2018英特爾ISEF學科最佳作品摘要：計算生物與生物信息學 | Best of Category Abstract: COMPUTATIONAL BIOLOGY AND BIOINFORMATICS

獲獎作品基本信息

年份	2018
學科	計算生物與生物信息學 COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
學校	Broad Run High School
國家/州	VA，United States of America

獲獎作品名稱

MiRNet: A Novel in silico Network-Based Approach to miRNA Drug Target Identification for Next Generation Drug Discovery

獲獎作品摘要

Chronic disease is the leading cause of death in the world. Next generation therapeutics are needed to combat the growing global health crisis of chronic diseases, such as cancers and heart disease. MiRNAs are key biomolecules that powerfully regulate disease pathways, making them promising therapeutic candidates.
The current miRNA drug discovery path—in vitro experiments—is time consuming, expensive, and yields hundreds of miRNAs, many of which are disease non-specific. This study created MiRNet, a network-based model to pinpoint disease specific miRNAs. Integrating massive transcriptomic and genomic datasets, MiRNet is a comprehensive miRNA-disease bipartite network model. A novel network diffusion algorithm utilizing Markov random walks and an unsupervised learning framework for community detection were developed to prioritize miRNA drug targets.
MiRNet successfully pinpointed 1-2 key miRNAs per disease. Examples include miR-155 for leukemia and miR-567 for ovarian cancer. Of the total 190 miRNAs identified, 47 were for cancers, 46 for cardiovascular diseases, 69 for endocrine diseases, and 28 for psychiatric diseases. MiRNet’s miRNA predictions were evaluated on 3 select diseases where differential miRNA expression data was available: breast cancer (97.6% accuracy, 4.6% false positive rate), leukemia (97.3% Acc, 7.6% FPR), lymphoma (97.1% Acc, 7.3% FPR).

The first of its kind in the published literature, MiRNet addresses the greatest barrier to widespread use of miRNA therapeutics: drug target identification. MiRNet provides a paradigm shift towards quicker, cheaper miRNA target discovery and clinical translation.

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ISEF 簡介

英特爾國際科學與工程大獎賽，簡稱 "ISEF"，由美國 Society for Science and the Public（科學和公共服務協會）主辦，英特爾公司冠名贊助，是全球規模最大、等級最高的中學生的科研科創賽事。ISEF 的學術活動學科包括了所有數學、自然科學、工程的全部領域和部分社會科學。ISEF 素有全球青少年科學學術活動的“世界杯”之美譽，旨在鼓勵學生團隊協作，開拓創新，長期專一深入地研究自己感興趣的課題。

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英特爾 ISEF 學術活動詳細介紹

英特爾 ISEF 全程指導方案

· 數學 · 物理 · 化學 · 生物 · 計算機 · 工程 ·

學科簡介：計算生物與生物信息學 COMPUTATIONAL BIOLOGY AND BIOINFORMATICS

Studies that primarily focus on the discipline and techniques of computer science and mathematics as they relate to biological systems. This includes the development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological, behavior, and social systems.

Subcategories:

Computational Biomodeling?(MOD):?Studies that involve computer simulations of biological systems most commonly with a goal of understanding how cells or organism develop, work collectively and survive.

Computational Epidemiology (EPD):?The study of disease frequency and distribution, and risk factors and socioeconomic determinants of health within populations. Such studies may include gathering information to confirm existence of disease outbreaks, developing case definitions and analyzing epidemic data, establishing disease surveillance, and implementing methods of disease prevention and control.

Computational Evolutionary Biology?(EVO):?A study that applies the discipline and techniques of computer science and mathematics to explore the processes of change in populations of organisms, especially taxonomy, paleontology, ethology, population genetics and ecology.

Computational Neuroscience?(NEU):?A study that applies the discipline and techniques of computer science and mathematics to understand brain function in terms of the information processing properties of the structures that make up the nervous system.

Computational Pharmacology?(PHA):?A study that applies the discipline and techniques of computer science and mathematics to predict and analyze the responses to drugs.

Genomics?(GEN):?The study of the function and structure of genomes using recombinant DNA, sequencing, and bioinformatics.

Other?(OTH):?Studies that cannot be assigned to one of the above subcategories. If the project involves multiple subcategories, the principal subcategory should be chosen instead of Other.