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MO clustering and MicroRNA Regulatory Network in Cancer

16.06.2016   13:30-15:30

Lecture of Dr. Ujjwal Maulik and Prof. Sanghamitra Bandyopadhyay

Data clustering and Multiobjective clustering

Data clustering is a popular unsupervised data mining tool that is used for partitioning a given data set into homogeneous groups based on some similarity/dissimilarity metric. Two well know clustering algorithms available in the literature are KMean and FCM. We will demonstrate how Metaheuristic techniques can be used to solve the problem of KMean/FCM. Result will be demonstrated for pixel classification of satellite images.

In the second part of the problem we will discuss Multiobjective clustering, in which multiple objective functions are simultaneously optimized. Selecting one solution from the set of Pareto Optimal solutions is always a critical issue. We will also discuss how machine learning techniques like Support Vector Machine (SVM) can be used to combine the Pareto Optimal solutions to evolve even a better solution along with the result for classification of Gene Micro Array Data.

Studying the MicroRNA Induced Regulatory Network in Colorectal and Breast Cancer

It is well-known that certain proteins, called transcription factors (TFs), regulate the expression of other genes weaving a complex regulatory network in the cell. Discovery of microRNAs, small non-coding RNAs altering gene expression at a post-transcriptional level, has added a new dimension in this context. Our studies indicate that microRNAs play a crucial role in fine-tuning the balance of myriad cellular activities.

Místo konání
KN:E-107 (Karlovo nám. 13, budova E)
Pořadatel
Katedra počítačů, FEL, ČVUT
Kontaktní osoba
Ing. Miroslav Bureš, Ph.D., buresm3@fel.cvut.cz