Temporal Aggregation Bias and Inference of Causal Regulatory Networks.

Stephen D. Bay

Stephen D. Bay Lonnie Chrisman Andrew Pohorille Jeff Shrager Temporal Aggregation Bias and Inference of Causal Regulatory Networks. 971-985 2004 11 J. Comput. Biol. 5 https://doi.org/10.1089/cmb.2004.11.971 https://www.wikidata.org/entity/Q81380599 db/journals/jcb/jcb11.html#BayCPS04

Dileep George Kazumi Saito Pat Langley Stephen D. Bay Kevin R. Arrigo Discovering Ecosystem Models from Time-Series Data. 141-152 https://doi.org/10.1007/978-3-540-39644-4_13 2003 conf/dis/2003 Discovery Science db/conf/dis/dis2003.html#GeorgeSLBA03 Kazumi Saito Dileep George Stephen D. Bay Jeff Shrager Inducing Biological Models from Temporal Gene Expression Data. 468-469 https://doi.org/10.1007/978-3-540-39644-4_47 2003 conf/dis/2003 Discovery Science db/conf/dis/dis2003.html#SaitoGBS03 Pat Langley Dileep George Stephen D. Bay Kazumi Saito Robust Induction of Process Models from Time-Series Data. 432-439 2003 conf/icml/2003 ICML db/conf/icml/icml2003.html#LangleyGBS03 http://www.aaai.org/Library/ICML/2003/icml03-058.php Stephen D. Bay Mark Schwabacher Mining distance-based outliers in near linear time with randomization and a simple pruning rule. 29-38 2003 conf/kdd/2003 KDD https://doi.org/10.1145/956750.956758 https://orkg.org/orkg/resource/R193610 db/conf/kdd/kdd2003.html#BayS03 Lonnie Chrisman Pat Langley Stephen D. Bay Incorporating Biological Knowledge into Evaluation of Causal Regulatory Hypotheses. 128-139 2003 conf/psb/2003 Pacific Symposium on Biocomputing http://psb.stanford.edu/psb-online/proceedings/psb03/chrisman.pdf db/conf/psb/psb2003.html#ChrismanLB03

Stephen D. Bay Jeff Shrager Andrew Pohorille Pat Langley Revising regulatory networks: from expression data to linear causal models. 289-297 2002 35 J. Biomed. Informatics 5-6 https://doi.org/10.1016/S1532-0464(03)00031-5 https://www.wikidata.org/entity/Q30824885 db/journals/jbi/jbi35.html#BaySPL02

Kazumi Saito Stephen D. Bay Pat Langley Revising Qualitative Models of Gene Regulation. 59-70 2002 conf/dis/2002 Discovery Science https://doi.org/10.1007/3-540-36182-0_8 db/conf/dis/dis2002.html#SaitoBL02 Stephen D. Bay Daniel G. Shapiro Pat Langley Revising Engineering Models: Combining Computational Discovery with Knowledge. 10-22 2002 conf/ecml/2002 ECML https://doi.org/10.1007/3-540-36755-1_2 db/conf/ecml/ecml2002.html#BaySL02

Stephen D. Bay Michael J. Pazzani Detecting Group Differences: Mining Contrast Sets. 213-246 2001 5 Data Min. Knowl. Discov. 3 db/journals/datamine/datamine5.html#BayP01 https://doi.org/10.1023/A:1011429418057

Stephen D. Bay Multivariate Discretization for Set Mining. 491-512 2001 3 Knowl. Inf. Syst. 4 https://doi.org/10.1007/PL00011680 https://www.wikidata.org/entity/Q55953843 db/journals/kais/kais3.html#Bay01

Stephen D. Bay Dennis F. Kibler Michael J. Pazzani Padhraic Smyth The UCI KDD Archive of Large Data Sets for Data Mining Research and Experimentation. 81-85 2000 2 SIGKDD Explor. 2 https://doi.org/10.1145/380995.381030 https://www.wikidata.org/entity/Q56594470 db/journals/sigkdd/sigkdd2.html#BayKPS00

Stephen D. Bay Michael J. Pazzani Characterizing Model Erros and Differences. 49-56 2000 conf/icml/2000 ICML db/conf/icml/icml2000.html#BayP00 Stephen D. Bay Multivariate discretization of continuous variables for set mining. 315-319 2000 conf/kdd/2000 KDD https://doi.org/10.1145/347090.347159 db/conf/kdd/kdd2000.html#Bay00

Stephen D. Bay Nearest neighbor classification from multiple feature subsets. 191-209 1999 3 Intell. Data Anal. 3 https://doi.org/10.1016/S1088-467X(99)00018-9 db/journals/ida/ida3.html#Bay99

Stephen D. Bay Michael J. Pazzani Detecting Change in Categorical Data: Mining Contrast Sets. 302-306 1999 conf/kdd/1999 KDD https://doi.org/10.1145/312129.312263 db/conf/kdd/kdd99.html#BayP99 Stephen D. Bay Combining Nearest Neighbor Classifiers Through Multiple Feature Subsets. 37-45 1998 conf/icml/1998 ICML db/conf/icml/icml1998.html#Bay98 Kevin R. Arrigo Lonnie Chrisman Dileep George Dennis F. Kibler Pat Langley Michael J. Pazzani Andrew Pohorille Kazumi Saito Mark Schwabacher Daniel G. Shapiro Jeff Shrager Padhraic Smyth