Peer-Reviewed


Peer-Reviewed Journal Publications
  1. V. Chandrashekhar, D. J. Tward, D. Crowley, A. K. Crow, M. A. Wright, B. Y. Hsueh, F. Gore, T. A. Machado, A. Branch, J. S. Rosenblum, K. Deisseroth, and J. T. Vogelstein. CloudReg: automatic terabyte-scale cross-modal brain volume registration. Nature Methods, 2021.
  2. M. Powell, A. Koenecke, J. Byrd, A. Nishimura, M. Konig, R. Xiong, S. Mahmood, V. B. Mucaj, L. Rose, S. Tamang, A. Sacarny, B. Caffo, S. Athey, E. Stuart, and J. Vogelstein. Ten Rules for Conducting Retrospective Pharmacoepidemiological Analyses: Example COVID-19 Study. Frontiers in Pharmacology, 2021.
  3. T. L. Athey, J. Teneggi, J. T. Vogelstein, D. Tward, U. Mueller, and M. I. Miller. Fitting Splines to Axonal Arbors Quantifies Relationship between Branch Order and Geometry. Frontiers in Neuroinformatics, 2021.
  4. A. Koenecke, M. Powell, R. Xiong, Z. Shen, N. Fischer, S. Huq, A. M. Khalafallah, M. Trevisan, P. Sparen, J. J. Carrero, A. Nishimura, B. Caffo, E. A. Stuart, R. Bai, V. Staedtke, D. L. Thomas, N. Papadopoulos, K. W. Kinzler, B. Vogelstein, S. Zhou, C. Bettegowda, M. F. Konig, B. Mensh, J. T. Vogelstein, and S. Athey. Alpha-1 adrenergic receptor antagonists to prevent hyperinflammation and death from lower respiratory tract infection. Elife, 2021.
  5. C. Shen, S. Panda, and J. T. Vogelstein. The Chi-Square Test of Distance Correlation. Journal of Computational and Graphical Statistics, (ja)0:1-21, 2021.
  6. Ronan Perry, Gavin Mischler, Richard Guo, Theodore Lee, Alexander Chang, Arman Koul, Cameron Franz, Hugo Richard, Iain Carmichael, Pierre Ablin, Alexandre Gramfort, and Joshua T. Vogelstein. mvlearn: Multiview Machine Learning in Python. Journal of Machine Learning Research, (109)22:1-7, 2021.
  7. S. Wang, J. Arroyo, J. T. Vogelstein, and C. E. Priebe. Joint Embedding of Graphs. Transactions on Pattern Analysis and Machine Intelligence, 2021.
  8. L. Rose, L. Graham, A. Koenecke, M. Powell, R. Xiong, Z. Shen, K. W. Kinzler, C. Bettegowda, B. Vogelstein, M. F. Konig, S. Athey, J. T. Vogelstein, and T. H. Wagner. The association between Alpha-1 adrenergic receptor antagonists and in-hospital mortality from COVID-19. Frontiers in Medicine, 2021.
  9. M. P. Milham, J. T. Vogelstein, and T. Xu. Removing the Reliability Bottleneck in Functional Magnetic Resonance Imaging Research to Achieve Clinical Utility. JAMA Psychiatry, 2021.
  10. J. Arroyo, A. Athreya, J. Cape, G. Chen, C. E. Priebe, and J. T. Vogelstein. Inference for Multiple Heterogenous Networks with a Common Invariant Subspace. Journal of Machine Learning Research, (142)22:1-49, 2021.
  11. S. Hong, T. Xu, A. Nikolaidis, J. Smallwood, D. S. Margulies, B. Bernhardt, J. T. Vogelstein, and M. P. Milham. Toward a connectivity gradient-based framework for reproducible biomarker discovery. NeuroImage, 2020.
  12. Ting Xu, Karl-Heinz Nenning, Ernst Schwartz, Seok-Jun Hong, Joshua T. Vogelstein, Alexandros Goulas, Damien A. Fair, Charles E. Schroeder, Daniel S. Margulies, Jonny Smallwood, Michael P. Milham, and Georg Langs. Cross-species functional alignment reveals evolutionary hierarchy within the connectome. NeuroImage, 2020.
