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Found 3 papers, 1 papers with code
Merging 1D and 3D genomic information: Challenges in modelling and validation
Genome organization in eukaryotes during interphase stems from the delicate balance between non-random correlations present in the DNA polynucleotide linear sequence and the physico/chemical reactions which shape continuously the form and structure of DNA and chromatin inside the nucleus of the cell.
WISDoM: characterizing neurological timeseries with the Wishart distribution
WISDoM (Wishart Distributed Matrices) is a new framework for the quantification of deviation of symmetric positive-definite matrices associated to experimental samples, like covariance or correlation matrices, from expected ones governed by the Wishart distribution WISDoM can be applied to tasks of supervised learning, like classification, in particular when such matrices are generated by data of different dimensionality (e. g. time series with same number of variables but different time sampling).
Circumventing the Curse of Dimensionality in Magnetic Resonance Fingerprinting through a Deep Learning Approach
Results demonstrated that training with random sampling and different levels of noise variance yielded the best performance.
Medical Physics
