Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Both sides previous revision Previous revision
Next revision
Previous revision
research [2019/03/03 19:35]
shyam [Data mining and causal discovery from biomedical data]
research [2019/09/05 12:05] (current)
shyam [Causal discovery from biomedical data]
Line 1: Line 1:
 ~~NOTOC~~ ~~NOTOC~~
 +
  
 ===== Research ===== ===== Research =====
Line 7: Line 8:
   * Learning electronic medical record (EMR) system and computerized clinical decision support   * Learning electronic medical record (EMR) system and computerized clinical decision support
   * Precision medicine and personalized modeling   * Precision medicine and personalized modeling
-  * Data mining and causal ​discovery from biomedical data +  * Causal ​discovery from biomedical data 
-  * Reuse of EMR data and research ​data warehousing+  * EMR data warehousing
   * Automated visual analytics   * Automated visual analytics
  
Line 54: Line 55:
  
 Publications:​ Publications:​
 +
 +  * Jabbari F, **Visweswaran S**, Cooper GF. {{papers:​2018_instance-specific_bayesian_network_structure_learning.pdf|Instance-specific Bayesian network structure learning}}. In: The 9th International Conference on Probabilistic Graphical Models. 2018 Sep 11 – 14.
  
   * **Visweswaran,​ S**, Ferreira, A, Cooper, GF. {{papers:​2015_personalized_modeling_for_prediction_with_decision-path_models.pdf|Personalized modeling for prediction with decision-path models}}. PLoS One. 2015 Jun 22;​10(6):​e0131022.   * **Visweswaran,​ S**, Ferreira, A, Cooper, GF. {{papers:​2015_personalized_modeling_for_prediction_with_decision-path_models.pdf|Personalized modeling for prediction with decision-path models}}. PLoS One. 2015 Jun 22;​10(6):​e0131022.
Line 70: Line 73:
  
  
-==== Data mining and causal ​discovery from biomedical data ====+==== Causal ​discovery from biomedical data ====
  
 Development of efficient data mining for biomarker discovery from high-dimensional genomic variant data is important for precision medicine and focuses on the use of information obtained from sequences such as whole exomes and whole genomes. Our work focuses on single nucleotide variants (SNVs) data obtained from genome-wide and whole exome data. This work is in collaboration with [[http://​www.dbmi.pitt.edu/​person/​gregory-cooper-md-phd|Gregory F. Cooper]] and [[https://​www.dept-med.pitt.edu/​gi/​faculty_info.aspx/​Whitcomb5013/​|David C. Whitcomb]] (at the University of Pittsburgh). ​ Development of efficient data mining for biomarker discovery from high-dimensional genomic variant data is important for precision medicine and focuses on the use of information obtained from sequences such as whole exomes and whole genomes. Our work focuses on single nucleotide variants (SNVs) data obtained from genome-wide and whole exome data. This work is in collaboration with [[http://​www.dbmi.pitt.edu/​person/​gregory-cooper-md-phd|Gregory F. Cooper]] and [[https://​www.dept-med.pitt.edu/​gi/​faculty_info.aspx/​Whitcomb5013/​|David C. Whitcomb]] (at the University of Pittsburgh). ​
Line 102: Line 105:
   * Strobl, EV, Eack, SM, Swaminathan,​ V, **Visweswaran,​ S**. {{papers:​2012_predicting_the_risk_of_psychosis_onset_advances_and_prospects.pdf|Predicting the risk of psychosis onset: Advances and prospects. Early Intervention in Psychiatry}}. 2012 Nov;​6(4):​368-79.   * Strobl, EV, Eack, SM, Swaminathan,​ V, **Visweswaran,​ S**. {{papers:​2012_predicting_the_risk_of_psychosis_onset_advances_and_prospects.pdf|Predicting the risk of psychosis onset: Advances and prospects. Early Intervention in Psychiatry}}. 2012 Nov;​6(4):​368-79.
  
-  * Wei W, **Visweswaran,​ S**, Cooper, GF. {{papers:​2011_the_application_of_naive_bayes_model_averaging_to_predict_alzheimers_disease_from_genome-wide_data.pdf|The application of naive Bayes model averaging to predict Alzheimer'​s disease from genome-wide data}}. Journal of the American Medical Informatics Association. 2011 Jul-Aug;​18(4):​370-5. 
  
-  * Jiang, X, Neapolitan, RE, Barmada, MM, **Visweswaran,​ S**. {{papers:​2011_learning_genetic_epistasis_using_bayesian_network_scoring_criteria.pdf|Learning genetic epistasis using Bayesian network scoring criteria}}. BMC Bioinformatics. 2011 Mar 31;12:89. 
  
-  * Jiang, X, Barmada, MM, **Visweswaran,​ S**. {{papers:​2010_identifying_genetic_interactions_in_genome-wide_data_using_bayesian_networks.pdf|Identifying genetic interactions in genome-wide data using Bayesian networks}}. Genetic Epidemiology. 2010 Sep; 34(6):​575-81. +==== EMR data warehousing ====
- +
- +
-==== Reuse of EMR data and research ​data warehousing ====+
  
 This work is funded by a [[https://​projectreporter.nih.gov/​project_info_description.cfm?​aid=9260460|UL1 grant from NCATS]], NIH and a [[http://​www.pcornet.org/​clinical-data-research-networks/​cdrn11-university-of-pittsburgh/​|CDRN grant from PCORI]]. This work is funded by a [[https://​projectreporter.nih.gov/​project_info_description.cfm?​aid=9260460|UL1 grant from NCATS]], NIH and a [[http://​www.pcornet.org/​clinical-data-research-networks/​cdrn11-university-of-pittsburgh/​|CDRN grant from PCORI]].
Line 116: Line 114:
  
 Publications:​ Publications:​
 +
 +  * **Visweswaran S**, Becich MJ, D’Itri VS, Sendro ER, MacFadden D, Anderson NR, Allen KA, Ranganathan D, Murphy SN, Morrato EH, Pincus HA, Toto R, Firestein GS, Nadler LM, Reis SE. [[https://​academic.oup.com/​jamiaopen/​article/​1/​2/​147/​5077449|Accrual to Clinical Trials (ACT): A Clinical and Translational Science Award Consortium network]]. JAMIA Open. 2018 Aug 21.
  
