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Showing 13 to 24 of 53 entries
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A conceptual model for translating omic data into clinical action.

Journal of pathology informatics

Herr TM, Bielinski SJ, Bottinger E, Brautbar A, Brilliant M, Chute CG, Denny J, Freimuth RR, Hartzler A, Kannry J, Kohane IS, Kullo IJ, Lin S, Pathak J, Peissig P, Pulley J, Ralston J, Rasmussen L, Roden D, Tromp G, Williams MS, Starren J.
PMID: 26430534
J Pathol Inform. 2015 Aug 31;6:46. doi: 10.4103/2153-3539.163985. eCollection 2015.

Genomic, proteomic, epigenomic, and other "omic" data have the potential to enable precision medicine, also commonly referred to as personalized medicine. The volume and complexity of omic data are rapidly overwhelming human cognitive capacity, requiring innovative approaches to translate...

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Herr TM, Bielinski SJ, Bottinger E, et al. A conceptual model for translating omic data into clinical action. J Pathol Inform. 2015;6:46doi: 10.4103/2153-3539.163985.
Herr, T. M., Bielinski, S. J., Bottinger, E., Brautbar, A., Brilliant, M., Chute, C. G., Denny, J., Freimuth, R. R., Hartzler, A., Kannry, J., Kohane, I. S., Kullo, I. J., Lin, S., Pathak, J., Peissig, P., Pulley, J., Ralston, J., Rasmussen, L., Roden, D., Tromp, G., Williams, M. S., & Starren, J. (2015). A conceptual model for translating omic data into clinical action. Journal of pathology informatics, 646. https://doi.org/10.4103/2153-3539.163985
Herr, Timothy M, et al. "A conceptual model for translating omic data into clinical action." Journal of pathology informatics vol. 6 (2015): 46. doi: https://doi.org/10.4103/2153-3539.163985
Herr TM, Bielinski SJ, Bottinger E, Brautbar A, Brilliant M, Chute CG, Denny J, Freimuth RR, Hartzler A, Kannry J, Kohane IS, Kullo IJ, Lin S, Pathak J, Peissig P, Pulley J, Ralston J, Rasmussen L, Roden D, Tromp G, Williams MS, Starren J. A conceptual model for translating omic data into clinical action. J Pathol Inform. 2015 Aug 31;6:46. doi: 10.4103/2153-3539.163985. eCollection 2015. PMID: 26430534; PMCID: PMC4584438.
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Computational Drug Repositioning Using Continuous Self-Controlled Case Series.

KDD : proceedings. International Conference on Knowledge Discovery & Data Mining

Kuang Z, Thomson J, Caldwell M, Peissig P, Stewart R, Page D.
PMID: 28316874
KDD. 2016 Aug;2016:491-500. doi: 10.1145/2939672.2939715.

Computational Drug Repositioning (CDR) is the task of discovering potential new indications for existing drugs by mining large-scale heterogeneous drug-related data sources. Leveraging the patient-level temporal ordering information between numeric physiological measurements and various drug prescriptions provided in Electronic...

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Kuang Z, Thomson J, Caldwell M, et al. Computational Drug Repositioning Using Continuous Self-Controlled Case Series. KDD. 2016;2016:491-500doi: 10.1145/2939672.2939715.
Kuang, Z., Thomson, J., Caldwell, M., Peissig, P., Stewart, R., & Page, D. (2016). Computational Drug Repositioning Using Continuous Self-Controlled Case Series. KDD : proceedings. International Conference on Knowledge Discovery & Data Mining, 2016491-500. https://doi.org/10.1145/2939672.2939715
Kuang, Zhaobin, et al. "Computational Drug Repositioning Using Continuous Self-Controlled Case Series." KDD : proceedings. International Conference on Knowledge Discovery & Data Mining vol. 2016 (2016): 491-500. doi: https://doi.org/10.1145/2939672.2939715
Kuang Z, Thomson J, Caldwell M, Peissig P, Stewart R, Page D. Computational Drug Repositioning Using Continuous Self-Controlled Case Series. KDD. 2016 Aug;2016:491-500. doi: 10.1145/2939672.2939715. PMID: 28316874; PMCID: PMC5351812.
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New genetic variants improve personalized breast cancer diagnosis.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science

Liu J, Page D, Peissig P, McCarty C, Onitilo AA, Trentham-Dietz A, Burnside E.
PMID: 25717406
AMIA Jt Summits Transl Sci Proc. 2014 Apr 07;2014:83-9. eCollection 2014.

