Time series gene expression data classification via L1-norm temporal SVM
PRIB'10 Proceedings of the 5th IAPR international conference on Pattern recognition in bioinformatics
CIBB'10 Proceedings of the 7th international conference on Computational intelligence methods for bioinformatics and biostatistics
Gene selection in time-series gene expression data
PRIB'11 Proceedings of the 6th IAPR international conference on Pattern recognition in bioinformatics
Hybrid method for the analysis of time series gene expression data
Knowledge-Based Systems
ACM Computing Surveys (CSUR)
Learning relevant time points for time-series data in the life sciences
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Classification approach based on non-negative least squares
Neurocomputing
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Motivation: Classification of tissues using static gene-expression data has received considerable attention. Recently, a growing number of expression datasets are measured as a time series. Methods that are specifically designed for this temporal data can both utilize its unique features (temporal evolution of profiles) and address its unique challenges (different response rates of patients in the same class). Results: We present a method that utilizes hidden Markov models (HMMs) for the classification task. We use HMMs with less states than time points leading to an alignment of the different patient response rates. To focus on the differences between the two classes we develop a discriminative HMM classifier. Unlike the traditional generative HMM, discriminative HMM can use examples from both classes when learning the model for a specific class. We have tested our method on both simulated and real time series expression data. As we show, our method improves upon prior methods and can suggest markers for specific disease and response stages that are not found when using traditional classifiers. Availability: Matlab implementation is available from http://www.cs.cmu.edu/~thlin/tram/ Contact: zivbj@cs.cmu.edu