Mark A. Rubin

发表

Genomic Testing & Precision Medicine: Bigger Is Better

Olivier Elemento, Mark A. Rubin, 2016 .

A hierarchical Naïve Bayes Model for handling sample heterogeneity in classification problems: an application to tissue microarrays

Riccardo Bellazzi, Paolo Magni, Mark A. Rubin, 2006, BMC Bioinformatics.

Buffered Reset Leads to Improved Compression in Fuzzy ARTMAP Classification of Radar Range Profiles

Stephen Grossberg, William W. Streilein, Mark A. Rubin, 1996 .

Application of fuzzy ARTMAP and ART-EMAP to automatic target recognition using radar range profiles

Mark A. Rubin, M. Rubin, 1995, Neural Networks.

The cancer precision medicine knowledge base for structured clinical-grade mutations and interpretations

Marcin Imielinski, Olivier Elemento, Mark A. Rubin, 2016, J. Am. Medical Informatics Assoc..

A comparison of classifiers for radar emitter type identification

Stephen Grossberg, Eric Granger, Mark A. Rubin, 1999 .

Expression profiling of human renal carcinomas with functional taxonomic analysis

Mark A. Rubin, Michael A. Gieseg, Michael Z. Man, 2002, BMC Bioinformatics.

Analyses of non-coding somatic drivers in 2,658 cancer whole genomes

Jun Yu, Chen Hong, Ken Chen, 2020, Nature.

The Precision Medicine Knowledge Base: an online application for collaborative editing, maintenance and sharing of structured clinical-grade cancer mutations interpretations

Marcin Imielinski, Olivier Elemento, Mark A. Rubin, 2016, bioRxiv.

Internet-based profiler system as integrative framework to support translational research

Alberto Riva, Ronglai Shen, Robert Kim, 2005, BMC Bioinformatics.

A What-and-Where fusion neural network for recognition and tracking of multiple radar emitters

Stephen Grossberg, Eric Granger, Pierre Lavoie, 2001, Neural Networks.

Probability, Preclusion and Biological Evolution in Heisenberg-Picture Everett Quantum Mechanics.

Mark A. Rubin, 2020 .

ARTMAP-FD: familiarity discrimination applied to radar target recognition

Gail A. Carpenter, William W. Streilein, Mark A. Rubin, 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

Classification of incomplete data using the fuzzy ARTMAP neural network

Stephen Grossberg, Eric Granger, Pierre Lavoie, 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

Context-based vision system for place and object recognition

Antonio Torralba, Kevin P. Murphy, William T. Freeman, 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

HIT'nDRIVE: Multi-driver Gene Prioritization Based on Hitting Time

Thomas Sauerwald, Gholamreza Haffari, Colin Collins, 2014, RECOMB.

Encapsulated Objects for Neural Network Simulation

Mark A. Rubin, 1993 .

Familiarity Discrimination of Radar Pulses

Stephen Grossberg, Eric Granger, William W. Streilein, 1998, NIPS.

GRAM: A GeneRAlized Model to predict the molecular effect of a non-coding variant in a cell-type specific manner

Mark Gerstein, Sandra Cohen, Mark A. Rubin, 2018, bioRxiv.

Locality and Counterfactual Reasoning in the Everett Interpretation of Heisenberg-Picture Quantum Mechanics

Mark A. Rubin, M. Rubin, 2001 .

Radar ESM with a what-and-where fusion neural network

Stephen Grossberg, Eric Granger, Mark A. Rubin, 2001, Neural Networks for Signal Processing XI: Proceedings of the 2001 IEEE Signal Processing Society Workshop (IEEE Cat. No.01TH8584).

EZH2 is a marker of aggressive breast cancer and promotes neoplastic transformation of breast epithelial cells

Debashis Ghosh, Ronglai Shen, Daniel F. Hayes, 2003, Proceedings of the National Academy of Sciences of the United States of America.

Clinical significance of TTF‐1 protein expression and TTF‐1 gene amplification in lung adenocarcinoma

Mark A. Rubin, Barbara A. Weir, Matthew Meyerson, 2009, Journal of cellular and molecular medicine.

Profiling Critical Cancer Gene Mutations in Clinical Tumor Samples

Keith L. Ligon, Sandro Santagata, Mark A. Rubin, 2009, PloS one.

Discovery and characterization of coding and non-coding driver mutations in more than 2,500 whole cancer genomes

Lucas Lochovsky, Donghoon Lee, Chris Sander, 2017, bioRxiv.

