Hasso-Plattner-Institut
Prof. Dr. Erwin Böttinger
 

Chair Digital Health - Personalized Medicine

2021

  • 1.
    Henkenjohann, R.: Role of Individual Motivations and Privacy Concerns in the Adoption of German Electronic Patient Record Apps—A Mixed-Methods Study. International Journal of Environmental Research and Public Health. 18, 31 (2021).
     
  • 2.
    Hirten, R.P., Danieletto, M., Tomalin, L., Choi, K.H., Zweig, M., Golden, E., Kaur, S., Helmus, D., Biello, A., Pyzik, R., Calcogna, C., Freeman, R., Sands, B.E., Charney, D., Bottinger, E.P., Murrough, J.W., Keefer, L., Suarez-Farinas, M., Nadkarni, G.N., Fayad, Z.A.: Factors Associated with Longitudinal Psychological and Physiological Stress in Health Care Workers During the COVID-19 Pandemic: Observational Study Using Apple Watch Data (Preprint). Journal of Medical Internet Research. (2021).
     
  • 3.
    Ruther, D.F., Sebode, M., Lohse, A.W., Wernicke, S., Bottinger, E., Casar, C., Braun, F., Schramm, C.: Mobile app requirements for patients with rare liver diseases: A single center survey for the ERN RARE-LIVER‬‬‬. Clinics and Research in Hepatology and Gastroenterology. 45, 101760 (2021).
     
  • 4.
    Dellepiane, S., Vaid, A., Jaladanki, S.K., Coca, S., Fayad, Z.A., Charney, A.W., Bottinger, E.P., He, J.C., Glicksberg, B.S., Chan, L., Nadkarni, G.: Acute Kidney Injury in Patients Hospitalized With COVID-19 in New York City: Temporal Trends From March 2020 to April 2021. Kidney Medicine. (2021).
     
  • 5.
    Zenner, A.M., Bottinger, E., Konigorski, S.: StudyMe: A New Mobile App for User-Centric N-of-1 Trials, (2021).
     
  • 6.
    Vaid, A., Chan, L., Chaudhary, K., Jaladanki, S.K., Paranjpe, I., Russak, A., Kia, A., Timsina, P., Levin, M.A., He, J.C., Boettinger, E.P., Charney, A.W., Fayad, Z.A., Coca, S.G., Glicksberg, B.S., Nadkarni, G.N.: Predictive Approaches for Acute Dialysis Requirement and Death in COVID-19. Clinical Journal of the American Society of Nephrology. 16, 1158–1168 (2021).
     
  • 7.
    Ruther, D.F., Sebode, M., Lohse, A.W., Wernicke, S., Boetinger, E., Casar, C., Braun, F., Schramm, C.: Mobile app requirements for patients with rare liver diseases: A single center survey for the ERN RARE-LIVER‬‬‬. Clinics and Research in Hepatology and Gastroenterology. 45, 101760 (2021).
     
  • 8.
    Datta, S., Sachs, J.P., Cruz, H.F., Martensen, T., Bode, P., Sasso, A.M., Glicksberg, B.S., Bottinger, E.: FIBER: enabling flexible retrieval of electronic health records data for clinical predictive modeling. JAMIA Open. 4, (2021).
     
  • 9.
    Klessascheck, F., Lichtenstein, T., Meier, M., Remy, S., Sachs, J.P., Pufahl, L., Miotto, R., Bottinger, E., Weske, M.: Domain-Specific Event Abstraction. Business Information Systems. 117–126 (2021).
     
  • 10.
    Kappattanavar, A.M., Steckhan, N., Sachs, J.P., da Cruz, H.F., Bottinger, E., Arnrich, B.: Monitoring of Sitting Postures With Sensor Networks in Controlled and Free-living Environments: Systematic Review. JMIR Biomedical Engineering. 6, e21105 (2021).
     
