ATOM: a powerful gene-based association test by combining optimally weighted markers. - Mingyao Li; Kai Wang 049; Struan F. A. Grant; Hakon Hakonarson; Chun Li (Bioinform., 2009)

Multiple testing in genome-wide association studies via hidden Markov models. - Zhi Wei 001; Wenguang Sun; Kai Wang 049; Hakon Hakonarson (Bioinform., 2009)

Mitochondrial Genome Sequence Analysis: A custom bioinformatics pipeline substantially improves Affymetrix MitoChip v2.0 call rate and accuracy. - Hongbo M. Xie; Juan C. Perin; Theodore G. Schurr; Matthew C. Dulik; Sergey I. Zhadanov; Joseph A. Baur; Michael P. King; Emily Place; Colleen Clarke; Michael Grauer; Jonathan Schug; Avni Santani; et al. (BMC Bioinform., 2011)

Being right is its own reward: Load and performance related ventral striatum activation to correct responses during a working memory task in youth. - Theodore D. Satterthwaite; Kosha Ruparel; James Loughead; Mark A. Elliott; Raphael T. Gerraty; Monica E. Calkins; Hakon Hakonarson; Ruben C. Gur; Raquel E. Gur; Daniel H. Wolf (NeuroImage, 2012)

Impact of in-scanner head motion on multiple measures of functional connectivity: Relevance for studies of neurodevelopment in youth. - Theodore D. Satterthwaite; Daniel H. Wolf; James Loughead; Kosha Ruparel; Mark A. Elliott; Hakon Hakonarson; Ruben C. Gur; Raquel E. Gur (NeuroImage, 2012)

An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data. - Theodore D. Satterthwaite; Mark A. Elliott; Raphael T. Gerraty; Kosha Ruparel; James Loughead; Monica E. Calkins; Simon B. Eickhoff; Hakon Hakonarson; Ruben C. Gur; Raquel E. Gur; Daniel H. Wolf (NeuroImage, 2013)

Heterogeneous impact of motion on fundamental patterns of developmental changes in functional connectivity during youth. - Theodore D. Satterthwaite; Daniel H. Wolf; Kosha Ruparel; Güray Erus; Mark A. Elliott; Simon B. Eickhoff; Efstathios D. Gennatas; Chad Jackson; Karthik Prabhakaran; Alex R. Smith; Hakon Hakonarson; Ragini Verma; et al. (NeuroImage, 2013)

Neuroimaging of the Philadelphia Neurodevelopmental Cohort. - Theodore D. Satterthwaite; Mark A. Elliott; Kosha Ruparel; James Loughead; Karthik Prabhakaran; Monica E. Calkins; Ryan Hopson; Chad Jackson; Jack Keefe; Marisa Riley; Frank D. Mentch; Patrick Sleiman; et al. (NeuroImage, 2014)

Identifying gene-gene interactions that are highly associated with Body Mass Index using Quantitative Multifactor Dimensionality Reduction (QMDR). - Rishika De; Shefali S. Verma; Fotios Drenos; Emily Rose Holzinger; Michael Holmes; Molly A. Hall; David R. Crosslin; David Carrell; Hakon Hakonarson; Gail P. Jarvik; Eric B. Larson; Jennifer A. Pacheco; et al. (BioData Min., 2015)

Biofilter as a Functional Annotation Pipeline for Common and Rare Copy Number Burden. - Dokyoon Kim; Anastasia Lucas; Joseph Glessner; Shefali S. Verma; Yukiko Bradford; Ruowang Li; Alex T. Frase; Hakon Hakonarson; Peggy L. Peissig; Murray H. Brilliant; Marylyn D. Ritchie (PSB, 2016)

The impact of quality assurance assessment on diffusion tensor imaging outcomes in a large-scale population-based cohort. - David R. Roalf; Megan Quarmley; Mark A. Elliott; Theodore D. Satterthwaite; Simon N. Vandekar; Kosha Ruparel; Efstathios D. Gennatas; Monica E. Calkins; Tyler M. Moore; Ryan Hopson; Karthik Prabhakaran; Chad Jackson; et al. (NeuroImage, 2016)