The critical role of the pathologist in determining eligibility for active surveillance as a management option in patients with prostate cancer: consensus statement with recommendations supported by the College of American Pathologists, International Society of Urological Pathology, Association of D

Lawrence D. True, Beatrice S. Knudsen, Mark A. Rubin, 2014, Archives of pathology & laboratory medicine.

An illustration of the potential for mapping MRI/MRS parameters with genetic over-expression profiles in human prostate cancer

B. Nicolas Bloch, Robert E. Lenkinski, John V. Frangioni, 2008, Magnetic Resonance Materials in Physics, Biology and Medicine.

Translational Crossroads for Biomarkers

Robert C. Bast, Mark A. Rubin, Robert Veltri, 2005, Clinical Cancer Research.

Integrative Clinical Genomics of Advanced Prostate Cancer

Lawrence D. True, Johann S. de Bono, Colin C. Pritchard, 2015, Cell.

Quantitative determination of expression of the prostate cancer protein alpha-methylacyl-CoA racemase using automated quantitative analysis (AQUA): a novel paradigm for automated and continuous biomarker measurements.

Mark A. Rubin, Arul M. Chinnaiyan, David L. Rimm, 2004, The American journal of pathology.

The genomic complexity of primary human prostate cancer

Eric S. Lander, Kristian Cibulskis, Todd R. Golub, 2010, Nature.

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Clement Adebamowo, Ritika Kundra, Steven J. M. Jones, 2018, Cell reports.

Humoral Immune Response to α-Methylacyl-CoA Racemase and Prostate Cancer

Debashis Ghosh, Mark A. Rubin, Arul M. Chinnaiyan, 2004 .

Oncogenic transcription factors as master regulators of chromatin topology

Mark A. Rubin, Olivier Elemento, M. Rubin, 2012, Cell cycle.

Prostate Cancer Expression Profiles Reveals Pathway Dysregulation in Meta-Analysis of Microarrays : Interstudy Validation of Gene Updated

Debashis Ghosh, Mark A. Rubin, Arul M. Chinnaiyan, 2002 .

Pathway and network analysis of more than 2,500 whole cancer genomes

Kathleen Marchal, Alfonso Valencia, Icgc, 2018, bioRxiv.

Image-based computational quantification and visualization of genetic alterations and tumour heterogeneity

Joachim M. Buhmann, Peter J. Wild, Maria Gabrani, 2016, Scientific Reports.

Organoid Cultures Derived from Patients with Advanced Prostate Cancer

Hans Clevers, Dong Gao, Stephen B. Solomon, 2014, Cell.

Autoantibody Signatures in Prostate Cancer

Jianjun Yu, Debashis Ghosh, Ronglai Shen, 2006 .

Health: Make precision medicine work for cancer care

Mark A. Rubin, M. Rubin, 2015, Nature.

Whole genome scanning identifies genotypes associated with recurrence and metastasis in prostate tumors

Jane Fridlyand, Ajay N. Jain, Mark A. Rubin, 2004 .

Erratum: Inferring loss-of-heterozygosity from unpaired tumors using high-density oligonucleotide SNP arrays (PLoS Computational Biology 2, 5, doi:10.1371/journal.pcbi.0020041)

Rameen Beroukhim, Edward A. Fox, Mark A. Rubin, 2007 .

Decreased α-Methylacyl CoA Racemase Expression in Localized Prostate Cancer is Associated with an Increased Rate of Biochemical Recurrence and Cancer-Specific Death

Debashis Ghosh, Ronglai Shen, Mark A. Rubin, 2005, Cancer Epidemiology Biomarkers & Prevention.

Inferring Loss-of-Heterozygosity From Tumor-only Samples Using High-Density Oligonucleotide SNP Arrays

Wing Hung Wong, Rameen Beroukhim, Edward A. Fox, 2022 .

Activation of β-Catenin Signaling in Prostate Cancer by Peptidyl-Prolyl Isomerase Pin1-Mediated Abrogation of the Androgen Receptor-β-Catenin Interaction

Mark A. Rubin, M. Rubin, Xiao Zhen Zhou, 2006, Molecular and Cellular Biology.

SNP panel identification assay (SPIA): a genetic-based assay for the identification of cell lines

Levi Garraway, Rameen Beroukhim, Mark A. Rubin, 2008, Nucleic acids research.