  • 11.
    Hirten, R.P., Danieletto, M., Tomalin, L., Choi, K.H., Zweig, M., Golden, E., Kaur, S., Helmus, D., Biello, A., Pyzik, R., Charney, A., Miotto, R., Glicksberg, B.S., Levin, M., Nabeel, I., Aberg, J., Reich, D., Charney, D., Bottinger, E.P., Keefer, L., Suarez-Farinas, M., Nadkarni, G.N., Fayad, Z.A.: Use of Physiological Data From a Wearable Device to Identify SARS-CoV-2 Infection and Symptoms and Predict COVID-19 Diagnosis: Observational Study. Journal of Medical Internet Research. 23, e26107 (2021).
     
  • 12.
    Vaid, A., Chan, L., Chaudhary, K., Jaladanki, S., Paranjpe, I., Russak, A., Kia, A., Timsina, P., Levin, M., He, J., Bottinger, E., Charney, A., Fayad, Z., Coca, S., Glicksberg, B., Nadkarni, G.: Predictive Approaches for Acute Dialysis Requirement and Death in COVID-19. Clinical Journal of the American Society of Nephrology. CJN.17311120 (2021).
     
  • 13.
    Golden, E.A., Zweig, M., Danieletto, M., Landell, K., Nadkarni, G., Bottinger, E., Katz, L., Somarriba, R., Sharma, V., Katz, C.L., Marin, D.B., DePierro, J., Charney, D.S.: A Resilience-Building App to Support the Mental Health of Health Care Workers in the COVID-19 Era: Design Process, Distribution, and Evaluation. JMIR Formative Research. 5, e26590 (2021).
     
  • 14.
    Belbin, G.M., Cullina, S., Wenric, S., Soper, E.R., Glicksberg, B.S., Torre, D., Moscati, A., Wojcik, G.L., Shemirani, R., Beckmann, N.D., Cohain, A., Sorokin, E.P., Park, D.S., Ambite, J.-L., Ellis, S., Auton, A., Bottinger, E.P., Cho, J.H., Loos, R.J., Abul-Husn, N.S., Zaitlen, N.A., Gignoux, C.R., Kenny, E.E.: Toward a fine-scale population health monitoring system. Cell. 184, 2068–2083.e11 (2021).
     
  • 15.
    Gu, X., Yang, H., Sheng, X., Ko, Y.-A., Qiu, C., Park, J., Huang, S., Kember, R., Judy, R.L., Park, J., Damrauer, S.M., Nadkarni, G., Loos, R.J.F., My, V.T.H., Chaudhary, K., Bottinger, E.P., Paranjpe, I., Saha, A., Brown, C., Akilesh, S., Hung, A.M., Palmer, M., Baras, A., Overton, J.D., Reid, J., Ritchie, M., Rader, D.J., Susztak, K.: Kidney disease genetic risk variants alter lysosomal beta-mannosidase (MANBA) expression and disease severity. Science Translational Medicine. 13, eaaz1458 (2021).
     
  • 16.
    Borchert, F., Mock, A., Tomczak, A., Hügel, J., Alkarkoukly, S., Knurr, A., Volckmar, A.-L., Stenzinger, A., Schirmacher, P., Debus, J., Jäger, D., Longerich, T., Fröhling, S., Eils, R., Bougatf, N., Sax, U., Schapranow, M.-P.: Knowledge bases and software support for variant interpretation in precision oncology. Briefings in Bioinformatics. (2021).
     
  • 17.
    Paranjpe, I., Chaudhary, K., Johnson, K.W., Jaladanki, S.K., Zhao, S., Freitas, J.K.D., Pujdas, E., Chaudhry, F., Bottinger, E.P., Levin, M.A., Fayad, Z.A., Charney, A.W., Houldsworth, J., Cordon-Cardo, C., Glicksberg, B.S., Nadkarni, G.N.: Association of SARS-CoV-2 viral load at admission with in-hospital acute kidney injury: A retrospective cohort study. PLOS ONE. 16, e0247366 (2021).
     
