获奖作品基本信息
| 年份 | 2018 |
| 学科 | 计算生物与生物信息学 COMPUTATIONAL BIOLOGY AND BIOINFORMATICS |
| 学校 | Broad Run High School |
| 国家/州 | VA，United States of America |
获奖作品名称
MiRNet: A Novel in silico Network-Based Approach to miRNA Drug Target Identification for Next Generation Drug Discovery
获奖作品摘要
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.
高中生科研 英特尔 Intel ISEF
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ISEF 简介
英特尔国际科学与工程大奖赛，简称 "ISEF"，由美国 Society for Science and the Public（科学和公共服务协会）主办，英特尔公司冠名赞助，是全球规模最大、等级最高的学生的科研科创赛事。ISEF 的竞赛学科包括了所有数学、自然科学、工程的全部领域和部分社会科学。ISEF 素有全球青少年科学竞赛的“世界杯”之美誉，旨在鼓励学生团队协作，开拓创新，长期专一深入地研究自己感兴趣的课题。
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学科简介：计算生物与生物信息学 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.