  13. J. W. Chow, A. Korchmaros, J. T. Vogelstein, M. P. Milham, and T. Xu. Impact of concatenating fMRI data on reliability for functional connectomics. Neuroimage, 2020.
  14. Karl-Heinz Nenning, Ting Xu, Ernst Schwartz, Jesus Arroyo, Adelheid Woehrer, Alexandre R. Franco, Joshua T. Vogelstein, Daniel S. Margulies, Hesheng Liu, Jonathan Smallwood, Michael P. Milham, and Georg Langs. Joint embedding: A scalable alignment to compare individuals in a connectivity space. NeuroImage, 2020.
  15. N. Wang, R. J. Anderson, D. G. Ashbrook, V. Gopalakrishnan, Y. Park, C. E. Priebe, Y. Qi, J. T. Vogelstein, R. W. Williams, and A. G. Johnson. Variability and heritability of mouse brain structure: Microscopic MRI atlases and connectomes for diverse strains. NeuroImage (Cover Story), 2020.
  16. M. A. Haendel, C. G. Chute, T. D. Bennett, D. A. Eichmann, J. Guinney, W. A. Kibbe, P. R. O. Payne, E. R. Pfaff, P. N. Robinson, J. H. Saltz, H. Spratt, C. Suver, J. Wilbanks, A. B. Wilcox, A. E. Williams, C. Wu, C. Blacketer, R. L. Bradford, J. J. Cimino, M. Clark, E. W. Colmenares, P. A. Francis, D. Gabriel, A. Graves, R. Hemadri, S. S. Hong, G. Hripscak, D. Jiao, J. G. Klann, K. Kostka, A. M. Lee, H. P. Lehmann, L. Lingrey, R. T. Miller, M. Morris, S. N. Murphy, K. Natarajan, M. B. Palchuk, U. Sheikh, H. Solbrig, S. Visweswaran, A. Walden, K. M. Walters, G. M. Weber, X. T. Zhang, R. L. Zhu, B. Amor, A. T. Girvin, A. Manna, N. Qureshi, M. G. Kurilla, S. G. Michael, L. M. Portilla, J. L. Rutter, C. P. Austin, and K. R. Gersing. The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment. Journal of the American Medical Informatics Association, 2020.
  17. C. Shen and J. T. Vogelstein. The exact equivalence of distance and kernel methods in hypothesis testing. AStA Advances in Statistical Analysis, 2020.
  18. M. Madhyastha, G. Li, V. Strnadov-Neeley, J. Browne, J. T. Vogelstein, R. Burns, and C. E. Priebe. Geodesic Forests. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2020.
  19. M. Schulz, B. T. Yeo, J. T. Vogelstein, J. Mourao-Miranda, J. N. Kather, K. Kording, B. Richards, and D. Bzdok. Different scalling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets. Nat Commun, 2020.
  20. M. Schulz, B. T. Yeo, J. T. Vogelstein, J. Mourao-Miranada, J. N. Kather, K. Kording, B. Richards, and D. Bzdok. Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets. Nat Commun, 2020.
  21. Z. Wang, H. Sair, C. Crainiceanu, M. Lindquist, B. A. Landman, S. Resnick, J. T. Vogelstein, and B. S. Caffo. On statistical tests of functional connectome fingerprinting. The Canadian Journal of Statistics, 2020.
  22. A. S. Charles, B. Falk, N. Turner, T. D. Pereira, D. Tward, B. D. Pedigo, J. Chung, R. Burns, S. S. Ghosh, J. M. Kebschull, W. Silversmith, and J. T. Vogelstein. Toward Community-Driven Big Open Brain Science: Open Big Data and Tools for Structure, Function, and Genetics. Annual Review of Neuroscience, (1)43:441-464, 2020.
  23. Maximilian F. Konig, Mike Powell, Verena Staedtke, Ren-Yuan Bai, David L. Thomas, Nicole Fischer, Sakibul Huq, Adham M. Khalafallah, Allison Koenecke, Ruoxuan Xiong, Brett Mensh, Nickolas Papadopoulos, Kenneth W. Kinzler, Bert Vogelstein, Joshua T. Vogelstein, Susan Athey, Shibin Zhou, and Chetan Bettegowda. Preventing cytokine storm syndrome in COVID-19 using alpha-1 adrenergic receptor antagonists. The Journal of Clinical Investigation, (7)130:3345-3347, 2020.
