Rothamel, Katherine

Conservation and divergence in the transcriptional programs of the human and mouse immune systems

Shay, T., Jojic, V., Zuk, O., Rothamel, K., Puyraimond-Zemmour, D., Feng, T., Wakamatsu, E., Benoist, C., Koller, D., Regev, A., et al. (2013) · Proceedings of the National Academy of Sciences

Genomics and Chromatin DynamicsBioinformatics and Genomic Networks

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Parsing the interferon transcriptional network and its disease associations

Mostafavi, S., Yoshida, H., Moodley, D., LeBoité, H., Rothamel, K., Raj, T., Ye, C. J., Chevrier, N., Zhang, S.-Y., Feng, T., et al. (2016) · Cell

interferon and immune responsesCytokine Signaling Pathways and Interactions

Cited by 337

CD33: increased inclusion of exon 2 implicates the Ig V-set domain in Alzheimer’s disease susceptibility

Raj, T., Ryan, K. J., Replogle, J. M., Chibnik, L. B., Rosenkrantz, L., Tang, A., Rothamel, K., Stranger, B. E., Bennett, D. A., Evans, D. A., et al. (2014) · Human Molecular Genetics

Alzheimer's disease research and treatmentsNeuroinflammation and Neurodegeneration Mechanisms

Cited by 144

HydRA: Deep-learning models for predicting RNA-binding capacity from protein interaction association context and protein sequence

Jin, W., Brannan, K. W., Kapeli, K., Park, S. S., Tan, H. Q., Gosztyla, M. L., Mujumdar, M., Ahdout, J., Henroid, B., Rothamel, K., et al. (2023) · Molecular Cell

RNA Research and SplicingMachine Learning in Bioinformatics

Cited by 52

HydRA: Deep-learning models for predicting RNA-binding capacity from protein interaction association context and protein sequence

Jin, W., Brannan, K. W., Kapeli, K., Park, S. S., Tan, H. Q., Gosztyla, M. L., Mujumdar, M., Ahdout, J., Henroid, B., Rothamel, K., et al. (2022) · Molecular Cell

RNA Research and SplicingMachine Learning in Bioinformatics

Cited by 52