Inference-based Hypothesis-finding and its Application to Systems Biology

基于推理的假设发现及其在系统生物学中的应用

• 批准号: 20240016
• 负责人: INOUE Katsumi
• 金额: $ 21.8万
• 依托单位: National Institute of Informatics
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (A)
• 财政年份: 2008
• 资助国家: 日本
• 起止时间: 2008 至 2011
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-20240016/
• 关键词: 人工知能; 推論; システム生物学; 仮説発見; 結論発見; 確率推論; 帰納論理プログラミング; アブダクション

Systems biology is an emergent field that aims to understand living organisms as biological systems. To find useful information from biological data, we focus on inference methods, induction and abduction, based on logic-based Artificial Intelligence. Both abduction and induction are used to infer hypotheses in inductive logic programming(ILP), and can be implemented using the consequence finding procedure SOLAR. However, in the current inference methods, efficiency of inference needs to be improved, and mechanisms for hypotheses selection are necessarily developed. Hence, the following three subtasks are set in this project:(1) Improving hypothesis finding methods based on SOLAR and related techniques;(2) Developing effective hypothesis selection methods based on both statistical and non-statistical approaches;(3) Applying those methods to problems in systems biology.

系统生物学是一个新兴的领域，旨在将生物体理解为生物系统。为了从生物数据中发现有用的信息，我们重点讨论了基于逻辑的人工智能的推理方法，归纳和溯因。在归纳逻辑程序设计（ILP）中，溯因和归纳都被用来推断假设，并且可以使用结果发现过程SOLAR来实现。然而，在现有的推理方法中，推理的效率需要提高，并且有必要开发假设选择机制。因此，本项目的主要任务是：（1）改进基于SOLAR及其相关技术的假设发现方法;（2）开发基于统计和非统计方法的有效假设选择方法;（3）将这些方法应用于系统生物学问题。

项目成果

Logic-Based Probabilistic Modeling

• DOI: 10.1007/978-3-642-02261-6_6
• 发表时间: 2009-06
• 作者: Taisuke Sato

Constraint-based probabilistic modeling for statistical abduction

• DOI: 10.1007/s10994-010-5206-7
• 发表时间: 2011-05
• 期刊: Machine Learning
• 影响因子: 7.5
• 作者: Taisuke Sato;Masakazu Ishihata;Katsumi Inoue

A glimpse of symbolic-statistical modeling by PRISM

• DOI: 10.1007/s10844-008-0062-7
• 发表时间: 2008-10
• 期刊: Journal of Intelligent Information Systems
• 影响因子: 3.4
• 作者: Taisuke Sato

Probabilistic Rule Learning in Nonmonotonic Domains

非单调域中的概率规则学习

• DOI: 10.1007/978-3-642-22359-4_17
• 发表时间: 2011
• 期刊: Proc.of 12th Int'l Workshop on the Computational Logic in Multi-Agent Systems (CLIMA-XII)
• 作者: Domenico Corapi;Daniel Sykes;Katsumi Inoue;Alessandra Russo
• 通讯作者: Alessandra Russo

Time Series Discretization via MDL-Based Histogram Density Estimation

通过基于 MDL 的直方图密度估计进行时间序列离散化

• 发表时间: 2011
• 作者: Chu;Annie;Yoshitaka Kameya
• 通讯作者: Yoshitaka Kameya