The application of knowledge growing system for inferring the behavior of genes interaction

A. D. W. Sumari, A. S. Ahmad, A. I. Wuryandari, Jaka Sembiring
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引用次数: 3

Abstract

Knowledge Growing System (KGS) is a novel perspective in Artificial Intelligence (AI) which is aimed to emulate how the human brain obtains new knowledge from information delivered by human sensory organs. The new knowledge is then used as the basis for making an estimation in the future of the phenomenon being observed as the basis for the most appropriate decision or action that will be decided or taken. In this paper we address the application of KGS to infer the behavior of genes interaction in Genetic Regulatory System (GRS) in order to estimate their behavior in the subsequent interaction time. For this purpose we model the genes as multi-agent that performs collaborative computations in Multiagent Collaborative Computation (MCC) paradigm. In order to show how KGS works in MCC framework, we use yeast genes-interaction values as the case study.
知识增长系统在基因相互作用行为推断中的应用
知识增长系统(Knowledge growth System, KGS)是人工智能领域的一个新视角,旨在模拟人类大脑如何从人类感觉器官传递的信息中获取新知识。然后,新知识被用作对未来观察到的现象进行估计的基础,作为将决定或采取的最适当决策或行动的基础。本文讨论了利用KGS来推断遗传调控系统(GRS)中基因相互作用的行为,以估计它们在后续相互作用时间中的行为。为此,我们在多智能体协同计算(MCC)范式中将基因建模为执行协同计算的多智能体。为了展示KGS在MCC框架下的工作原理,我们使用酵母基因相互作用值作为案例研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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