Optimal Scheduling of Fallible Inspections. - Giovanni Parmigiani (Oper. Res., 1996)

Penalized likelihood for sparse contingency tables with an application to full-length cDNA libraries. - Corinne Dahinden; Giovanni Parmigiani; Mark C. Emerick; Peter Bühlmann (BMC Bioinform., 2007)

Multivariate Analysis and Visualization of Splicing Correlations in Single-Gene Transcriptomes. - Mark C. Emerick; Giovanni Parmigiani; William S. Agnew (BMC Bioinform., 2007)

Pre-processing Agilent microarray data. - Marianna Zahurak; Giovanni Parmigiani; Wayne Yu; Robert B. Scharpf; David Berman; Edward M. Schaeffer; Shabana Shabbeer; Leslie Cope (BMC Bioinform., 2007)

Identifying differential correlation in gene/pathway combinations. - Rosemary Braun; Leslie Cope; Giovanni Parmigiani (BMC Bioinform., 2008)

The ordering of expression among a few genes can provide simple cancer biomarkers and signal BRCA1 mutations. - Xue Lin; Bahman Afsari; Luigi Marchionni; Leslie Cope; Giovanni Parmigiani; Daniel Q. Naiman; Donald Geman (BMC Bioinform., 2009)

CoGAPS: an R/C++ package to identify patterns and biological process activity in transcriptomic data. - Elana J. Fertig; Jie Ding; Alexander V. Favorov; Giovanni Parmigiani; Michael F. Ochs (Bioinform., 2010)

Estimation of sequencing error rates in short reads. - Xin Victoria Wang; Natalie Blades; Jie Ding; Razvan Sultana; Giovanni Parmigiani (BMC Bioinform., 2012)

curatedOvarianData: clinically annotated data for the ovarian cancer transcriptome. - Benjamin Frederick Ganzfried; Markus Riester; Benjamin Haibe-Kains; Thomas Risch; Svitlana Tyekucheva; Ina Jazic; Xin Victoria Wang; Mahnaz Ahmadifar; Michael J. Birrer; Giovanni Parmigiani; Curtis Huttenhower; Levi Waldron (Database J. Biol. Databases Curation, 2013)

DeMix: deconvolution for mixed cancer transcriptomes using raw measured data. - Jaeil Ahn; Ying Yuan; Giovanni Parmigiani; Milind B. Suraokar; Lixia Diao; Ignacio I. Wistuba; Wenyi Wang 001 (Bioinform., 2013)

Cross-study validation for the assessment of prediction algorithms. - Christoph Bernau; Markus Riester; Anne-Laure Boulesteix; Giovanni Parmigiani; Curtis Huttenhower; Levi Waldron; Lorenzo Trippa (Bioinform., 2014)

Más-o-menos: a simple sign averaging method for discrimination in genomic data analysis. - Sihai Dave Zhao; Giovanni Parmigiani; Curtis Huttenhower; Levi Waldron (Bioinform., 2014)

Integrative correlation: Properties and relation to canonical correlations. - Leslie Cope; Daniel Q. Naiman; Giovanni Parmigiani (J. Multivar. Anal., 2014)

Using Machine Learning and Natural Language Processing to Review and Classify the Medical Literature on Cancer Susceptibility Genes. - Yujia Bao; Zhengyi Deng; Yan Wang; Heeyoon Kim; Victor Diego Armengol; Francisco Acevedo; Nofal Ouardaoui; Cathy Wang; Giovanni Parmigiani; Regina Barzilay; Danielle Braun; Kevin S. Hughes (CoRR, 2019)

Merging versus Ensembling in Multi-Study Machine Learning: Theoretical Insight from Random Effects. - Zoe Guan; Giovanni Parmigiani; Prasad Patil (CoRR, 2019)

Tree-Weighting for Multi-Study Ensemble Learners. - Maya Ramchandran; Prasad Patil; Giovanni Parmigiani (PSB, 2020)

Representation via Representations: Domain Generalization via Adversarially Learned Invariant Representations. - Zhun Deng; Frances Ding; Cynthia Dwork; Rachel Hong; Giovanni Parmigiani; Prasad Patil; Pragya Sur (CoRR, 2020)

Receiver operating characteristic curves with an indeterminacy zone. - Giovanni Parmigiani (Pattern Recognit. Lett., 2020)

covid19census: U.S. and Italy COVID-19 metrics and other epidemiological data. - Claudio Zanettini; Mohamed Omar; Wikum Dinalankara; Eddie Luidy Imada; Elizabeth Colantuoni; Giovanni Parmigiani; Luigi Marchionni (Database J. Biol. Databases Curation, 2021)

Robustifying genomic classifiers to batch effects via ensemble learning. - Yuqing Zhang; Prasad Patil; W. Evan Johnson; Giovanni Parmigiani (Bioinform., 2021)

Cross-Cluster Weighted Forests. - Maya Ramchandran; Rajarshi Mukherjee; Giovanni Parmigiani (CoRR, 2021)

Prediction of Hereditary Cancers Using Neural Networks. - Zoe Guan; Giovanni Parmigiani; Danielle Braun; Lorenzo Trippa (CoRR, 2021)

Optimal Ensemble Construction for Multi-Study Prediction with Applications to COVID-19 Excess Mortality Estimation. - Gabriel Loewinger; Rolando Acosta Nunez; Rahul Mazumder; Giovanni Parmigiani (CoRR, 2021)

Multi-Study Boosting: Theoretical Considerations for Merging vs. Ensembling. - Cathy Shyr; Pragya Sur; Giovanni Parmigiani; Prasad Patil (CoRR, 2022)

Defining Replicability of Prediction Rules. - Giovanni Parmigiani (CoRR, 2023)

Multi-source domain adaptation for regression. - Yujie Wu; Giovanni Parmigiani; Boyu Ren (CoRR, 2023)