Prognostic Prediction Using Clinical Expression Time Series: Towards a Supervised Learning Approach Based on Meta-biclusters

2012 ◽  
pp. 11-20
Author(s):  
André V. Carreiro ◽  
Artur J. Ferreira ◽  
Mário A. T. Figueiredo ◽  
Sara C. Madeira
Keyword(s):  
Time Series ◽  
Supervised Learning ◽  
Learning Approach ◽  
Clinical Expression ◽  
Prognostic Prediction ◽  
Expression Time

scholarly journals Prognostic Prediction through Biclustering-Based Classification of Clinical Gene Expression Time Series

2011 ◽  
Vol 8 (3) ◽  
pp. 73-89 ◽  
Author(s):  
André V. Carreiro ◽  
Orlando Anunciação ◽  
João A. Carriço ◽  
Sara C. Madeira
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Expression Patterns ◽  
Classification Problems ◽  
Temporal Perspective ◽  
Temporal Gene Expression ◽  
Gene Expression Time Series ◽  
Prognostic Prediction ◽  
Multiple Sclerosis Patients ◽  
Expression Time

Summary The constant drive towards a more personalized medicine led to an increasing interest in temporal gene expression analyzes. It is now broadly accepted that considering a temporal perspective represents a great advantage to better understand disease progression and treatment results at a molecular level. In this context, biclustering algorithms emerged as an important tool to discover local expression patterns in biomedical applications, and CCC-Biclustering arose as an efficient algorithm relying on the temporal nature of data to identify all maximal temporal patterns in gene expression time series. In this work, CCC-Biclustering was integrated in new biclustering-based classifiers for prognostic prediction. As case study we analyzed multiple gene expression time series in order to classify the response of Multiple Sclerosis patients to the standard treatment with Interferon-β, to which nearly half of the patients reveal a negative response. In this scenario, using an effective predictive model of a patient’s response would avoid useless and possibly harmful therapies for the non-responder group. The results revealed interesting potentialities to be further explored in classification problems involving other (clinical) time series.

scholarly journals Towards a Classification Approach using Meta-Biclustering: Impact of Discretization in the Analysis of Expression Time Series

2012 ◽  
Vol 9 (3) ◽  
pp. 105-120 ◽  
Author(s):  
André V. Carreiro ◽  
Artur J. Ferreira ◽  
Mário A. T. Figueiredo ◽  
Sara C. Madeira
Keyword(s):  
Time Series ◽  
Classification Accuracy ◽  
Expression Patterns ◽  
Temporal Expression ◽  
Classification Approach ◽  
Different Types ◽  
Response Profiles ◽  
Prognostic Prediction ◽  
The Impact ◽  
Expression Time

Summary Biclustering has been recognized as a remarkably effective method for discovering local temporal expression patterns and unraveling potential regulatory mechanisms, essential to understanding complex biomedical processes, such as disease progression and drug response. In this work, we propose a classification approach based on meta-biclusters (a set of similar biclusters) applied to prognostic prediction. We use real clinical expression time series to predict the response of patients with multiple sclerosis to treatment with Interferon-β. As compared to previous approaches, the main advantages of this strategy are the interpretability of the results and the reduction of data dimensionality, due to biclustering. This would allow the identification of the genes and time points which are most promising for explaining different types of response profiles, according to clinical knowledge. We assess the impact of different unsupervised and supervised discretization techniques on the classification accuracy. The experimental results show that, in many cases, the use of these discretization methods improves the classification accuracy, as compared to the use of the original features.

A Supervised Learning Approach for Dynamic Sampling (SLADS) in Raman Hyperspectral Imaging

2018 ◽  
Vol 2018 (15) ◽  
pp. 132-1-1323
Author(s):  
Shijie Zhang ◽  
Zhengtian Song ◽  
G. M. Dilshan P. Godaliyadda ◽  
Dong Hye Ye ◽  
Atanu Sengupta ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Hyperspectral Imaging ◽  
Learning Approach ◽  
Dynamic Sampling
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