  • 18.
    Vaid, A., Jaladanki, S.K., Xu, J., Teng, S., Kumar, A., Lee, S., Somani, S., Paranjpe, I., Freitas, J.K.D., Wanyan, T., Johnson, K.W., Bicak, M., Klang, E., Kwon, Y.J., Costa, A., Zhao, S., Miotto, R., Charney, A.W., Böttinger, E., Fayad, Z.A., Nadkarni, G.N., Wang, F., Glicksberg, B.S.: Federated Learning of Electronic Health Records to Improve Mortality Prediction in Hospitalized Patients With COVID-19: Machine Learning Approach. JMIR Medical Informatics. 9, e24207 (2021).
     
  • 19.
    Dellepiane, S., Vaid, A., Jaladanki, S.K., Paranjpe, I., Coca, S., Fayad, Z.A., Charney, A.W., Bottinger, E.P., He, J.C., Glicksberg, B.S., Chan, L., Nadkarni, G.: Temporal Trends in COVID-19 associated AKI from March to December 2020 in New York City. (2021).
     
  • 20.
    Hackl, M., Datta, S., Miotto, R., Bottinger, E.: Unsupervised Learning to Subphenotype Heart Failure Patients from Electronic Health Records. Artificial Intelligence in Medicine. pp. 219–228. Springer International Publishing (2021).
     

2020

  • 1.
    Somani, S.S., Richter, and F., Fuster, V., Freitas, J.K.D., Naik, N., Sigel, K., Bottinger, E.P., Levin, M.A., Fayad, Z., Just, A.C., Charney, A.W., Zhao, S., Glicksberg, B.S., Lala, A., Nadkarni, G.N.: Characterization of Patients Who Return to Hospital Following Discharge from Hospitalization for COVID-19. Journal of General Internal Medicine. 35, 2838–2844 (2020).
     
  • 2.
    Hirten, R.P., Danieletto, M., Tomalin, L., Choi, K.H., Zweig, M., Golden, E., Kaur, S., Helmus, D., Biello, A., Pyzik, R., Charney, A., Miotto, R., Glicksberg, B.S., Levin, M., Nabeel, I., Aberg, J., Reich, D., Charney, D., Bottinger, E.P., Keefer, L., Suarez-Farinas, M., Nadkarni, G.N., Fayad, Z.A.: Physiological Data from a Wearable Device Identifies SARS-CoV-2 Infection and Symptoms and Predicts COVID-19 Diagnosis: Observational Study (Preprint). Journal of Medical Internet Research. (2020).
     
  • 3.
    da Cruz, H.F., Pfahringer, B., Martensen, T., Schneider, F., Meyer, A., Bottinger, E., Schapranow, M.-P.: Using Interpretability Approaches to Update Black-Box Clinical Prediction Models: an External Validation Study in Nephrology. Artificial Intelligence in Medicine. 101982 (2020).
     
  • 4.
    Freitas, J.K.D., Johnson, K.W., Golden, E., Nadkarni, G.N., Dudley, J.T., Bottinger, E.P., Glicksberg, B.S., Miotto, R.: Phe2vec: Automated Disease Phenotyping based on Unsupervised Embeddings from Electronic Health Records. (2020).
     
  • 5.
    Kappattanavar, A.M., da Cruz, H.F., Arnrich, B., Böttinger, E.: Position Matters: Sensor Placement for SittingPosture Classification. ICHI ’20: Proceedings of the 2020 IEEE International Conference on Healthcare Informatics. IEEE (2020).
     
  • 6.
    Schmitt, F., Sundermeier, J., Bohn, N., Morassi Sasso, A.: Spotlight on Women in Tech: Fostering an Inclusive Workforce when Exploring and Exploiting Digital Innovation Potentials. ICIS Proceedings (2020).
     
