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Tumor clustering based on biomolecular data plays a very important role for cancer classifications discovery. To further improve the robustness, stability and accuracy of tumor clustering, we develop a novel dimension reduction method named p-norm singular value decomposition (PSVD) to seek a low-rank approximation matrix to the bimolecular data. To enhance the robustness to outliers, the Lp-norm...
Based on various genomic information of chimeric transcript, recent studies used machine-learning methods to predict the oncogenic potentials for chimeric transcripts, however these works ignored transcriptional signature of those chimeric transcripts. Based on clonal evolution theory, we hypothesized that a chimeric transcript is more likely to be an oncogenic ‘driver’ mutation, if the neoplastic...
Mutations in the tet oncogene family member 2 gene (TET2) are frequently found in adult patients with acute myeloid leukemia (AML). Reports of TET2 mutations in children are limited. We assessed the prevalence of TET2 mutations in Taiwanese children with AML and analyzed their prognosis.Between 1997 and 2010, a total of 69 consecutive children with AML were enrolled at the National Taiwan University...
Protein classification plays an important role in the research in Bioinformatics. Many discriminative methods, including the SVM based algorithms are used to do this job. In order to use these methods, variable length protein sequences must be converted into fixed-length dimensional vectors. The current work presents a new method of converting sequences into vectors. The method first constructs profile...
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