Studying and modelling dynamic biological processes using time-series gene expression data
Biological processes are often dynamic, thus researchers must monitor their activity at
multiple time points. The most abundant source of information regarding such dynamic …
multiple time points. The most abundant source of information regarding such dynamic …
Dealing with missing values in large-scale studies: microarray data imputation and beyond
T Aittokallio - Briefings in bioinformatics, 2010 - academic.oup.com
High-throughput biotechnologies, such as gene expression microarrays or mass-
spectrometry-based proteomic assays, suffer from frequent missing values due to various …
spectrometry-based proteomic assays, suffer from frequent missing values due to various …
A time series forest for classification and feature extraction
A tree-ensemble method, referred to as time series forest (TSF), is proposed for time series
classification. TSF employs a combination of entropy gain and a distance measure, referred …
classification. TSF employs a combination of entropy gain and a distance measure, referred …
Time–frequency time–space LSTM for robust classification of physiological signals
TD Pham - Scientific reports, 2021 - nature.com
Automated analysis of physiological time series is utilized for many clinical applications in
medicine and life sciences. Long short-term memory (LSTM) is a deep recurrent neural …
medicine and life sciences. Long short-term memory (LSTM) is a deep recurrent neural …
Functional echo state network for time series classification
Echo state networks (ESNs) are a new approach to recurrent neural networks (RNNs) that
have been successfully applied in many domains. Nevertheless, an ESN is a predictive …
have been successfully applied in many domains. Nevertheless, an ESN is a predictive …
Early classification of multivariate temporal observations by extraction of interpretable shapelets
MF Ghalwash, Z Obradovic - BMC bioinformatics, 2012 - Springer
Background Early classification of time series is beneficial for biomedical informatics
problems such including, but not limited to, disease change detection. Early classification …
problems such including, but not limited to, disease change detection. Early classification …
Early classification of multivariate time series using a hybrid hmm/svm model
MF Ghalwash, D Ramljak… - 2012 IEEE International …, 2012 - ieeexplore.ieee.org
Early classification of time series has been receiving a lot of attention as of late, particularly
in the context of gene expression. In the biomédical realm, early classification can be of …
in the context of gene expression. In the biomédical realm, early classification can be of …
Natural cubic spline regression modeling followed by dynamic network reconstruction for the identification of radiation-sensitivity gene association networks from time …
A Michna, H Braselmann, M Selmansberger, A Dietz… - PloS one, 2016 - journals.plos.org
Gene expression time-course experiments allow to study the dynamics of transcriptomic
changes in cells exposed to different stimuli. However, most approaches for the …
changes in cells exposed to different stimuli. However, most approaches for the …
Non-negative matrix and tensor factorization based classification of clinical microarray gene expression data
Non-negative information can benefit the analysis of microarray data. This paper
investigates the classification performance of non-negative matrix factorization (NMF) over …
investigates the classification performance of non-negative matrix factorization (NMF) over …
Detection and interpretation of metabolite–transcript coresponses using combined profiling data
H Redestig, IG Costa - Bioinformatics, 2011 - academic.oup.com
Motivation: Studying the interplay between gene expression and metabolite levels can yield
important information on the physiology of stress responses and adaptation strategies …
important information on the physiology of stress responses and adaptation strategies …