The Philadelphia Neurodevelopmental Cohort: A publicly available resource for the study of normal and abnormal brain development in youth. - Theodore D. Satterthwaite; John J. Connolly; Kosha Ruparel; Monica E. Calkins; Chad Jackson; Mark A. Elliott; David R. Roalf; Karthik Prabhakaran; Ryan Hopson; Meckenzie Behr; Haijun Qiu; Frank D. Mentch; et al. (NeuroImage, 2016)

DeepPolyA: A Convolutional Neural Network Approach for Polyadenylation Site Prediction. - Xin Gao 004; Jie Zhang 049; Zhi Wei 001; Hakon Hakonarson (IEEE Access, 2018)

Detecting potential pleiotropy across cardiovascular and neurological diseases using univariate, bivariate, and multivariate methods on 43, 870 individuals from the eMERGE network. - Xinyuan Zhang 003; Yogasudha Veturi; Shefali Setia Verma; William Bone; Anurag Verma; Anastasia Lucas; Scott J. Hebbring; Joshua C. Denny; Ian B. Stanaway; Gail P. Jarvik; David R. Crosslin; Eric B. Larson; et al. (PSB, 2019)

Making work visible for electronic phenotype implementation: Lessons learned from the eMERGE network. - Ning Shang; Cong Liu 020; Luke V. Rasmussen; Casey N. Ta; Robert J. Carroll; Barbara Benoit; Todd Lingren; Ozan Dikilitas; Frank D. Mentch; David S. Carrell; Wei-Qi Wei; Yuan Luo 001; et al. (J. Biomed. Informatics, 2019)

Elucidation of DNA methylation on N6-adenine with deep learning. - Fei Tan 002; Tian Tian; Xiurui Hou; Xiang Yu; Lei Gu; Fernanda Mafra; Brian D. Gregory; Zhi Wei 001; Hakon Hakonarson (Nat. Mach. Intell., 2020)

DeepCNV: a deep learning approach for authenticating copy number variations. - Joseph Glessner; Xiurui Hou; Cheng Zhong; Jie Zhang 049; Munir Khan; Fabian Brand; Peter M. Krawitz; Patrick Sleiman; Hakon Hakonarson; Zhi Wei 001 (Briefings Bioinform., 2021)

Performance of model-based multifactor dimensionality reduction methods for epistasis detection by controlling population structure. - Fentaw Abegaz; François Van Lishout; Jestinah M. Mahachie John; Kridsadakorn Chaichoompu; Archana Bhardwaj; Diane Duroux; Elena S. Gusareva; Zhi Wei 001; Hakon Hakonarson; Kristel Van Steen (BioData Min., 2021)

Genomic considerations for FHIR®; eMERGE implementation lessons. - Mullai Murugan; Lawrence J. Babb; Casey Overby Taylor; Luke V. Rasmussen; Robert R. Freimuth; Eric Venner; Fei Yan; Victoria Yi; Stephen J. Granite; Hana Zouk; Samuel J. Aronson; Kevin Power; et al. (J. Biomed. Informatics, 2021)

Under-specification as the source of ambiguity and vagueness in narrative phenotype algorithm definitions. - Jingzhi Yu; Jennifer A. Pacheco; Anika S. Ghosh; Yuan Luo 001; Chunhua Weng; Ning Shang; Barbara Benoit; David S. Carrell; Robert J. Carroll; Ozan Dikilitas; Robert R. Freimuth; Vivian S. Gainer; et al. (BMC Medical Informatics Decis. Mak., 2022)

Characterizing variability of electronic health record-driven phenotype definitions. - Pascal S. Brandt; Abel N. Kho; Yuan Luo 001; Jennifer A. Pacheco; Theresa L. Walunas; Hakon Hakonarson; George Hripcsak; Cong Liu 020; Ning Shang; Chunhua Weng; Nephi Walton; David S. Carrell; et al. (J. Am. Medical Informatics Assoc., 2023)

Clinical decision support with a comprehensive in-EHR patient tracking system improves genetic testing follow up. - Ian M. Campbell; Dean J. Karavite; Morgan L. McManus; Fred C. Cusick; David C. Junod; Sarah E. Sheppard; Eli M. Lourie; Eric D. Shelov; Hakon Hakonarson; Anthony A. Luberti; Naveen Muthu; Robert W. Grundmeier (J. Am. Medical Informatics Assoc., 2023)