Publications

@inproceedings{Sieberts2017RECOMB,

title = {Respiratory Viral DREAM Challenge: Discovering dynamic molecular signatures in response to viral exposure},

author = {Solveig Sieberts and Ricardo Henao and Slim Fourati and Riku Klén and Mehrad Mahmoudian and Aarthi Talla and Gaurav Pandey and Torbjörn Nordling and Mehmet Ahsen and Reem Almugbel and Zafer Aydın and Joshua Burkhart and Samad Jahandideh and Xiao Liang and Motoki Shiga and Ana Stanescu and Robert Vogel and Ka Yee Yeung and Thomas Yu and Laura Elo and Ephraim Tsalik and Lara Mangravite},

url = {https://www.iscb.org/recomb-regsysgen2017},

year  = {2017},

date = {2017-11-01},

booktitle = {10th Annual RECOMB/ISCB Conference on Regulatory and Systems Genomics, with DREAM Challenges},

publisher = {International Society for Computational Biology},

address = {New York City, New York},

abstract = {Acute respiratory infections (ARIs) are among the most common reasons for seeking medical attention in the United States. Prediction of susceptibility or early infection can have can have important impacts in terms of treatment decisions or in the prediction or control of pandemics. Following exposure to respiratory viruses, infection rates are variable among individuals, and some patients who exhibit viral shedding never develop clinical symptoms. We developed and ran a DREAM challenge to predict viral shedding and respiratory symptoms in patients exposed to one of four different respiratory viruses (H1N1, H3N2, rhinovirus, or respiratory syncytial virus (RSV)), based on pre- and early-stage post-exposure, longitudinally sampled blood gene expression profiling up to 24 hours post-e. The challenge attracted participation of 118 individuals organized into 36 teams, who were able to demonstrate signal to predict which subjects will become symptomatic following viral exposure, even prior to exposure. This challenge featured an active community phase featuring participant-lead projects exploring gene pathways that contribute to prediction, heterogeneity in prediction across samples, characteristics of well performing models, reproducibility of submitted code, and the assessment of model overfitting in the absence of an independent test data set.},

keywords = {DREAM challenge, Gene expression, Infection susceptibility, Predictive modeling, Respiratory infection, Viral infection},

pubstate = {published},

tppubtype = {inproceedings}

}