected. Of particular interest is the scenario when the biomarker involved is a continuous one where the challenge is often to identify cut-offs or thresholds that would stratify the population according to the level of clinical outcome or treatment benefit. On the other hand, there are well-established Machine Learning (ML) methods such as the Support Vector Machines (SVM) that classify data, both linear as well as non-linear, into subgroups in an optimal way. SVMs have proven to be immensely useful in data-centric engineering and recently researchers have also sought its applications in healthcare. Despite their wide applicability, SVMs are not yet in the mainstream of toolkits to be utilised in observational clinical studies or in clinical trials. This research investigates the very role of SVMs in stratifying the patient population based on a continuous biomarker across a variety of datasets. Based on the mathematical framework underlying SVMs, we formulate and fit algorithms in the context of biomarker stratified cancer datasets to evaluate their merits. The analysis reveals their superior performance for certain data-types when compared to other ML methods suggesting that SVMs may have the potential to provide a robust yet simplistic solution to stratify real cancer patients based on continuous biomarkers, and hence accelerate the identification of subgroups for improved clinical outcomes or guide targeted cancer therapies.

已知生物标志物是靶向癌症治疗背后的关键驱动因素，可以将患者分为风险类别或确定最有可能受益的患者亚组。然而，生物标志物对患者进行分层的能力在很大程度上依赖于所收集的临床终点数据的类型。特别令人感兴趣的是，当所涉及的生物标志物是连续的，其中的挑战往往是确定截断或阈值，根据临床结果或治疗益处的水平对人群进行分层。另一方面，有完善的机器学习(ML)方法，如支持向量机(SVM)，它以最佳方式将线性和非线性数据分类到子组中。事实证明，svm在以数据为中心的工程中非常有用，最近研究人员也在医疗保健领域寻求其应用。尽管支持向量机具有广泛的适用性，但它尚未成为用于观察性临床研究或临床试验的主流工具包。本研究探讨了支持向量机在基于各种数据集的连续生物标志物对患者群体进行分层中的作用。基于支持向量机的数学框架，我们在生物标志物分层癌症数据集的背景下制定和拟合算法，以评估其优点。分析显示，与其他ML方法相比，支持向量机在某些数据类型上具有优越的性能，这表明支持向量机可能有潜力提供一种强大而简单的解决方案，根据连续的生物标志物对真实的癌症患者进行分层，从而加速识别亚组，以改善临床结果或指导靶向癌症治疗。

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引用次数: 1

RANDOMIZE: A Web Server for Data Randomization 一个用于数据随机化的Web服务器

Archives of proteomics and bioinformatics

Pub Date : 2020-04-02 DOI: 10.1101/2020.04.02.013656

A. Wani, Don Armstrong, J. Dahrendorff, M. Uddin

Summary DNA methylation microarray data may suffer from batch effects due to improper handling of the samples during the plating process. RANDOMIZE is a web-based application designed to perform randomization of relevant metadata to evenly distribute samples across the factors typically responsible for batch effects in DNA methylation microarrays, such as row, chips and plates. Randomization helps to reduce the likelihood of bias and impact of difference among groups. Availability The tool is freely available online at https://coph-usf.shinyapps.io/RANDOMIZE/ and can be accessed using any web browser. Sample data and tutorial is also available with the tool. Contact ahwani@usf.edu

DNA甲基化微阵列数据可能由于在电镀过程中样品处理不当而遭受批处理效应。RANDOMIZE是一个基于web的应用程序，旨在执行相关元数据的随机化，以均匀地分布样本在DNA甲基化微阵列中通常负责批处理效应的因素，如行，芯片和板。随机化有助于减少偏倚的可能性和组间差异的影响。可用性该工具可免费在线访问https://coph-usf.shinyapps.io/RANDOMIZE/，并可使用任何web浏览器访问。该工具还提供了示例数据和教程。联系ahwani@usf.edu