Recent large-scale genome-wide association studies (GWAS) have identified a number of new genetic variants associated with breast cancer. However, the degree to which these genetic variants improve breast cancer diagnosis in concert with mammography remains unknown. We conducted a...

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Liu J, Page D, Peissig P, et al. New genetic variants improve personalized breast cancer diagnosis. AMIA Jt Summits Transl Sci Proc. 2014;2014:83-9
Liu, J., Page, D., Peissig, P., McCarty, C., Onitilo, A. A., Trentham-Dietz, A., & Burnside, E. (2014). New genetic variants improve personalized breast cancer diagnosis. AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 201483-9.
Liu, Jie, et al. "New genetic variants improve personalized breast cancer diagnosis." AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science vol. 2014 (2014): 83-9.
Liu J, Page D, Peissig P, McCarty C, Onitilo AA, Trentham-Dietz A, Burnside E. New genetic variants improve personalized breast cancer diagnosis. AMIA Jt Summits Transl Sci Proc. 2014 Apr 07;2014:83-9. eCollection 2014. PMID: 25717406; PMCID: PMC4333695.
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MCIndoor20000: A fully-labeled image dataset to advance indoor objects detection.

Data in brief

Bashiri FS, LaRose E, Peissig P, Tafti AP.
PMID: 29876376
Data Brief. 2018 Jan 03;17:71-75. doi: 10.1016/j.dib.2017.12.047. eCollection 2018 Apr.

A fully-labeled image dataset provides a unique resource for reproducible research inquiries and data analyses in several computational fields, such as computer vision, machine learning and deep learning machine intelligence. With the present contribution, a large-scale fully-labeled image dataset...

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Bashiri FS, LaRose E, Peissig P, et al. MCIndoor20000: A fully-labeled image dataset to advance indoor objects detection. Data Brief. 2018;17:71-75doi: 10.1016/j.dib.2017.12.047.
Bashiri, F. S., LaRose, E., Peissig, P., & Tafti, A. P. (2018). MCIndoor20000: A fully-labeled image dataset to advance indoor objects detection. Data in brief, 1771-75. https://doi.org/10.1016/j.dib.2017.12.047
Bashiri, Fereshteh S, et al. "MCIndoor20000: A fully-labeled image dataset to advance indoor objects detection." Data in brief vol. 17 (2018): 71-75. doi: https://doi.org/10.1016/j.dib.2017.12.047
Bashiri FS, LaRose E, Peissig P, Tafti AP. MCIndoor20000: A fully-labeled image dataset to advance indoor objects detection. Data Brief. 2018 Jan 03;17:71-75. doi: 10.1016/j.dib.2017.12.047. eCollection 2018 Apr. PMID: 29876376; PMCID: PMC5988436.
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Medical records-based chronic kidney disease phenotype for clinical care and "big data" observational and genetic studies.

NPJ digital medicine

Shang N, Khan A, Polubriaginof F, Zanoni F, Mehl K, Fasel D, Drawz PE, Carrol RJ, Denny JC, Hathcock MA, Arruda-Olson AM, Peissig PL, Dart RA, Brilliant MH, Larson EB, Carrell DS, Pendergrass S, Verma SS, Ritchie MD, Benoit B, Gainer VS, Karlson EW, Gordon AS, Jarvik GP, Stanaway IB, Crosslin DR, Mohan S, Ionita-Laza I, Tatonetti NP, Gharavi AG, Hripcsak G, Weng C, Kiryluk K.
PMID: 33850243
NPJ Digit Med. 2021 Apr 13;4(1):70. doi: 10.1038/s41746-021-00428-1.