  • 7.
    Chan, L., Jaladanki, S.K., Somani, S., Paranjpe, I., Kumar, A., Zhao, S., Kaufman, L., Leisman, S., Sharma, S., He, J.C., Murphy, B., Fayad, Z.A., Levin, M.A., Bottinger, E.P., Charney, A.W., Glicksberg, B.S., Coca, S.G., Nadkarni, G.N.: Outcomes of Patients on Maintenance Dialysis Hospitalized with COVID-19. Clinical Journal of the American Society of Nephrology. 16, 452–455 (2020).
     
  • 8.
    Musmann, F., Sasso, A., Arnrich, B.: ALPS: A Web Platform for Analysing Multimodal Sensor Data in the Context of Digital Health. 2020 IEEE International Conference on Healthcare Informatics (ICHI). pp. 1–12. IEEE Computer Society, Los Alamitos, CA, USA (2020).
     
  • 9.
    Chan, L., Chaudhary, K., Saha, A., Chauhan, K., Vaid, A., Zhao, S., Paranjpe, I., Somani, S., Richter, F., Miotto, R., Lala, A., Kia, A., Timsina, P., Li, L., Freeman, R., Chen, R., Narula, J., Just, A.C., Horowitz, C., Fayad, Z., Cordon-Cardo, C., Schadt, E., Levin, M.A., Reich, D.L., Fuster, V., Murphy, B., He, J.C., Charney, A.W., Böttinger, E.P., Glicksberg, B.S., Coca, S.G., Nadkarni, G.N.: AKI in Hospitalized Patients with COVID-19. Journal of the American Society of Nephrology. 32, 151–160 (2020).
     
  • 10.
    Whiffin, N., Armean, and I.M., Kleinman, A., Marshall, J.L., Minikel, E.V., Goodrich, J.K., Quaife, N.M., Cole, J.B., Wang, Q., Karczewski, K.J., Cummings, B.B., Francioli, L., Laricchia, K., Guan, A., Alipanahi, B., Morrison, P., Baptista, M.A.S., Merchant, K.M., Ware, J.S., Havulinna, A.S., Iliadou, B., Lee, J.-J., Nadkarni, G.N., Whiteman, C., Daly, M., Esko, T., Hultman, C., Loos, R.J.F., Milani, L., Palotie, A., Pato, C., Pato, M., Saleheen, D., Sullivan, P.F., Alföldi, J., Cannon, P., MacArthur, D.G., and: The effect of LRRK2 loss-of-function variants in humans. Nature Medicine. 26, 869–877 (2020).
     
  • 11.
    Vaid, A., Somani, S., Russak, A.J., Freitas, J.K.D., Chaudhry, F.F., Paranjpe, I., Johnson, K.W., Lee, S.J., Miotto, R., Richter, F., Zhao, S., Beckmann, N.D., Naik, N., Kia, A., Timsina, P., Lala, A., Paranjpe, M., Golden, E., Danieletto, M., Singh, M., Meyer, D., OtextquotesingleReilly, P.F., Huckins, L., Kovatch, P., Finkelstein, J., Freeman, R.M., Argulian, E., Kasarskis, A., Percha, B., Aberg, J.A., Bagiella, E., Horowitz, C.R., Murphy, B., Nestler, E.J., Schadt, E.E., Cho, J.H., Cordon-Cardo, C., Fuster, V., Charney, D.S., Reich, D.L., Bottinger, E.P., Levin, M.A., Narula, J., Fayad, Z.A., Just, A.C., Charney, A.W., Nadkarni, G.N., Glicksberg, B.S.: Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation. Journal of Medical Internet Research. 22, e24018 (2020).
     
  • 12.
    Chan, L., Jaladanki, S.K., Somani, S., Paranjpe, I., Kumar, A., Zhao, S., Kaufman, L., Leisman, S., Sharma, S., He, J.C., Murphy, B., Fayad, Z.A., Levin, M.A., Bottinger, E.P., Charney, A.W., Glicksberg, B.S., Coca, S.G., Nadkarni, G.N.: Outcomes of Patients on Maintenance Dialysis Hospitalized with COVID-19. Clinical Journal of the American Society of Nephrology. CJN.12360720 (2020).
     
