R
Razin A. Shaikh
Researcher at University of Oxford
Publications - 12
Citations - 42
Razin A. Shaikh is an academic researcher from University of Oxford. The author has contributed to research in topics: Computer science & Negation. The author has an hindex of 2, co-authored 5 publications receiving 13 citations. Previous affiliations of Razin A. Shaikh include The University of Nottingham Ningbo China.
Papers
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Proceedings ArticleDOI
Classification of Five Cell Types from PBMC Samples using Single Cell Transcriptomics and Artificial Neural Networks
Razin A. Shaikh,Jiahui Zhong,Minjie Lyu,Sen Lin,Derin B. Keskin,Guang Lan Zhang,Lou Chitkushev,Vladimir Brusic +7 more
TL;DR: Using 27 human single cell transcriptomics data sets, an artificial neural network model for classification of Peripheral Blood Mononuclear Cells (PBMC) is developed by combining multiple independent data sets to form training data sets.
Proceedings ArticleDOI
Completeness for arbitrary finite dimensions of ZXW-calculus, a unifying calculus
TL;DR: The ZXW-calculus as discussed by the authors is a universal graphical language for qubit quantum computation, meaning that every linear map between qubits can be expressed in the ZX-Calculus.
How to sum and exponentiate Hamiltonians in ZXW calculus
TL;DR: In this article , the authors develop practical summation techniques in ZXW calculus to reason about quantum dynamics, such as unitary time evolution, and demonstrate the linearity of the Schr¨odinger equation and give a diagrammatic representation of the Hamiltonian in Greene-Diniz et al.
Proceedings ArticleDOI
Single Cell Transcriptomics Reveals Summary Patterns Specific for PBMCs and Other Cell Types
TL;DR: The results indicate that classification methods based on overall properties of SCT data sets provide a useful first step for classification of cell types and subtypes.
Journal ArticleDOI
The Conceptual VAE
TL;DR: A new model of concepts is presented, based on the framework of variational autoencoders, which is designed to have attractive properties such as factored conceptual domains, and at the same time be learnable from data.