SBIR Phase II: Software for Micro RNA Detection and Analysis

这个小企业创新研究（SBIR）二期项目将开发用于RNA基因检测的机器学习工具。先前的I期研究成功地开发了人工神经网络，用于在四种模式真核生物中区分功能性RNA （fRNA）编码区和非编码区。第二阶段项目将侧重于(1)改进10种能够区分fRNA编码和非编码序列信息的关键真核生物的最佳进化神经网络，(2)对人类和小鼠预测的fRNA编码区域进行实验验证，(3)开发能够区分真核fRNA亚型的机器学习算法。(4)扩展方法，包括能够区分fRNA亚型并评估其附加功能潜力的机器学习工具，以及(5)为产品开发用户友好的图形用户界面（GUI）。该项目的商业应用将是为制药工业确定一类新的药物设计和发现靶点。拟议工作的教育方面将是协助向下一代科学家传播关于frna重要性的知识。

This Small Business Innovation Research (SBIR) Phase II project will develop machine learning tools for RNA gene detection. Prior Phase I research resulted in the successful development of artificial neural networks for the discrimination of functional RNA (fRNA) coding regions from non-coding regions in four model eukaryotes. The Phase II project will focus on (1) refinement of best evolved neural networks for 10 key eukaryotes capable of discriminating fRNA coding from non-coding sequence information, (2) experimental verification of predicted fRNA coding regions in human and mouse, (3) development of machine learning algorithms capable of discriminating between eukaryotic fRNA subtypes, (4) extension of the approach to include machine learning tools capable of discriminating between fRNA subtypes and to evaluate this potential for additional functionality, and (5) development of a user-friendly graphical user interface (GUI) for the product.The commercial application of this project will be to identify a new class of targets for drug design and discovery for the pharmaceutical industry. The educational aspects of the proposed work will be to assist in dissemination of knowledge about the importance of fRNAs to the next generation of scientists.