  • 13.
    Kraus, M., Mathew Stephen, M., Schapranow, M.-P.: Eatomics: Shiny exploration of quantitative proteomics data. Journal of Proteome Research. (2020).
     
  • 14.
    Borchert, F., Lohr, C., Modersohn, L., Langer, T., Follmann, M., Sachs, J.P., Hahn, U., Schapranow, M.-P.: GGPONC: A Corpus of German Medical Text with Rich Metadata Based on Clinical Practice Guidelines. Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis. pp. 38–48. Association for Computational Linguistics, Online (2020).
     
  • 15.
    Sigel, K., Swartz, T., Golden, E., Paranjpe, I., Somani, S., Richter, F., Freitas, J.K.D., Miotto, R., Zhao, S., Polak, P., Mutetwa, T., Factor, S., Mehandru, S., Mullen, M., Cossarini, F., Bottinger, E., Fayad, Z., Merad, M., Gnjatic, S., Aberg, J., Charney, A., Nadkarni, G., Glicksberg, B.S.: Coronavirus 2019 and People Living With Human Immunodeficiency Virus: Outcomes for Hospitalized Patients in New York City. Clinical Infectious Diseases. (2020).
     
  • 16.
    Chaudhary, K., Vaid, A., Duffy, Áine, Paranjpe, I., Jaladanki, S., Paranjpe, M., Johnson, K., Gokhale, A., Pattharanitima, P., Chauhan, K., O’Hagan, R., Vleck, T.V., Coca, S.G., Cooper, R., Glicksberg, B., Bottinger, E.P., Chan, L., Nadkarni, G.N.: Utilization of Deep Learning for Subphenotype Identification in Sepsis-Associated Acute Kidney Injury. Clinical Journal of the American Society of Nephrology. CJN.09330819 (2020).
     
  • 17.
    Slosarek, T., Wohlbrandt, A., Böttinger, E.: Using CEF Digital Service Infrastructures in the Smart4Health Project for the Exchange of Electronic Health Records. arXiv preprint arXiv:2001.01477. (2020).
     
  • 18.
    Paranjpe, I., Russak, A., Freitas, J.K.D., Lala, A., Miotto, R., Vaid, A., Johnson, K.W., Danieletto, M., Golden, E., Meyer, D., Singh, M., Somani, S., Manna, S., Nangia, U., Kapoor, A., OtextquotesingleHagan, R., OtextquotesingleReilly, P.F., Huckins, L.M., Glowe, P., Kia, A., Timsina, P., Freeman, R.M., Levin, M.A., Jhang, J., Firpo, A., Kovatch, P., Finkelstein, J., Aberg, J.A., Bagiella, E., Horowitz, C.R., Murphy, B., Fayad, Z.A., Narula, J., Nestler, E.J., Fuster, V., Cordon-Cardo, C., Charney, D.S., Reich, D.L., Just, A.C., Bottinger, E.P., Charney, A.W., Glicksberg, B.S., Nadkarni, G.: Clinical Characteristics of Hospitalized Covid-19 Patients in New York City. medRxiv. Version 1 (April 23, 2020 – 03:18) (2020).
     
  • 19.
    Paranjpe, I., Chaudhary, K., Paranjpe, M., O’Hagan, R., Manna, S., Jaladanki, S., Kapoor, A., Horowitz, C., DeFelice, N., Cooper, R., Glicksberg, B., Bottinger, E.P., Just, A.C., Nadkarni, G.N.: Association of APOL1 Risk Genotype and Air Pollution for Kidney Disease. Clinical Journal of the American Society of Nephrology. 15, 401–403 (2020).
     
  • 20.
    Remy, S., Pufahl, L., Sachs, J.P., Böttinger, E., Weske, M.: Event Log Generation in a Health System: A Case Study. Lecture Notes in Computer Science. pp. 505–522. Springer International Publishing (2020).
     
