Voxel Weight Matrix-Based Feature Extraction for Biomedical Applications
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Fahad Albalawi | Abderrazak Chahid | Taous-Meriem Laleg-Kirati | Sultan Alshehri | T. Laleg‐Kirati | Fahad Albalawi | Abderrazak Chahid | S. Alshehri
[1] N. Jamal,et al. An fMRI study of English and Spanish word reading in bilingual adults , 2020, Brain and Language.
[2] Nikola Kasabov,et al. Integrating Time-Space and Orientation. A Case Study on fMRI + DTI Brain Data , 2018, Springer Series on Bio- and Neurosystems.
[3] Jun Zhang,et al. Pharmacologically informed machine learning approach for identifying pathological states of unconsciousness via resting-state fMRI , 2020, NeuroImage.
[4] P. Bucher. Weight matrix descriptions of four eukaryotic RNA polymerase II promoter elements derived from 502 unrelated promoter sequences. , 1990, Journal of molecular biology.
[5] Jasmin Kevric,et al. Epileptic seizure detection using hybrid machine learning methods , 2017, Neural Computing and Applications.
[6] F. Pukelsheim. The Three Sigma Rule , 1994 .
[7] N. Logothetis,et al. Neurophysiological investigation of the basis of the fMRI signal , 2001, Nature.
[8] R. McCarley,et al. A review of MRI findings in schizophrenia , 2001, Schizophrenia Research.
[9] Stuart D. Washington,et al. A Machine Learning Approach to the Differentiation of Functional Magnetic Resonance Imaging Data of Chronic Fatigue Syndrome (CFS) From a Sedentary Control , 2020, Frontiers in Computational Neuroscience.
[10] Fahad Albalawi,et al. Hybrid model for efficient prediction of poly(A) signals in human genomic DNA. , 2019, Methods.
[11] Chadia Zayane-Aissa,et al. Nonlinear neural network for hemodynamic model state and input estimation using fMRI data , 2014, Biomed. Signal Process. Control..
[12] Wenjian Wang,et al. Granular support vector machine: a review , 2017, Artificial Intelligence Review.
[13] Alfred Mertins,et al. Machine Learning Based Classification of Resting-State fMRI Features Exemplified by Metabolic State (Hunger/Satiety) , 2019, Front. Hum. Neurosci..
[14] David Pollard,et al. Quantization and the method of k -means , 1982, IEEE Trans. Inf. Theory.
[15] S. Ogawa,et al. Oxygenation‐sensitive contrast in magnetic resonance image of rodent brain at high magnetic fields , 1990, Magnetic resonance in medicine.
[16] Ghassan Hamarneh,et al. Generalized Sparse Classifiers for Decoding Cognitive States in fMRI , 2010, MLMI.
[17] Jonathan D. Cohen,et al. A Developmental Functional MRI Study of Prefrontal Activation during Performance of a Go-No-Go Task , 1997, Journal of Cognitive Neuroscience.
[18] K Lehnertz,et al. Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.
[19] Saurabh Sinha,et al. On counting position weight matrix matches in a sequence, with application to discriminative motif finding , 2006, ISMB.
[20] S. Rombouts,et al. Altered resting state networks in mild cognitive impairment and mild Alzheimer's disease: An fMRI study , 2005, Human brain mapping.
[21] Kaustubh Supekar,et al. Sparse logistic regression for whole-brain classification of fMRI data , 2010, NeuroImage.
[22] Hariharan Ramasangu,et al. Classification of cognitive state using clustering based maximum margin feature selection framework , 2017, 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI).
[23] Paul J. Laurienti,et al. An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets , 2003, NeuroImage.
[24] Geoffrey J. Gordon,et al. The support vector decomposition machine , 2006, ICML.
[25] Toufik Bouden,et al. A deconvolution scheme for the stochastic metabolic/hemodynamic model (sMHM) based on the square root cubature Kalman filter and maximum likelihood estimation , 2018, Biomed. Signal Process. Control..
[26] Tom M. Mitchell,et al. Learning to Decode Cognitive States from Brain Images , 2004, Machine Learning.
[27] Tom Mitchell,et al. Detecting Cognitive States Using Machine Learning , 2002 .
[28] Neelam Sinha,et al. Cognitive state classification using transformed fMRI data , 2014, 2014 International Conference on Signal Processing and Communications (SPCOM).
[29] Hariharan Ramasangu,et al. Classification of cognitive state using statistics of split time series , 2016, 2016 IEEE Annual India Conference (INDICON).
[30] Zixiang Xiong,et al. Optimal number of features as a function of sample size for various classification rules , 2005, Bioinform..
[31] Yingli Lu,et al. Regional homogeneity approach to fMRI data analysis , 2004, NeuroImage.
[32] Patricia Figueiredo,et al. Decoding visual brain states from fMRI using an ensemble of classifiers , 2012, Pattern Recognit..
[33] J. Thompson,et al. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. , 1994, Nucleic acids research.