Causation, prediction, and search P Spirtes, CN Glymour, R Scheines, D Heckerman, C Meek, G Cooper, ... MIT press, 2000 | 7135 | 2000 |

Empirical analysis of predictive algorithms for collaborative filtering JS Breese, D Heckerman, C Kadie Proceedings of the Fourteenth conference on Uncertainty in artificial …, 1998 | 6833 | 1998 |

Learning Bayesian networks: The combination of knowledge and statistical data D Heckerman, D Geiger, DM Chickering Machine learning 20 (3), 197-243, 1995 | 4535 | 1995 |

A tutorial on learning with Bayesian networks D Heckerman Innovations in Bayesian networks, 33-82, 2008 | 4175 | 2008 |

A hexanucleotide repeat expansion in C9ORF72 is the cause of chromosome 9p21-linked ALS-FTD AE Renton, E Majounie, A Waite, J Simón-Sánchez, S Rollinson, ... Neuron 72 (2), 257-268, 2011 | 2879 | 2011 |

Inductive learning algorithms and representations for text categorization S Dumais, J Platt, D Heckerman, M Sahami | 2129 | 1998 |

A Bayesian approach to filtering junk e-mail M Sahami, S Dumais, D Heckerman, E Horvitz Learning for Text Categorization: Papers from the 1998 workshop 62, 98-105, 1998 | 2107 | 1998 |

Introduction to statistical relational learning D Koller, N Friedman, S Džeroski, C Sutton, A McCallum, A Pfeffer, ... MIT press, 2007 | 1535 | 2007 |

Efficient control of population structure in model organism association mapping HM Kang, NA Zaitlen, CM Wade, A Kirby, D Heckerman, MJ Daly, E Eskin Genetics 178 (3), 1709-1723, 2008 | 1282 | 2008 |

The Lumiere project: Bayesian user modeling for inferring the goals and needs of software users E Horvitz, J Breese, D Heckerman, D Hovel, K Rommelse Proceedings of the Fourteenth conference on Uncertainty in artificial …, 1998 | 1049 | 1998 |

CD8^{+} T-cell responses to different HIV proteins have discordant associations with viral loadP Kiepiela, K Ngumbela, C Thobakgale, D Ramduth, I Honeyborne, ... Nature medicine 13 (1), 46, 2007 | 1030 | 2007 |

Bayesian networks for data mining D Heckerman Data mining and knowledge discovery 1 (1), 79-119, 1997 | 987 | 1997 |

Technique which utilizes a probabilistic classifier to detect" junk" e-mail by automatically updating a training and re-training the classifier based on the updated training set E Horvitz, DE Heckerman, ST Dumais, M Sahami, JC Platt US Patent 6,161,130, 2000 | 955 | 2000 |

Intelligent user assistance facility E Horvitz, JS Breese, DE Heckerman, SD Hobson, DO Hovel, AC Klein, ... US Patent 6,021,403, 2000 | 802 | 2000 |

FaST linear mixed models for genome-wide association studies C Lippert, J Listgarten, Y Liu, CM Kadie, RI Davidson, D Heckerman Nature methods 8 (10), 833, 2011 | 698 | 2011 |

Dependency networks for inference, collaborative filtering, and data visualization D Heckerman, DM Chickering, C Meek, R Rounthwaite, C Kadie Journal of Machine Learning Research 1 (Oct), 49-75, 2000 | 656 | 2000 |

Bayesian factor regression models in the “large p, small n” paradigm JM Bernardo, MJ Bayarri, JO Berger, AP Dawid, D Heckerman, ... Bayesian statistics 7, 733-742, 2003 | 614 | 2003 |

Large-sample learning of Bayesian networks is NP-hard DM Chickering, D Heckerman, C Meek Journal of Machine Learning Research 5 (Oct), 1287-1330, 2004 | 594 | 2004 |

An MDP-based recommender system G Shani, D Heckerman, RI Brafman Journal of Machine Learning Research 6 (Sep), 1265-1295, 2005 | 581 | 2005 |

Probabilistic interpretations for MYCIN's certainty factors D Heckerman Machine Intelligence and Pattern Recognition 4, 167-196, 1986 | 547 | 1986 |