2017英特尔ISEF获奖作品摘要 计算生物与生物信息学 Intel Finalist Abstract Computational Biology and Bioinformatics

获奖作品基本信息

年份	2017
学科	计算生物与生物信息学 Computational Biology and Bioinformatics
国家/州	United States of America

获奖作品名称

Overcoming Lung Cancer with Novel Computationally Boosted Antibody Biosensor

获奖作品摘要

Non-Small Cell Lung Cancer (NSCLC) is the most prominent form of Lung Cancer and kills at least 800,000 people each year. Early diagnosis of NSCLC yields higher survival rates among patients, in comparison to effective therapies alone. However, the research and development of such devices is long and expensive. In this project a computational approach consisting of two algorithms were created to create a boosted biosensor to screen for NSCLC. The first algorithm is a python-coded algorithm which finds the highest expressed protein bio-marker in tissue and serum. The bio-marker identified by the first algorithm was the survivin protein with 70% expression in tissue and 80% expression in serum. The second algorithm is a deep learning algorithm which boosted sensor sensitivity by classifying certain antibodies as super binders with respect to the survivin protein. The EP2880Y antibody was identified as a super-binder to the survivin protein. Using the predictions from the sequence of algorithms, a low powered chemiresistive carbon nano-tube biosensor was created with the EP2880Y antibody to target the survivin protein. The sensor detects the presence of survivin by measuring an increase in resistance. Using this information the experimenter created an excel spread sheet that analyzes the resistance values to give an output of increased risk or decreased risk of NSCLC.  This sensor can be implemented in areas without proper medical facility and the algorithms can be used to replace long and expensive lab tests.

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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、等级最高的中学生的科研科创赛事。ISEF 的学术活动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学术活动的“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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英特尔 ISEF 学术活动详细介绍

英特尔 ISEF 全程指导方案

· 数学 · 物理 · 化学 · 生物 · 计算机 · 工程 ·

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.