2009/03/23
Lecture Series:Knowledge Representation and Reasoning for Systems Biology by Associate Professor Andrei Doncescu
- Lecture Series:
- Knowledge Representation and Reasoning for Systems Biology
- Lecturer:
- Associate Professor Andrei Doncescu (University of Paul Sabatier, France)
- Abstract:
- Andrei Doncescu received his Ph.D from the Department of Computer Science & Electric Engineering University of Poitiers, France.
He is current Associate Professor in the University Paul Sabatier and Research Leader of Medical Diagnosis Team in Laboratory of Architecture and Analysis of Systems Toulouse France.
His research activity is oriented Knowledge Basis Discovery for Modeling of Complex Systems and more of 150 papers have been published in this research field.
He serves on the editorial boards of both IEEE and non-IEEE journals.
He received in 2005 and 2007 outstanding awards from the IEEE society and in 2006 the Best Presentation Awards of International Conference in Soft Computing and Intelligent System.He and Prof. Katsumi Inoue (NII) are the Leaders of the JST-CNRS Project Knowledge-based Discovery in Systems Biology. - Place:
- National Institute of Informatics,
- March 2 (Mon): Presentation Room, 19F
March 10 (Tue): Meeting Room, 20F
March 23, 30 April 6 (Mon): Lecture Room, 20F - Date:
- March 2, 10, 23, 30, April 6
2pm - 4pm
- Lecture 1
- Fuzzy Logic for Engineering : A Tutorial
- 2nd March 2009: Presentation Room, 19F
- A fuzzy logic system (FLS) is unique in that it is able to simultaneously handle numerical data and linguistic knowledge. This tutorial provides a guided tour through, those aspects of fuzzy sets and fuzzy logic that are necessary to synthesis a fuzzy logic systems. Because the Biological Systems are for the most part causal, the causality is impose as a constraint of the development of FLS. In the end of this tutorial we show the manner to update a FLS by incorporate knowledge in a unified mathematical manner.
- Lecture 2
- 1. Plausible Reasoning
2.1.1. Classical Probability Theory
2.1.2. Statistical Inference
2:2. Quantities Measurements: time, space, minds - 10th March 2009: Meeting Room, 20F
- The first part of the lecture reminds the basic notions of probability theory. After this short introduction we give an overview of the utility of thermodynamics at the molecular level to understand proteins and receptor-ligand binding.
- Lecture 3
- Knowledge Basis Discovery by Data Mining Techniques
3.1. From Clustering to Regression
3.2. Application on Systems Biology - 23th March 2009: Lecture Room, 20F
- Computational models have been playing a significant role for the computer-based analysis of biological and biomedical data. High-throughput technologies are opening global perspectives for analyzing living organism at the molecular level. In the first part of this lecture a variety of artificial intelligence technologies and statistical tools are presented to detect significant differences in gene expression levels. Using this type of approach we can infer regulatory interactions directly from data by fitting simple network models to large scale gene expression data and to extract the most well-determined interactions in the network.
- Lecture 4
- The Analytical Modelling in Medicine
- 30th March 2009: Lecture Room, 20F
- Taking into account the current competences in the field of the classical automatic control, namely, analysis, observation and control of dynamical systems, linear, nonlinear with or without delay and/or propagation, the objective is to propose a"progressive transfer" of our "competences" towards the domains of the life sciences, and especially towards all the domains of life sciences in which a dynamic behavior can be pointed out. Every behavior of a biological system with respect to one (or several) time scaling can be interpreted in a dynamical system context by using the associated tools.
- Lecture 5
- Logic, Knowledge and Computation for Systems Biology
- 6th April 2009: Lecture Room, 20F
- System biology provides a new approach to studying and analyzing the biological process. Biological Pathways represent a key sub-system level of organization. The aim of the biological pathway is to map and understand the cause-effect relationship in the complex interactions of biological systems. Complex diseases such as cancer have multiple origins and are therefore difficult to understand and cure. We present the use of logic relationship to model breast cancer gene expression networks with mRNA microarray data as a challenge. Some of these challenges are discussed.
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