Discovering monotonic stemness marker genes from time-series stem cell microarray data

Discovering Ecosystem Models from Time-Series Data

Effect of cell culture using chitosan membranes on stemness marker genes in mesenchymal stem cells.

Mesenchymal stem cell (MSC) therapy is a promising treatment for diseases of the nervous system. However, MSCs often lose their stemness and homing abilities when cultured in conventional two‑dimensional (2D) systems. Consequently, it is important to explore novel culture methods for MSC-based therapies in clinical practice. To investigate the effect of a cell culture using chitosan membranes o...

Discovering Clusters in Motion Time-Series Data

A new approach is proposed for clustering time-series data. The approach can be used to discover groupings of similar object motions that were observed in a video collection. A finite mixture of hidden Markov models (HMMs) is fitted to the motion data using the expectation-maximization (EM) framework. Previous approaches for HMM-based clustering employ a k-means formulation, where each sequence...

Detecting Gene Regulation Relations from Microarray Time Series Data

Microarrays are important tools in the quest to map the gene regulation networks of cells. A common use of microarrays result in time series pairs that indicates how the output of one gene affects another. Substantial efforts have been made towards identifying pairs of microarray time series that indicate that one gene is a regulator for another. However, most approaches make assumptions about ...

Diagnosis of Breast Cancer Subtypes using the Selection of Effective Genes from Microarray Data

Introduction: Early diagnosis of breast cancer and the identification of effective genes are important issues in the treatment and survival of the patients. Gene expression data obtained using DNA microarray in combination with machine learning algorithms can provide new and intelligent methods for diagnosis of breast cancer. Methods: Data on the expression of 9216 genes from 84 patients across...