  24. G. Franca, M. Rizzo, and J. T. Vogelstein. Kernel k-Groups via Hartigan's Method. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020.
  25. T. M. Tomita, J. Browne, C. Shen, J. Chung, J. L. Patsolic, B. Falk, J. Yim, C. E. Priebe, R. Burns, M. Maggioni, and J. T. Vogelstein. Sparse Projection Oblique Randomer Forests. Journal of Machine Learninig Research, 2020.
  26. S. Hong, J. T. Vogelstein, A. Gozzi, B. C. Bernhardt, B. T. Yeo, M. P. Milham, and A. D. Martino. Toward Neurosubtypes in Autism. Biological Psychiatry, (1)88:111 - 128, 2020.
  27. A. Nikolaidis, A. S. Heinsfeld, T. Xu, P. Bellec, J. T. Vogelstein, and M. Milham. Bagging Improves Reproducibility of Functional Parcellation of the Human Brain. NeuroImage, 2020.
  28. Y. Lee, C. Shen, C. E. Priebe, and J. T. Vogelstein. Network dependence testing via diffusion maps and distance-based correlations. Biometrika, 2019.
  29. J. Chung, B. D. Pedigo, E. W. Bridgeford, B. K. Varjavand, and J. T. Vogelstein. GraSPy: Graph Statistics in Python. Journal of Machine Learning Research, (158)20:1-7, 2019.
  30. J. T. Vogelstein, E. W. Bridgeford, B. D. Pedigo, J. Chung, K. Levin, B. Mensh, and C. E. Priebe. Connectal Coding: Discovering the Structures Linking Cognitive Phenotypes to Individual Histories. Current Opinion in Neurobiology, 2019.
  31. C. E. Priebe, Y. Park, J. T. Vogelstein, J. M. Conroy, V. Lyzinski, M. Tang, A. Athreya, J. Cape, and E. Bridgeford. On a two-truths phenomenon in spectral graph clustering. Proceedings of the National Academy of Sciences of the United States of America, (13)116:5995-6000, 2019.
  32. J. J. Son, J. C. Clucas, C. White, A. Krishnakumar, J. T. Vogelstein, M. P. Milham, and A. Klein. Thermal sensors improve wrist-worn position tracking. npj digital medicine, 2019.
  33. J. T. Vogelstein, E. W. Bridgeford, Q. Wang, C. E. Priebe, M. Maggioni, and C. Shen. Discovering and deciphering relationships across disparate data modalities. eLife, 2019.
  34. R. Tang, M. Ketcha, A. Badea, E. D. Calabrese, D. S. Margulies, J. T. Vogelstein, C. E. Priebe, and D. L. Sussman. Connectome Smoothing via Low-rank Approximations. Transactions in Medical Imaging, 2018.
  35. C. Shen, C. E. Priebe, and J. T. Vogelstein. From Distance Correlation to Multiscale Graph Correlation. Journal of the American Statistical Association, 2018.
  36. J. T. Vogelstein, E. Perlman, B. Falk, A. Baden, W. Gray Roncal, V. Chandrashekhar, F. Collman, S. Seshamani, J. L. Patsolic, K. Lillaney, M. Kazhdan, R. Hider, D. Pryor, J. Matelsky, T. Gion, P. Manavalan, B. Wester, M. Chevillet, E. T. Trautman, K. Khairy, E. Bridgeford, D. M. Kleissas, D. J. Tward, A. K. Crow, B. Hsueh, M. A. Wright, M. I. Miller, S. J. Smith, R. J. Vogelstein, K. Deisseroth, and R. Burns. A Community-Developed Open-Source Computational Ecosystem for Big Neuro Data. Nature Methods, (11)15:846-847, 2018.
  37. A. Athreya, D. E. Fishkind, M. Tang, C. E. Priebe, Y. Park, J. T. Vogelstein, K. Levin, V. Lyzinski, Y. Qin, and D. L. Sussman. Statistical Inference on Random Dot Product Graphs: a Survey. Journal of Machine Learning Research, 2018.