   * Amin, W, Borromeo, C, Saul, M, Becich, MJ, **Visweswaran,​ S**. {{papers:​2015_informatics_synergies_between_path_and_act_networks.pdf|Informatics synergies between PaTH and ACT networks}}. In: Proceedings of the 2015 Summit on Clinical Research Informatics (Mar 2015).   * Amin, W, Borromeo, C, Saul, M, Becich, MJ, **Visweswaran,​ S**. {{papers:​2015_informatics_synergies_between_path_and_act_networks.pdf|Informatics synergies between PaTH and ACT networks}}. In: Proceedings of the 2015 Summit on Clinical Research Informatics (Mar 2015).
-  * Visweswaran,​ S, Tenenbaum, J, Gouripeddi, R. Secondary use of data for research - EHR, omics and environmental data. In: AMIA Joint Summits Translational Science Proceedings (Mar 2016).+
  
  
Line 129: Line 129:
  
 Publications:​ Publications:​
 +
 +  * Bhavnani SK, **Visweswaran S**, Divekar R, Brasier A. [[https://​journals.sagepub.com/​doi/​full/​10.1177/​0021886318794606|Towards team-centered informatics:​ Accelerating innovation in multidisciplinary scientific teams through visual analytics]]. The Journal of Applied Behavioral Science. 2019 Mar;​55(1):​50-72.
 +
 +  * Bhavnani SK, Dang B, Kilaru V, Caro M, **Visweswaran S**, Saade G, Smith AK, Menon R. [[http://​dx.doi.org/​10.1515/​jpm-2017-0126|Methylation differences reveal heterogeneity in preterm pathophysiology:​ Results from bipartite network analyses]]. Journal of Perinatal Medicine. 2018 Jul 26;​46(5):​509-521.
  
   * Bhavnani, SK, **Visweswaran,​ S**, Divekar, R, Bellala, G. {{papers:​2015_where_is_the_science_in_big_data_visual_analytics_from_pretty_pictures_to_transformative_biomedical_discoveries.pdf|Where is the science in big data visual analytics? From pretty pictures to transformative biomedical discoveries}}. In: AMIA Joint Summits Translational Science Proceedings. 2015 Mar 23; 2015.    * Bhavnani, SK, **Visweswaran,​ S**, Divekar, R, Bellala, G. {{papers:​2015_where_is_the_science_in_big_data_visual_analytics_from_pretty_pictures_to_transformative_biomedical_discoveries.pdf|Where is the science in big data visual analytics? From pretty pictures to transformative biomedical discoveries}}. In: AMIA Joint Summits Translational Science Proceedings. 2015 Mar 23; 2015. 
Line 138: Line 142:
   * Bhavnani, SK, Dang, B, Caro, M, Bellala, G, **Visweswaran,​ S**, Asuncion, M, Divekar, R. {{papers:​2014_heterogeneity_within_and_across_pediatric_pulmonary_infections_from_bipartite_networks_to_at-risk_subphenotypes.pdf|Heterogeneity within and across pediatric pulmonary infections: From bipartite networks to at-risk subphenotypes}}. In: AMIA Joint Summits Translational Science Proceedings. 2014 Apr 7; 2014:29-34.   * Bhavnani, SK, Dang, B, Caro, M, Bellala, G, **Visweswaran,​ S**, Asuncion, M, Divekar, R. {{papers:​2014_heterogeneity_within_and_across_pediatric_pulmonary_infections_from_bipartite_networks_to_at-risk_subphenotypes.pdf|Heterogeneity within and across pediatric pulmonary infections: From bipartite networks to at-risk subphenotypes}}. In: AMIA Joint Summits Translational Science Proceedings. 2014 Apr 7; 2014:29-34.
  
-  * Bhavnani, SK, Drake, J, Bellala, G, Dang, B, Peng, B, Oteo, JA, Santibañez-Saenz,​ P, **Visweswaran,​ S**, Olano, JP. {{papers:​2013_how_cytokines_co-occur_across_rickettsioses_patients_from_bipartite_visual_analytics_to_mechanistic_inferences_of_a_cytokine_storm.pdf|How cytokines co-occur across rickettsioses patients: From bipartite visual analytics to mechanistic inferences of a cytokine storm}}. In: AMIA Joint Summits Translational Science Proceedings. 2013 Mar 18; 2013:15-9. 
- 
-  * Bhavnani, SK, Bassler, K, **Visweswaran,​ S**. [[http://​www.google.com/​patents/​US20130245959|Computer-Implementable Algorithm for Biomarker Discovery Using Bipartite Networks]]. US Patent Application. 
- 
-  * Bhavnani, SK, Bellala, G, Victor, S, Bassler, K, **Visweswaran,​ S**. {{papers:​2012_the_role_of_complementary_bipartite_visual_analytical_representations_in_the_analysis_of_snps.pdf|The role of complementary bipartite visual analytical representations in the analysis of SNPs: A case study in ancestral informative markers}}. Journal of the American Medical Informatics Association. 2012 Jun 1; 19(e1):​e5-e12.