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Big Data in Systems Medicine and Sys...

Zhao, Shan.

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Big Data in Systems Medicine and Systems Pharmacology: From Correlations to Clinical Predictions.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Big Data in Systems Medicine and Systems Pharmacology: From Correlations to Clinical Predictions./
作者:	Zhao, Shan.
面頁冊數:	259 p.
附註:	Source: Dissertation Abstracts International, Volume: 75-04(E), Section: B.
Contained By:	Dissertation Abstracts International75-04B(E).
標題:	Health Sciences, Pharmacology. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3605543
ISBN:	9781303620430

Big Data in Systems Medicine and Systems Pharmacology: From Correlations to Clinical Predictions.
Zhao, Shan.

Big Data in Systems Medicine and Systems Pharmacology: From Correlations to Clinical Predictions. - 259 p.

Source: Dissertation Abstracts International, Volume: 75-04(E), Section: B.

Thesis (Ph.D.)--Icahn School of Medicine at Mount Sinai, 2013.

This item must not be sold to any third party vendors.

It was exactly three score years ago that James Watson and Francis Crick published the seminal work on the structure of DNA in Nature. This discovery has led to increased understanding in all of biology including pharmacology and cancer. Another seminal scientific advancement occurred in 1953, a device creation by Tom Kilburn that would eventually lead to the creation of the first transistorized computer. It was perhaps not realized then how such distant discoveries can become intertwined and synergistic. In this study we will examine the use of computerized algorithms and their application for polypharmacology and precision medicine where we identified combinations of currently available drugs for the alleviation of adverse events and the usage of molecular correlations detected through high throughput data and their clinical decision making tools.

ISBN: 9781303620430Subjects--Topical Terms:

1017717
Health Sciences, Pharmacology.

Big Data in Systems Medicine and Systems Pharmacology: From Correlations to Clinical Predictions.
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245 1 0 $a Big Data in Systems Medicine and Systems Pharmacology: From Correlations to Clinical Predictions.
300 $a 259 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-04(E), Section: B.
500 $a Includes supplementary digital materials.
500 $a Adviser: Ravi Iyengar.
502 $a Thesis (Ph.D.)--Icahn School of Medicine at Mount Sinai, 2013.
506 $a This item must not be sold to any third party vendors.
506 $a This item must not be added to any third party search indexes.
520 $a It was exactly three score years ago that James Watson and Francis Crick published the seminal work on the structure of DNA in Nature. This discovery has led to increased understanding in all of biology including pharmacology and cancer. Another seminal scientific advancement occurred in 1953, a device creation by Tom Kilburn that would eventually lead to the creation of the first transistorized computer. It was perhaps not realized then how such distant discoveries can become intertwined and synergistic. In this study we will examine the use of computerized algorithms and their application for polypharmacology and precision medicine where we identified combinations of currently available drugs for the alleviation of adverse events and the usage of molecular correlations detected through high throughput data and their clinical decision making tools.
520 $a The utilization of modern computers stemming from Kilburn's work has enabled the acquisition, storage, and processing large amount of data including those from individuals. This study focuses on how such large volume of clinical data from spontaneous reporting of drug utilization and adverse events in the FDA adverse event reporting system (FAERS) can be used to discover new beneficial combinations for adverse event mitigation from currently available therapies. This realization pushes the need to carefully reconsider the discovery process utilized by modern pharmacology. It suggests the potential importance of novel combinations of currently FDA approved drugs in altering the benefit/risk ratios for each other.
520 $a At the same time, Killburn's work has another use. The creation and operation of high throughput molecular assaying platforms like microarray and parallel DNA sequencing have enabled detailed molecular studies of cancers through large scale projects such as The Cancer Genome Atlas (TCGA) consortium. TCGA is a large compendium of multiple types of molecular information largely at the genomic level to provide comprehensive molecular portraits for more than a dozen cancers. In this study, we focus on four cancers within the TCGA and using a simple array of statistical tests done on a massive scale for billions of correlations to derive new understanding of functional similarities and difference between cancers and individuals within cancers, which lead to clinically relevant predictions that can determine an individual patient's prognosis and therapeutic response.
520 $a Since the publication of the structure of DNA, and understanding the genetic code, the conversion of DNA to RNA and RNA to protein there has been enormous progress in cell biology and medicine. Many signaling pathways have been deduced, cloning and transgenic methods mastered, and the human genome sequenced. The realm of biological understanding has expanded beyond probably anything the authors of the seminal studies 60 years ago would have imagined. At the end of the thesis, we provide a discussion and perspective of the future that is to come as synergies between biology and computing become more intertwined.
590 $a School code: 1734.
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650 4 $a Biology, Bioinformatics. $3 1018415
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710 2 $a Icahn School of Medicine at Mount Sinai. $b Pharmacology and System Biology. $3 3171738
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