  38. J. D. Cohen, L. Li, Y. Wang, C. Thoburn, B. Afsari, L. Danilova, C. Douville, A. A. Javed, F. Wong, A. Mattox, R. H. Hruban, C. L. Wolfgang, M. G. Goggins, M. D. Molin, T. L. Wang, R. Roden, A. P. Klein, J. Ptak, L. Dobbyn, J. Schaefer, N. Silliman, M. Popoli, J. T. Vogelstein, J. D. Browne, R. E. Schoen, R. E. Brand, J. Tie, P. Gibbs, H. L. Wong, A. S. Mansfield, J. Jen, S. M. Hanash, M. Falconi, P. J. Allen, S. Zhou, C. Bettegowda, L. A. Diaz, C. Tomasetti, K. W. Kinzler, B. Vogelstein, A. M. Lennon, and N. Papadopoulos. Detection and localization of surgically resectable cancers with a multi-analyte blood test. Science, (6378)359:926-930, 2018.
  39. E. L. Dyer, W. G. Roncal, H. L. Fernandes, D. Gürsoy, V. De Andrade, R. Vescovi, K. Fezzaa, X. Xiao, J. T. Vogelstein, C. Jacobsen, K. P. Körding, and N. Kasthuri. Quantifying Mesoscale Neuroanatomy Using X-Ray Microtomography. eNeuro, 2017.
  40. D. Durante, D. B. Dunson, and J. T. Vogelstein. Rejoinder: Nonparametric Bayes Modeling of Populations of Networks. Journal of the American Statistical Association, 2017.
  41. D. Durante, D. B. Dunson, and J. T. Vogelstein. Nonparametric Bayes Modeling of Populations of Networks. Journal of the American Statistical Association, (520)112:1516-1530, 2017.
  42. G. Kiar, K. J. Gorgolewski, D. Kleissas, W. G. Roncal, B. Litt, B. Wandell, R. A. Poldrack, M. Wiener, R. J. Vogelstein, R. Burns, and J. T. Vogelstein. Science in the cloud (SIC): A use case in MRI connectomics. GigaScience, (5)6:1-10, 2017.
  43. N. Binkiewicz, J. T. Vogelstein, and K. Rohe. Covariate-assisted spectral clustering. Biometrika, (2)104:361-377, 2017.
  44. S. Chen, K. Liu, Y. Yang, Y. Xu, S. Lee, M. Lindquist, B. S. Caffo, and J. T. Vogelstein. An M-estimator for reduced-rank system identification. Pattern Recognition Letters, 2017.
  45. D. G. C. Hildebrand, M. Cicconet, R. M. Torres, W. Choi, T. M. Quan, J. Moon, A. W. Wetzel, A. Scott Champion, B. J. Graham, O. Randlett, G. S. Plummer, R. Portugues, I. H. Bianco, S. Saalfeld, A. D. Baden, K. Lillaney, R. Burns, J. T. Vogelstein, A. F. Schier, W. C. A. Lee, W. K. Jeong, J. W. Lichtman, and F. Engert. Whole-brain serial-section electron microscopy in larval zebrafish. Nature, (7654)545:345-349, 2017.
  46. C. Shen, J. T. Vogelstein, and C. E. Priebe. Manifold matching using shortest-path distance and joint neighborhood selection. Pattern Recognition Letters, 2017.
  47. A. K. Simhal, C. Aguerrebere, F. Collman, J. T. Vogelstein, K. D. Micheva, R. J. Weinberg, S. J. Smith, and G. Sapiro. Probabilistic fluorescence-based synapse detection. PLoS Computational Biology, 2017.
  48. Q. Wang, M. Zhang, T. Tomita, J. T. Vogelstein, S. Zhou, N. Papadopoulos, K. W. Kinzler, and B. Vogelstein. Selected reaction monitoring approach for validating peptide biomarkers. Proceedings of the National Academy of Sciences of the United States of America, (51)114:13519-13524, 2017.
  49. D. Zheng, D. Mhembere, V. Lyzinski, J. T. Vogelstein, C. E. Priebe, and R. Burns. Semi-external memory sparse matrix multiplication for billion-node graphs. IEEE Transactions on Parallel and Distributed Systems, (5)28:1470-1483, 2017.
  50. L. Chen, C. Shen, J. T. Vogelstein, and C. E. Priebe. Robust Vertex Classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, (3)38:578-590, 2016.