Chronic Kidney Disease (CKD) represents a slowly progressive disorder that is typically silent until late stages, but early intervention can significantly delay its progression. We designed a portable and scalable electronic CKD phenotype to facilitate early disease recognition and...

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Shang N, Khan A, Polubriaginof F, et al. Medical records-based chronic kidney disease phenotype for clinical care and "big data" observational and genetic studies. NPJ Digit Med. 2021;4(1):70doi: 10.1038/s41746-021-00428-1.
Shang, N., Khan, A., Polubriaginof, F., Zanoni, F., Mehl, K., Fasel, D., Drawz, P. E., Carrol, R. J., Denny, J. C., Hathcock, M. A., Arruda-Olson, A. M., Peissig, P. L., Dart, R. A., Brilliant, M. H., Larson, E. B., Carrell, D. S., Pendergrass, S., Verma, S. S., Ritchie, M. D., Benoit, B., Gainer, V. S., Karlson, E. W., Gordon, A. S., Jarvik, G. P., Stanaway, I. B., Crosslin, D. R., Mohan, S., Ionita-Laza, I., Tatonetti, N. P., Gharavi, A. G., Hripcsak, G., Weng, C., & Kiryluk, K. (2021). Medical records-based chronic kidney disease phenotype for clinical care and "big data" observational and genetic studies. NPJ digital medicine, 4(1), 70. https://doi.org/10.1038/s41746-021-00428-1
Shang, Ning, et al. "Medical records-based chronic kidney disease phenotype for clinical care and "big data" observational and genetic studies." NPJ digital medicine vol. 4,1 (2021): 70. doi: https://doi.org/10.1038/s41746-021-00428-1
Shang N, Khan A, Polubriaginof F, Zanoni F, Mehl K, Fasel D, Drawz PE, Carrol RJ, Denny JC, Hathcock MA, Arruda-Olson AM, Peissig PL, Dart RA, Brilliant MH, Larson EB, Carrell DS, Pendergrass S, Verma SS, Ritchie MD, Benoit B, Gainer VS, Karlson EW, Gordon AS, Jarvik GP, Stanaway IB, Crosslin DR, Mohan S, Ionita-Laza I, Tatonetti NP, Gharavi AG, Hripcsak G, Weng C, Kiryluk K. Medical records-based chronic kidney disease phenotype for clinical care and "big data" observational and genetic studies. NPJ Digit Med. 2021 Apr 13;4(1):70. doi: 10.1038/s41746-021-00428-1. PMID: 33850243; PMCID: PMC8044136.
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Statistical Relational Learning to Predict Primary Myocardial Infarction from Electronic Health Records.

Proceedings of the ... Innovative Applications of Artificial Intelligence Conference. Innovative Applications of Artificial Intelligence Conference

Weiss JC, Page D, Peissig PL, Natarajan S, McCarty C.
PMID: 25360347
Proc Innov Appl Artif Intell Conf. 2012;2012:2341-2347.

Electronic health records (EHRs) are an emerging relational domain with large potential to improve clinical outcomes. We apply two statistical relational learning (SRL) algorithms to the task of predicting primary myocardial infarction. We show that one SRL algorithm, relational...

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Weiss JC, Page D, Peissig PL, et al. Statistical Relational Learning to Predict Primary Myocardial Infarction from Electronic Health Records. Proc Innov Appl Artif Intell Conf. 2012;2012:2341-2347
Weiss, J. C., Page, D., Peissig, P. L., Natarajan, S., & McCarty, C. (2012). Statistical Relational Learning to Predict Primary Myocardial Infarction from Electronic Health Records. Proceedings of the ... Innovative Applications of Artificial Intelligence Conference. Innovative Applications of Artificial Intelligence Conference, 20122341-2347.
Weiss, Jeremy C, et al. "Statistical Relational Learning to Predict Primary Myocardial Infarction from Electronic Health Records." Proceedings of the ... Innovative Applications of Artificial Intelligence Conference. Innovative Applications of Artificial Intelligence Conference vol. 2012 (2012): 2341-2347.
Weiss JC, Page D, Peissig PL, Natarajan S, McCarty C. Statistical Relational Learning to Predict Primary Myocardial Infarction from Electronic Health Records. Proc Innov Appl Artif Intell Conf. 2012;2012:2341-2347. PMID: 25360347; PMCID: PMC4211289.
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Who's Behind the Makeup? The Effects of Varying Levels of Cosmetics Application on Perceptions of Facial Attractiveness, Competence, and Sociosexuality.

