CLUSTERING SAMPLES CHARACTERIZED BY TIME COURSE GENE EXPRESSION PROFILES USING THE MIXTURE OF STATE SPACE MODELS

Author(s):  
OSAMU HIROSE ◽  
RYO YOSHIDA ◽  
RUI YAMAGUCHI ◽  
SEIYA IMOTO ◽  
TOMOYUKI HIGUCHI ◽  
...  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Arika Fukushima ◽  
Masahiro Sugimoto ◽  
Satoru Hiwa ◽  
Tomoyuki Hiroyasu

Abstract Background Historical and updated information provided by time-course data collected during an entire treatment period proves to be more useful than information provided by single-point data. Accurate predictions made using time-course data on multiple biomarkers that indicate a patient’s response to therapy contribute positively to the decision-making process associated with designing effective treatment programs for various diseases. Therefore, the development of prediction methods incorporating time-course data on multiple markers is necessary. Results We proposed new methods that may be used for prediction and gene selection via time-course gene expression profiles. Our prediction method consolidated multiple probabilities calculated using gene expression profiles collected over a series of time points to predict therapy response. Using two data sets collected from patients with hepatitis C virus (HCV) infection and multiple sclerosis (MS), we performed numerical experiments that predicted response to therapy and evaluated their accuracies. Our methods were more accurate than conventional methods and successfully selected genes, the functions of which were associated with the pathology of HCV infection and MS. Conclusions The proposed method accurately predicted response to therapy using data at multiple time points. It showed higher accuracies at early time points compared to those of conventional methods. Furthermore, this method successfully selected genes that were directly associated with diseases.


PLoS ONE ◽  
2009 ◽  
Vol 4 (12) ◽  
pp. e8126 ◽  
Author(s):  
Tao Huang ◽  
WeiRen Cui ◽  
LeLe Hu ◽  
KaiYan Feng ◽  
Yi-Xue Li ◽  
...  

Blood ◽  
2004 ◽  
Vol 104 (10) ◽  
pp. 3126-3135 ◽  
Author(s):  
Elena Tenedini ◽  
Maria Elena Fagioli ◽  
Nicola Vianelli ◽  
Pier Luigi Tazzari ◽  
Francesca Ricci ◽  
...  

Abstract Gene expression profiles of bone marrow (BM) CD34-derived megakaryocytic cells (MKs) were compared in patients with essential thrombocythemia (ET) and healthy subjects using oligonucleotide microarray analysis to identify differentially expressed genes and disease-specific transcripts. We found that proapoptotic genes such as BAX, BNIP3, and BNIP3L were down-regulated in ET MKs together with genes that are components of the mitochondrial permeability transition pore complex, a system with a pivotal role in apoptosis. Conversely, antiapoptotic genes such as IGF1-R and CFLAR were up-regulated in the malignant cells, as was the SDF1 gene, which favors cell survival. On the basis of the array results, we characterized apoptosis of normal and ET MKs by time-course evaluation of annexin-V and sub-G1 peak DNA stainings of immature and mature MKs after culture in serum-free medium with an optimal thrombopoietin concentration, and annexin-V–positive MKs only, with decreasing thrombopoietin concentrations. ET MKs were more resistant to apoptosis than their normal counterparts. We conclude that imbalance between proliferation and apoptosis seems to be an important step in malignant ET megakaryocytopoiesis.


2009 ◽  
Vol 38 (1) ◽  
pp. 143-158 ◽  
Author(s):  
Huanying Ge ◽  
Min Wei ◽  
Paola Fabrizio ◽  
Jia Hu ◽  
Chao Cheng ◽  
...  

2013 ◽  
Vol 58 (5) ◽  
pp. 511-522 ◽  
Author(s):  
Yu Li ◽  
Meile Li ◽  
Lijun Tan ◽  
Shengbin Huang ◽  
Lixing Zhao ◽  
...  

2015 ◽  
Vol 103 ◽  
pp. 77-84 ◽  
Author(s):  
Yanzhu Lin ◽  
Kim Lehmann ◽  
Philip Z. Brohawn ◽  
Zheng Liu ◽  
Nitin Agarwal

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