  51. D. Koutra, N. Shah, J. T. Vogelstein, B. Gallagher, and C. Faloutsos. DeltaCon: Principled Massive-Graph Similarity Function with Attribution. ACM Transactions on Knowledge Discovery from Data, 2016.
  52. V. Lyzinski, D. E. Fishkind, M. Fiori, J. T. Vogelstein, C. E. Priebe, and G. Sapiro. Graph Matching: Relax at Your Own Risk. IEEE Transactions on Pattern Analysis and Machine Intelligence, (1)38:60-73, 2016.
  53. R. D. Airan, J. T. Vogelstein, J. J. Pillai, B. Caffo, J. J. Pekar, and H. I. Sair. Factors affecting characterization and localization of interindividual differences in functional connectivity using MRI. Human Brain Mapping, (5)37:1986-1997, 2016.
  54. C. E. Priebe, D. L. Sussman, M. Tang, and J. T. Vogelstein. Statistical Inference on Errorfully Observed Graphs. Journal of Computational and Graphical Statistics, (4)24:930-953, 2015.
  55. L. Chen, J. T. Vogelstein, V. Lyzinski, and C. E. Priebe. A Joint Graph Inference Case Study: the C.elegans Chemical and Electrical Connectomes. Worm, 2015.
  56. K. M. Harris, J. Spacek, M. E. Bell, P. H. Parker, L. F. Lindsey, A. D. Baden, J. T. Vogelstein, and R. Burns. A resource from 3D electron microscopy of hippocampal neuropil for user training and tool development. Scientific Data, 2015.
  57. N. Kasthuri, K. J. Hayworth, D. R. Berger, R. L. Schalek, J. A. Conchello, S. Knowles-Barley, D. Lee, A. Vázquez-Reina, V. Kaynig, T. R. Jones, M. Roberts, J. L. Morgan, J. C. Tapia, H. S. Seung, W. G. Roncal, J. T. Vogelstein, R. Burns, D. L. Sussman, C. E. Priebe, H. Pfister, and J. W. Lichtman. Saturated Reconstruction of a Volume of Neocortex. Cell, (3)162:648-661, 2015.
  58. V. Lyzinski, D. L. Sussman, D. E. Fishkind, H. Pao, L. Chen, J. T. Vogelstein, Y. Park, and C. E. Priebe. Spectral clustering for divide-and-conquer graph matching. Parallel Computing, 2015.
  59. J. T. Vogelstein, J. M. Conroy, V. Lyzinski, L. J. Podrazik, S. G. Kratzer, E. T. Harley, D. E. Fishkind, R. J. Vogelstein, and C. E. Priebe. Fast Approximate Quadratic programming for graph matching. PLoS ONE, 2015.
  60. J. T. Vogelstein and C. E. Priebe. Shuffled Graph Classification: Theory and Connectome Applications. Journal of Classification, (1)32:3-20, 2015.
  61. W. R. Gray Roncal, D. M. Kleissas, J. T. Vogelstein, P. Manavalan, K. Lillaney, M. Pekala, R. Burns, R. J. Vogelstein, C. E. Priebe, M. A. Chevillet, and G. D. Hager. An automated images-to-graphs framework for high resolution connectomics. Frontiers in Neuroinformatics, 2015.
  62. D. E. Carlson, J. T. Vogelstein, Q. Wu, W. Lian, M. Zhou, C. R. Stoetzner, D. Kipke, D. Weber, D. B. Dunson, and L. Carin. Multichannel Electrophysiological Spike Sorting via Joint Dictionary Learning and Mixture Modeling. IEEE Transactions on Biomedical Engineering, (1)61:41-54, 2014.
  63. E. M. Sweeney, J. T. Vogelstein, J. L. Cuzzocreo, P. A. Calabresi, D. S. Reich, C. M. Crainiceanu, and R. T. Shinohara. A comparison of supervised machine learning algorithms and feature vectors for MS lesion segmentation using multimodal structural MRI. PLoS ONE, 2014.
  64. J. T. Vogelstein, Y. Park, T. Ohyama, R. A. Kerr, J. W. Truman, C. E. Priebe, and M. Zlatic. Discovery of brainwide neural-behavioral maps via multiscale unsupervised structure learning. Science, (6182)344:386-392, 2014.
  65. N. C. Weiler, F. Collman, J. T. Vogelstein, R. Burns, and S. J. Smith. Synaptic molecular imaging in spared and deprived columns of mouse barrel cortex with array tomography. Scientific Data, 2014.