  • 21.
    Drimalla, H., Scheffer, T., Landwehr, N., Baskow, I., Roepke, S., Behnia, B., Dziobek, I.: Towards the automatic detection of social biomarkers in autism spectrum disorder: introducing the simulated interaction task (SIT). npj digital medicine. 3, (2020).
     
  • 22.
    Chan, L., Chaudhary, K., Saha, A., Chauhan, K., Vaid, A., Zhao, S., Paranjpe, I., Somani, S., Richter, F., Miotto, R., Lala, A., Kia, A., Timsina, P., Li, L., Freeman, R., Chen, R., Narula, J., Just, A.C., Horowitz, C., Fayad, Z., Cordon-Cardo, C., Schadt, E., Levin, M.A., Reich, D.L., Fuster, V., Murphy, B., He, J.C., Charney, A.W., Böttinger, E.P., Glicksberg, B.S., Coca, S.G., Nadkarni, G.N., Li, L.: AKI in Hospitalized Patients with COVID-19. Journal of the American Society of Nephrology. ASN.2020050615 (2020).
     
  • 23.
    Sasso, A.M., Datta, S., Jeitler, M., Steckhan, N., Kessler, C.S., Michalsen, A., Arnrich, B., Boettinger, E.: HYPE: Predicting Blood Pressure from Photoplethysmograms in a Hypertensive Population. International Conference on Artificial Intelligence in Medicine. pp. 325–335. Springer (2020).
     
  • 24.
    Konak, O., Wegner, P., Arnrich, B.: IMU-Based Movement Trajectory Heatmaps for Human Activity Recognition. Sensors. 20, 7179 (2020).
     

2019

  • 1.
    Slosarek, T., Kraus, M., Schapranow, M.-P., Bottinger, E.: Qualitative Comparison of Selected Indel Detection Methods for RNA-Seq Data. International Work-Conference on Bioinformatics and Biomedical Engineering. pp. 166–177. Springer (2019).
     
  • 2.
    Freitas da Cruz, H., Pfahringer, B., Schneider, F., Meyer, A., Schapranow, M.-P.: External Validation of a Black-Box Clinical Predictive Model in Nephrology: Can Interpretability Methods Help Illuminate Performance Differences?. Proceedings of 17th Conference on Artificial Intelligence in Medicine. pp. 191–201 (2019).
     
  • 3.
    Konak, O., Freitas Da Cruz, H., Thiele, M., Golla, D., Schapranow, M.-P.: An Information and Communication Platform Supporting Analytics for Elderly Care. 5th International Conference on Information for Ageing Well, Communication Technologies e Health (2019).
     
  • 4.
    Freitas da Cruz, H., Schneider, F., Schapranow, M.-P.: Prediction of Acute Kidney Injury in Cardiac Surgery Patients: Interpretation using Local Interpretable Model-agnostic Explanations. Proceedings of the 12th International Conference on Biomedical Engineering Systems and Technologies. pp. 380–387. , Prague, Czech Republic (2019).
     
  • 5.
    Drimalla, H., Baskow, I., Roepke, S., Behnia, B., Dziobek, I.: Imitation und Erkennung von Emotionen bei Autismus-Spektrum-Störungen - eine computerbasierte Analyse des fazialen Emotionsausdrucks. 12. Wissenschaftliche Tagung Autismus-Spektrum. (2019).
     
  • 6.
    Freitas da Cruz, H., Bergner, B., Konak, O., Schneider, F., Bode, P., Lempert, C., Schapranow, M.-P.: MORPHER – A Platform to Support Modeling of Outcome and Risk Prediction in Health Research. Proceedings of the 19th IEEE International Conference on Bioinformatics and Biomedicine. , Athens, Greece (2019).
     
  • 7.
    Freitas da Cruz, H., Horschig, S., Nusshag, C., Schapranow, M.-P.: Knowledge Distillation from Machine Learning Models for Prediction of Hemodialysis Outcomes. International Journal On Advances in Life Sciences. 11, 33–43 (2019).
     
