Notable work | - Inhibition of the measurement of the wave function of a single quantum system in repeated weak quantum nondemolition measurements (Yoshihisa Yamamoto, 10.1103/PHYSREVLETT.74.4106, 1995, corresponding author, Physical Review Letters)
- Singular value decomposition for genome-wide expression data processing and modeling (Proceedings of the National Academy of Sciences of the United States of America, 2000, 10.1073/PNAS.97.18.10101, corresponding author, principal investigator)
- Quantum Measurement of a Single System (Yoshihisa Yamamoto, David B. Oberman, Wiley, 2001, Outstanding Doctoral Thesis Research in Atomic, Molecular, or Optical Physics (DAMOP) Award, corresponding author)
- Generalized singular value decomposition for comparative analysis of genome-scale expression data sets of two different organisms (2003, Proceedings of the National Academy of Sciences of the United States of America, corresponding author, principal investigator)
- Integrative analysis of genome-scale data by using pseudoinverse projection predicts novel correlation between DNA replication and RNA transcription (Proceedings of the National Academy of Sciences of the United States of America, Gene H. Golub, corresponding author, principal investigator, 2004)
- Reconstructing the pathways of a cellular system from genome-scale signals by using matrix and tensor computations (Proceedings of the National Academy of Sciences of the United States of America, Gene H. Golub, corresponding author, principal investigator, 2005)
- Singular value decomposition of genome-scale mRNA lengths distribution reveals asymmetry in RNA gel electrophoresis band broadening (Proceedings of the National Academy of Sciences of the United States of America, Gene H. Golub, corresponding author, principal investigator, 2006)
- Discovery of principles of nature from mathematical modeling of DNA microarray data (2006, Proceedings of the National Academy of Sciences of the United States of America, corresponding author, principal investigator)
- A tensor higher-order singular value decomposition for integrative analysis of DNA microarray data from different studies (Proceedings of the National Academy of Sciences of the United States of America, Gene H. Golub, corresponding author, principal investigator, 2007)
- Global effects of DNA replication and DNA replication origin activity on eukaryotic gene expression (2009, Molecular Systems Biology, corresponding author, principal investigator)
- Tensor decomposition reveals concurrent evolutionary convergences and divergences and correlations with structural motifs in ribosomal RNA (PLOS One, 2011, 10.1371/JOURNAL.PONE.0018768, corresponding author, principal investigator)
- A higher-order generalized singular value decomposition for comparison of global mRNA expression from multiple organisms (PLOS One, 2011, 10.1371/JOURNAL.PONE.0028072, corresponding author, principal investigator)
- GSVD comparison of patient-matched normal and tumor aCGH profiles reveals global copy-number alterations predicting glioblastoma multiforme survival (2012, PLOS One, corresponding author, principal investigator)
- SVD identifies transcript length distribution functions from DNA microarray data and reveals evolutionary forces globally affecting GBM metabolism (2013, PLOS One, corresponding author, principal investigator)
- Tensor GSVD of patient- and platform-matched tumor and normal DNA copy-number profiles uncovers chromosome arm-wide patterns of tumor-exclusive platform-consistent alterations encoding for cell transformation and predicting ovarian cancer survival (2015, PLOS One, corresponding author, principal investigator)
- Platform-Independent Genome-Wide Pattern of DNA Copy-Number Alterations Predicting Astrocytoma Survival and Response to Treatment Revealed by the GSVD Formulated as a Comparative Spectral Decomposition (2016, PLOS One, corresponding author, principal investigator)
- Mathematically universal and biologically consistent astrocytoma genotype encodes for transformation and predicts survival phenotype (2018, APL Bioengineering, corresponding author, principal investigator)
- GSVD- and tensor GSVD-uncovered patterns of DNA copy-number alterations predict adenocarcinomas survival in general and in response to platinum (2019, APL Bioengineering, corresponding author, principal investigator)
- Multi-Tensor Decompositions for Personalized Cancer Diagnostics, Prognostics, and Therapeutics (lecturer, Michael Saunders, Stanford Institute for Computational & Mathematical Engineering, 2020)
- Multi-Tensor Decompositions for Personalized Cancer Diagnostics, Prognostics, and Therapeutics (lecturer, Amazon Web Services, 2020)
- Retrospective clinical trial experimentally validates glioblastoma genome-wide pattern of DNA copy-number alterations predictor of survival (2020, APL Bioengineering, corresponding author, principal investigator)
- Discovering Genome-Scale Predictors of Survival and Response to Treatment with Multi-Tensor Decompositions (lecturer, American Association for Cancer Research, 2020)
- Retrospective Clinical Trial Experimentally Validates Glioblastoma Genome-Wide Pattern of DNA Copy-Number Alterations Predictor of Survival (lecturer, Physical Sciences in Oncology Network, 2020)
- How High-Dimensional Multi-Tensor Machine Learning Is Being Used to Improve the Prognosis, Diagnosis, and Treatment of Gliomas (lecturer, Society for Neuro-Oncology (SNO), 2021)
- Mathematical discovery and computational validation of two orthogonal mechanistically-driven whole-genome genotype–survival phenotype relationships in pediatric neuroblastoma nerve cancer (Association for Computing Machinery, Institute of Electrical and Electronics Engineers, 2023, lecturer, corresponding author, principal investigator, ACM/IEEE Supercomputing Conference)
- AI/ML-derived whole-genome predictor prospectively and clinically predicts survival and response to treatment in brain cancer (Association for Computing Machinery, Institute of Electrical and Electronics Engineers, 2023, lecturer, corresponding author, principal investigator, ACM/IEEE Supercomputing Conference)
- Prism AI Presents: AI-Powered Biomarker Discovery for Personalized Medicine (lecturer, Precision Medicine World Conference (PMWC), 2024)
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