Barash, Yoseph

A new view of transcriptome complexity and regulation through the lens of local splicing variations

Vaquero-Garcia, J., Barrera, A., Gazzara, M. R., González-Vallinas, J., Lahens, N. F., Hogenesch, J. B., Lynch, K. W., & Barash, Y. (2016) · eLife

RNA Research and SplicingRNA regulation and disease

Cited by 488

Functional coordination of alternative splicing in the mammalian central nervous system

Fagnani, M., Barash, Y., Ip, J. Y., Misquitta, C., Pan, Q., Saltzman, A. L., Shai, O., Lee, L., Rozenhek, A., Mohammad, N., et al. (2007) · Genome Biology

RNA Research and SplicingRNA regulation and disease

Cited by 120

Enhanced integrated gradients: improving interpretability of deep learning models using splicing codes as a case study

Jha, A., K. Aicher, J., R. Gazzara, M., Singh, D., & Barash, Y. (2020) · Genome Biology

Explainable Artificial Intelligence (XAI)RNA Research and Splicing

Cited by 93

Identifying common transcriptome signatures of cancer by interpreting deep learning models

Jha, A., Quesnel-Vallières, M., Wang, D., Thomas-Tikhonenko, A., Lynch, K. W., & Barash, Y. (2022) · Genome Biology

Gene expression and cancer classificationCancer-related molecular mechanisms research

Cited by 39

Rapid and Scalable Profiling of Nascent RNA with fastGRO

Barbieri, E., Hill, C., Quesnel-Vallières, M., Zucco, A. J., Barash, Y., & Gardini, A. (2020) · Cell Reports

Genomics and Chromatin DynamicsRNA Research and Splicing

Cited by 33