Archives of proteomics and bioinformatics最新文献_第2页

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

{"title":"Do Support Vector Machines Play a Role in Stratifying Patient Population Based on Cancer Biomarkers?","authors":"Ben Lanza, D. Parashar","doi":"10.1101/2020.11.02.364612","DOIUrl":"https://doi.org/10.1101/2020.11.02.364612","url":null,"abstract":"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.","PeriodicalId":87222,"journal":{"name":"Archives of proteomics and bioinformatics","volume":"1 1","pages":"20 - 38"},"PeriodicalIF":0.0,"publicationDate":"2020-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84149414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

{"title":"RANDOMIZE: A Web Server for Data Randomization.","authors":"Agaz H Wani, Don Armstrong, Jan Dahrendorff, Monica Uddin","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":87222,"journal":{"name":"Archives of proteomics and bioinformatics","volume":"1 1","pages":"31-37"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/28/df/nihms-1661644.PMC7861512.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25343302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

{"title":"Antisense inhibition of accA in E. coli suppressed luxS expression and increased antibiotic susceptibility","authors":"Tatiana Hillman","doi":"10.1101/747980","DOIUrl":"https://doi.org/10.1101/747980","url":null,"abstract":"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.","PeriodicalId":87222,"journal":{"name":"Archives of proteomics and bioinformatics","volume":"273 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78009049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1