引用次数: 1

RANDOMIZE: A Web Server for Data Randomization. RANDOMIZE：数据随机化网络服务器。

Archives of proteomics and bioinformatics

Pub Date : 2020-01-01

Agaz H Wani, Don Armstrong, Jan Dahrendorff, Monica Uddin

The microarray-based Illumina Infinium MethylationEpic BeadChip (Epic 850k) has become a useful and standard tool for epigenome wide deoxyribonucleic acid (DNA) methylation profiling. Data from this technology may suffer from batch effects due to improper handling of the samples during the plating process. Batch effects are a significant issue and can give rise to spurious and inaccurate results and reduction in power to detect real biological differences. Careful study design, such as randomizing the samples to uniformly distribute the samples across the factors responsible for batch effects, is crucial to address batch effects and other technical artifacts. Randomization helps to reduce the likelihood of bias and impact of difference among groups. This process of randomizing the samples can be a tedious, error-prone, and time-consuming task without a user-friendly and efficient tool. We present RANDOMIZE, a web-based application designed to perform randomization of relevant metadata to evenly distribute samples across the factors typically responsible for batch effects in DNA methylation microarrays, such as rows, chips and plates. We demonstrate that the tool is efficient, fast and easy to use. The tool is freely available online at https://coph-usf.shinyapps.io/RANDOMIZE/ and can be accessed using any web browser. Sample data and tutorial is also available with the tool.

基于芯片的 Illumina Infinium MethylationEpic BeadChip（Epic 850k）已成为表观基因组宽脱氧核糖核酸（DNA）甲基化分析的有用标准工具。由于在电镀过程中对样本的处理不当，该技术的数据可能会受到批次效应的影响。批次效应是一个重要问题，可能导致虚假和不准确的结果，并降低检测真实生物差异的能力。要解决批次效应和其他技术假象，谨慎的研究设计至关重要，例如随机化样本，使样本均匀分布在造成批次效应的各种因素上。随机化有助于减少偏差的可能性和组间差异的影响。如果没有一个用户友好且高效的工具，随机化样本的过程可能是一项繁琐、容易出错且耗时的任务。我们介绍的 RANDOMIZE 是一款基于网络的应用程序，旨在对相关元数据进行随机化处理，以便在 DNA 甲基化微阵列中造成批次效应的典型因素（如行、芯片和平板）之间均匀分布样本。我们展示了该工具的高效、快速和易用性。该工具可在 https://coph-usf.shinyapps.io/RANDOMIZE/ 免费在线获取，可使用任何网络浏览器访问。该工具还提供样本数据和教程。

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引用次数: 0

Antisense inhibition of accA in E. coli suppressed luxS expression and increased antibiotic susceptibility 在大肠杆菌中反义抑制accA抑制luxS表达，增加抗生素敏感性

Archives of proteomics and bioinformatics

Pub Date : 2019-08-28 DOI: 10.1101/747980

Tatiana Hillman

Bacterial multiple drug resistance is a significant issue for the medical community. Gram-negative bacteria exhibit higher rates of multi-drug resistance, partly due to the impermeability of the Gram-negative bacterial cell wall and double-membrane cell envelope, which limits the internal accumulation of antibiotic agents. The outer lipopolysaccharide membrane regulates the transport of hydrophobic molecules, while the inner phospholipid membrane controls influx of hydrophilic particles. In Escherichia coli, the gene accA produces the acetyl-CoA carboxylase transferase enzyme required for catalyzing synthesis of fatty acids and phospholipids that compose the inner membrane. To increase antibiotic susceptibility and decrease growth, this study interrupted fatty acid synthesis and disrupted the composition of the inner membrane through inhibiting the gene accA with antisense RNA. This inhibition suppressed expression of luxS, a vital virulence factor that regulates cell growth, transfers intercellular quorum-sensing signals mediated by autoinducer-2, and is necessary for biofilm formation. Bacterial cells in which accA was inhibited also displayed a greater magnitude of antibiotic susceptibility. These findings confirm accA as a potent target for developing novel antibiotics such as antimicrobial gene therapies.

细菌多重耐药是医学界关注的重要问题。革兰氏阴性菌表现出较高的耐多药率，部分原因是革兰氏阴性菌细胞壁和双膜细胞包膜的不渗透性，这限制了抗生素药物的内部积累。外脂多糖膜调节疏水分子的运输，内磷脂膜控制亲水颗粒的流入。在大肠杆菌中，accA基因产生乙酰辅酶a羧化酶转移酶，催化合成构成内膜的脂肪酸和磷脂所需的酶。为了增加抗生素敏感性和降低生长，本研究通过用反义RNA抑制accA基因来中断脂肪酸的合成并破坏内膜的组成。这种抑制抑制了luxS的表达，luxS是一种重要的毒力因子，调节细胞生长，传递由自诱导剂-2介导的细胞间群体感应信号，是生物膜形成所必需的。accA被抑制的细菌细胞也表现出更大程度的抗生素敏感性。这些发现证实了accA是开发新型抗生素(如抗菌基因疗法)的有效靶点。

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引用次数: 1

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