  • 8.
    Datta, S., Schraplau, A., da Cruz, H.F., Sachs, J.P., Mayer, F., Böttinger, E.: A Machine Learning Approach for Non-Invasive Diagnosis of Metabolic Syndrome. 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE). pp. 933–940. IEEE (2019).
     
  • 9.
    Wojcik, G.L., Graff, M., Nishimura, K.K., Tao, R., Haessler, J., Gignoux, C.R., Highland, H.M., Patel, Y.M., Sorokin, E.P., Avery, C.L., Belbin, G.M., Bien, S.A., Cheng, I., Cullina, S., Hodonsky, C.J., Hu, Y., Huckins, L.M., Jeff, J., Justice, A.E., Kocarnik, J.M., Lim, U., Lin, B.M., Lu, Y., Nelson, S.C., Park, S.-S.L., Poisner, H., Preuss, M.H., Richard, M.A., Schurmann, C., Setiawan, V.W., Sockell, A., Vahi, K., Verbanck, M., Vishnu, A., Walker, R.W., Young, K.L., Zubair, N., Acuna-Alonso, V., Ambite, J.L., Barnes, K.C., Boerwinkle, E., Bottinger, E.P., Bustamante, C.D., Caberto, C., Canizales-Quinteros, S., Conomos, M.P., Deelman, E., Do, R., Doheny, K., Fernandez-Rhodes, L., Fornage, M., Hailu, B., Heiss, G., Henn, B.M., Hindorff, L.A., Jackson, R.D., Laurie, C.A., Laurie, C.C., Li, Y., Lin, D.-Y., Moreno-Estrada, A., Nadkarni, G., Norman, P.J., Pooler, L.C., Reiner, A.P., Romm, J., Sabatti, C., Sandoval, K., Sheng, X., Stahl, E.A., Stram, D.O., Thornton, T.A., Wassel, C.L., Wilkens, L.R., Winkler, C.A., Yoneyama, S., Buyske, S., Haiman, C.A., Kooperberg, C., Le Marchand, L., Loos, R.J.F., Matise, T.C., North, K.E., Peters, U., Kenny, E.E., Carlson, C.S.: Genetic analyses of diverse populations improves discovery for complex traits. Nature. 570, 514–518 (2019).
     
  • 10.
    Drimalla, H., Landwehr, N., Hess, U., Dziobek, I.: From face to face: the contribution of facial mimicry to cognitive and emotional empathy. Cognition and Emotion. 33, 1672–1686 (2019).
     
  • 11.
    Belbin, G.M., Wenric, S., Cullina, S., Glicksberg, B.S., Moscati, A., Wojcik, G.L., Shemirani, R., Beckmann, N.D., Cohain, A., Sorokin, E.P., Park, D.S., Ambite, J.-L., Ellis, S., Auton, A., Bottinger, E.P., Cho, J.H., Loos, R.J., Abul husn, N.S., Zaitlen, N.A., Gignoux, C.R., Kenny, E.E., and: Towards a fine-scale population health monitoring system. (2019).
     

2018

  • 1.
    Kraus, M., Hesse, G., Slosarek, T., Danner, M., Kesar, A., Bhushan, A., Schapranow, M.-P.: DEAME-Differential Expression Analysis Made Easy. Heterogeneous Data Management, Polystores, and Analytics for Healthcare. pp. 162–174. Springer (2018).
     
  • 2.
    Freitas da Cruz, H., Horschig, S., Nusshag, C., Schapranow, M.-P.: Prediction of Patient Outcomes after Renal Replacement Therapy in Intensive Care. Proceedings of the 3rd International Conference on Informatics and Assistive Technologies for Health-Care, Medical Support and Wellbeing (2018).
     
  • 3.
    Freitas da Cruz, H., Gebhardt, M., Becher, F., Schapranow, M.-P.: Interactive Data Exploration Supporting Elderly Care Planning. Proceedings of the 10th International Conference on eHealth, Telemedicine, and Social Medicine (2018).