用于假设蛋白分类的受生物启发的ChaosNet架构
Biologically inspired ChaosNet architecture for Hypothetical Protein Classification
AI总结：
该研究提出将受生物启发的混沌神经网络框架ChaosNet应用于假设蛋白功能分类，利用其混沌神经元的特性，在训练数据极少的情况下取得了可与传统标准机器学习方法相比拟的分类效果。
摘要：
ChaosNet是一种受人类大脑混沌特性启发、为分类问题开发的人工神经网络框架。该架构的每个神经元都是被称为广义吕罗级数（Generalized Luroth Series, GLS）的一维混沌映射。将GLS作为神经元加入ChaosNet，既利用了混沌的优势特性，又让计算变得简单直接。已有研究表明，ChaosNet仅需极少数据，就能在复杂分类任务上达到与传统人工神经网络（ANNs）相当甚至更优的表现。本文中，我们使用ChaosNet对假设蛋白（Hypothetical proteins, HP）进行功能分类，这确实是生物信息学中备受关注的研究主题。我们将仅用极少训练数据得到的结果，与文献中使用的标准机器学习技术进行了对比。
英文摘要：
ChaosNet is a type of artificial neural network framework developed for classification problems and is influenced by the chaotic property of the human brain. Each neuron of the ChaosNet architecture is the one-dimensional chaotic map called the Generalized Luroth Series (GLS). The addition of GLS as neurons in ChaosNet makes the computations straightforward while utilizing the advantageous elements of chaos. With substantially less data, ChaosNet has been demonstrated to do difficult classification problems on par with or better than traditional ANNs. In this paper, we use Chaosnet to perform a functional classification of Hypothetical proteins [HP], which is indeed a topic of great interest in bioinformatics. The results obtained with significantly lesser training data are compared with the standard machine learning techniques used in the literature.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