Frontiers in psychology

Aguinaldo ER, Peissig JJ.
PMID: 34220632
Front Psychol. 2021 Jun 17;12:661006. doi: 10.3389/fpsyg.2021.661006. eCollection 2021.

Research has demonstrated a positive effect of makeup on facial attractiveness (Cash et al., 1989; Russell, 2003; Etcoff et al., 2011). Makeup has also been found to influence social perceptions (Etcoff et al., 2011; Klatt et al., 2016). While...

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Aguinaldo ER, Peissig JJ. Who's Behind the Makeup? The Effects of Varying Levels of Cosmetics Application on Perceptions of Facial Attractiveness, Competence, and Sociosexuality. Front Psychol. 2021;12:661006doi: 10.3389/fpsyg.2021.661006.
Aguinaldo, E. R., & Peissig, J. J. (2021). Who's Behind the Makeup? The Effects of Varying Levels of Cosmetics Application on Perceptions of Facial Attractiveness, Competence, and Sociosexuality. Frontiers in psychology, 12661006. https://doi.org/10.3389/fpsyg.2021.661006
Aguinaldo, Erick R, and Peissig, Jessie J. "Who's Behind the Makeup? The Effects of Varying Levels of Cosmetics Application on Perceptions of Facial Attractiveness, Competence, and Sociosexuality." Frontiers in psychology vol. 12 (2021): 661006. doi: https://doi.org/10.3389/fpsyg.2021.661006
Aguinaldo ER, Peissig JJ. Who's Behind the Makeup? The Effects of Varying Levels of Cosmetics Application on Perceptions of Facial Attractiveness, Competence, and Sociosexuality. Front Psychol. 2021 Jun 17;12:661006. doi: 10.3389/fpsyg.2021.661006. eCollection 2021. PMID: 34220632; PMCID: PMC8245686.
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A systems approach using Diversity Outbred mice distinguishes the cardiovascular effects and genetics of circulating GDF11 from those of its homolog, myostatin.

G3 (Bethesda, Md.)

Starcher AE, Peissig K, Stanton JB, Churchill GA, Cai D, Maxwell JT, Grider A, Love K, Chen SY, Coleman AE, Strauss E, Pazdro R.
PMID: 34510201
G3 (Bethesda). 2021 Oct 19;11(11). doi: 10.1093/g3journal/jkab293.

Growth differentiation factor 11 (GDF11) is a member of the TGF-β protein family that has been implicated in the development of cardiac hypertrophy. While some studies have suggested that systemic GDF11 protects against cardiomyocyte enlargement and left ventricular wall...

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Starcher AE, Peissig K, Stanton JB, et al. A systems approach using Diversity Outbred mice distinguishes the cardiovascular effects and genetics of circulating GDF11 from those of its homolog, myostatin. G3 (Bethesda). 2021;11(11)doi: 10.1093/g3journal/jkab293.
Starcher, A. E., Peissig, K., Stanton, J. B., Churchill, G. A., Cai, D., Maxwell, J. T., Grider, A., Love, K., Chen, S. Y., Coleman, A. E., Strauss, E., & Pazdro, R. (2021). A systems approach using Diversity Outbred mice distinguishes the cardiovascular effects and genetics of circulating GDF11 from those of its homolog, myostatin. G3 (Bethesda, Md.), 11(11), . https://doi.org/10.1093/g3journal/jkab293
Starcher, Abigail E, et al. "A systems approach using Diversity Outbred mice distinguishes the cardiovascular effects and genetics of circulating GDF11 from those of its homolog, myostatin." G3 (Bethesda, Md.) vol. 11,11 (2021). doi: https://doi.org/10.1093/g3journal/jkab293
Starcher AE, Peissig K, Stanton JB, Churchill GA, Cai D, Maxwell JT, Grider A, Love K, Chen SY, Coleman AE, Strauss E, Pazdro R. A systems approach using Diversity Outbred mice distinguishes the cardiovascular effects and genetics of circulating GDF11 from those of its homolog, myostatin. G3 (Bethesda). 2021 Oct 19;11(11). doi: 10.1093/g3journal/jkab293. PMID: 34510201; PMCID: PMC8527520.
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Developing a clinical utility framework to evaluate prediction models in radiogenomics.