  66. R. C. Craddock, S. Jbabdi, C. G. Yan, J. T. Vogelstein, F. X. Castellanos, A. Di Martino, C. Kelly, K. Heberlein, S. Colcombe, and M. P. Milham. Imaging human connectomes at the macroscale. Nature Methods, (6)10:524-539, 2013.
  67. C. E. Priebe, J. Vogelstein, and D. Bock. Optimizing the quantity/quality trade-off in connectome inference. Communications in Statistics - Theory and Methods, (19)42:3455-3462, 2013.
  68. J. T. Vogelstein, W. G. Roncal, R. J. Vogelstein, and C. E. Priebe. Graph classification using signal-subgraphs: Applications in statistical connectomics. IEEE Transactions on Pattern Analysis and Machine Intelligence, (7)35:1539-1551, 2013.
  69. D. Dai, H. He, J. T. Vogelstein, and Z. Hou. Accurate prediction of AD patients using cortical thickness networks. Machine Vision and Applications, (7)24:1445-1457, 2012.
  70. D. E. Fishkind, D. L. Sussman, M. Tang, J. T. Vogelstein, and C. E. Priebe. Consistent adjacency-spectral partitioning for the stochastic block model when the model parameters are unknown. SIAM Journal on Matrix Analysis and Applications, (1)34:23-39, 2012.
  71. W. R. Gray, J. A. Bogovic, J. T. Vogelstein, B. A. Landman, J. L. Prince, and R. J. Vogelstein. Magnetic Resonance Connectome Automated Pipeline: An Overview. IEEE Pulse, (2)3:42-48, 2012.
  72. N. J. Roberts, J. T. Vogelstein, G. Parmigiani, K. W. Kinzler, B. Vogelstein, and V. E. Velculescu. The predictive capacity of personal genome sequencing. Science Translational Medicine, 2012.
  73. S. B. Hofer, H. Ko, B. Pichler, J. Vogelstein, H. Ros, H. Zeng, E. Lein, N. A. Lesica, and T. D. Mrsic-Flogel. Differential connectivity and response dynamics of excitatory and inhibitory neurons in visual cortex. Nature Neuroscience, (8)14:1045-1052, 2011.
  74. Y. Mishchencko, J. T. Vogelstein, and L. Paninski. A Bayesian approach for inferring neuronal conectivity from calcium fluorescent imaging data. The annals of applied statistics, 2011.
  75. J. T. Vogelstein, R. J. Vogelstein, and C. E. Priebe. Are mental properties supervenient on brain properties? Scientific Reports, 2011.
  76. L. Paninski, Y. Ahmadian, D. G. Ferreira, S. Koyama, K. Rahnama Rad, M. Vidne, J. Vogelstein, and W. Wu. A new look at state-space models for neural data. Journal of Computational Neuroscience, (1-2)29:107-126, 2009.
  77. J. T. Vogelstein, A. M. Packer, T. A. Machado, T. Sippy, B. Babadi, R. Yuste, and L. Paninski. Fast non-negative deconvolution for spike train inference from population calcium imaging. Journal of Neurophysiology, 2009.
  78. J. T. Vogelstein, B. O. Watson, A. M. Packer, R. Yuste, B. Jedynak, and L. Paninski. Spike inference from calcium imaging using sequential Monte Carlo methods. Biophysical Journal, (2)97:636-655, 2009.
  79. R. J. Vogelstein, U. Mallik, J. T. Vogelstein, and G. Cauwenberghs. Dynamically reconfigurable silicon array of spiking neurons with conductance-based synapses. IEEE Transactions on Neural Networks, (1)18:253-265, 2007.
  80. J. T. Vogelstein, L. H. Snyder, and D. E. Angelaki. Accuracy of saccades to remembered targets as a function of body orientation in space. Journal of Neurophysiology, (1)90:521-524, 2003.
  81. D. L. Greenspan, D. C. Connolly, R. Wu, R. Y. Lei, J. T. Vogelstein, Y. T. Kim, J. E. Mok, N. Muñoz, F. X. Bosch, K. Shah, and K. R. Cho. Loss of FHIT expression in cervical carcinoma cell lines and primary tumors. Cancer Research, 1997.