Proceedings of SPIE--the International Society for Optical Engineering

Wu Y, Liu J, Del Rio AM, Page DC, Alagoz O, Peissig P, Onitilo AA, Burnside ES.
PMID: 27095854
Proc SPIE Int Soc Opt Eng. 2015 Feb 21;9416. doi: 10.1117/12.2081954. Epub 2015 Mar 17.

Combining imaging and genetic information to predict disease presence and behavior is being codified into an emerging discipline called "radiogenomics." Optimal evaluation methodologies for radiogenomics techniques have not been established. We aim to develop a clinical decision framework based...

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Wu Y, Liu J, Del Rio AM, et al. Developing a clinical utility framework to evaluate prediction models in radiogenomics. Proc SPIE Int Soc Opt Eng. 2015;9416doi: 10.1117/12.2081954.
Wu, Y., Liu, J., Del Rio, A. M., Page, D. C., Alagoz, O., Peissig, P., Onitilo, A. A., & Burnside, E. S. (2015). Developing a clinical utility framework to evaluate prediction models in radiogenomics. Proceedings of SPIE--the International Society for Optical Engineering, 9416. https://doi.org/10.1117/12.2081954
Wu, Yirong, et al. "Developing a clinical utility framework to evaluate prediction models in radiogenomics." Proceedings of SPIE--the International Society for Optical Engineering vol. 9416 (2015). doi: https://doi.org/10.1117/12.2081954
Wu Y, Liu J, Del Rio AM, Page DC, Alagoz O, Peissig P, Onitilo AA, Burnside ES. Developing a clinical utility framework to evaluate prediction models in radiogenomics. Proc SPIE Int Soc Opt Eng. 2015 Feb 21;9416. doi: 10.1117/12.2081954. Epub 2015 Mar 17. PMID: 27095854; PMCID: PMC4834184.
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Discriminatory power of common genetic variants in personalized breast cancer diagnosis.

Proceedings of SPIE--the International Society for Optical Engineering

Wu Y, Abbey CK, Liu J, Ong I, Peissig P, Onitilo AA, Fan J, Yuan M, Burnside ES.
PMID: 27279675
Proc SPIE Int Soc Opt Eng. 2016 Feb 27;9787. doi: 10.1117/12.2217030. Epub 2016 Mar 24.

Technology advances in genome-wide association studies (GWAS) has engendered optimism that we have entered a new age of precision medicine, in which the risk of breast cancer can be predicted on the basis of a person's genetic variants. The...

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Wu Y, Abbey CK, Liu J, et al. Discriminatory power of common genetic variants in personalized breast cancer diagnosis. Proc SPIE Int Soc Opt Eng. 2016;9787doi: 10.1117/12.2217030.
Wu, Y., Abbey, C. K., Liu, J., Ong, I., Peissig, P., Onitilo, A. A., Fan, J., Yuan, M., & Burnside, E. S. (2016). Discriminatory power of common genetic variants in personalized breast cancer diagnosis. Proceedings of SPIE--the International Society for Optical Engineering, 9787. https://doi.org/10.1117/12.2217030
Wu, Yirong, et al. "Discriminatory power of common genetic variants in personalized breast cancer diagnosis." Proceedings of SPIE--the International Society for Optical Engineering vol. 9787 (2016). doi: https://doi.org/10.1117/12.2217030
Wu Y, Abbey CK, Liu J, Ong I, Peissig P, Onitilo AA, Fan J, Yuan M, Burnside ES. Discriminatory power of common genetic variants in personalized breast cancer diagnosis. Proc SPIE Int Soc Opt Eng. 2016 Feb 27;9787. doi: 10.1117/12.2217030. Epub 2016 Mar 24. PMID: 27279675; PMCID: PMC4894298.
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Age-Specific Effects of Mirror-Muscle Activity on Cross-Limb Adaptations Under Mirror and Non-Mirror Visual Feedback Conditions.

Frontiers in aging neuroscience

Reissig P, Stöckel T, Garry MI, Summers JJ, Hinder MR.
PMID: 26648867
Front Aging Neurosci. 2015 Dec 01;7:222. doi: 10.3389/fnagi.2015.00222. eCollection 2015.

Cross-limb transfer (CLT) describes the observation of bilateral performance gains due to unilateral motor practice. Previous research has suggested that CLT may be reduced, or absent, in older adults, possibly due to age-related structural and functional brain changes. Based...

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Reissig P, Stöckel T, Garry MI, et al. Age-Specific Effects of Mirror-Muscle Activity on Cross-Limb Adaptations Under Mirror and Non-Mirror Visual Feedback Conditions. Front Aging Neurosci. 2015;7:222doi: 10.3389/fnagi.2015.00222.
Reissig, P., Stöckel, T., Garry, M. I., Summers, J. J., & Hinder, M. R. (2015). Age-Specific Effects of Mirror-Muscle Activity on Cross-Limb Adaptations Under Mirror and Non-Mirror Visual Feedback Conditions. Frontiers in aging neuroscience, 7222. https://doi.org/10.3389/fnagi.2015.00222
Reissig, Paola, et al. "Age-Specific Effects of Mirror-Muscle Activity on Cross-Limb Adaptations Under Mirror and Non-Mirror Visual Feedback Conditions." Frontiers in aging neuroscience vol. 7 (2015): 222. doi: https://doi.org/10.3389/fnagi.2015.00222
Reissig P, Stöckel T, Garry MI, Summers JJ, Hinder MR. Age-Specific Effects of Mirror-Muscle Activity on Cross-Limb Adaptations Under Mirror and Non-Mirror Visual Feedback Conditions. Front Aging Neurosci. 2015 Dec 01;7:222. doi: 10.3389/fnagi.2015.00222. eCollection 2015. PMID: 26648867; PMCID: PMC4664666.
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Baseline Regularization for Computational Drug Repositioning with Longitudinal Observational Data.

IJCAI : proceedings of the conference

Kuang Z, Thomson J, Caldwell M, Peissig P, Stewart R, Page D.
PMID: 28392671
IJCAI (U S). 2016 Jul;2016:2521-2528.

Computational Drug Repositioning (CDR) is the knowledge discovery process of finding new indications for existing drugs leveraging heterogeneous drug-related data. Longitudinal observational data such as Electronic Health Records (EHRs) have become an emerging data source for CDR. To address...

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Kuang Z, Thomson J, Caldwell M, et al. Baseline Regularization for Computational Drug Repositioning with Longitudinal Observational Data. IJCAI (U S). 2016;2016:2521-2528
Kuang, Z., Thomson, J., Caldwell, M., Peissig, P., Stewart, R., & Page, D. (2016). Baseline Regularization for Computational Drug Repositioning with Longitudinal Observational Data. IJCAI : proceedings of the conference, 20162521-2528.
Kuang, Zhaobin, et al. "Baseline Regularization for Computational Drug Repositioning with Longitudinal Observational Data." IJCAI : proceedings of the conference vol. 2016 (2016): 2521-2528.
Kuang Z, Thomson J, Caldwell M, Peissig P, Stewart R, Page D. Baseline Regularization for Computational Drug Repositioning with Longitudinal Observational Data. IJCAI (U S). 2016 Jul;2016:2521-2528. PMID: 28392671; PMCID: PMC5382966.
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Showing 13 to 24 of 53 entries