Research

I'm currently an associate research scientist with Mark Gerstein's lab in Computational Biology and Bioinformatics at Yale. My interests are in computational neuroscience, genomics, machine-learning and evolutionary theory. I'm also interested in music theory and analysis (which is the area of my PhD). Below is a summary of some of my work in these areas.

Neuroscience and Genomics


The brain exhibits multiple layers of organization and dynamics, from the genetic and molecular, to the behavioral and computational. In our work on PsychENCODE below, we developed a integrative deep-learning approach for tracing risk for psychiatric disorders across multiple levels of organization. Further, our forthcoming work on cancer genomics develops an approach which extends the standard individual driver based model of cancer progression to include the effects of weak drivers acting in tandem.

D. Wang*, S. Liu*, J. Warrell*, H. Won*, X. Shi*, F. C. P. Navarro*, D. Clarke*, M. Gu*, P Emani*, Y. T. Yang, M. Xu, M. J. Gandal, S. Lou, J. Zhang, J. J. Park, C. Yan, S. K. Rhie, K. Manakongtreecheep, H. Zhou, A. Nathan, M. Peters, E. Mattei, D. Fitzgerald, T. Brunetti, J. Moore, Y. Jiang, K. Girdhar, G. E. Hoffman, S. Kalayci, Z. H. Gumus, G. E. Crawford, PsychENCODE Consortium, P. Roussos, S. Akbarian, A. E. Jaffe, K. P. White, Z. Weng, N. Sestan, D. H. Geschwind, J. A. Knowles, M. B. Gerstein, 2018. Comprehensive functional genomic resource and integrative model for the human brain. Science, 362(6420), p.eaat8464. (* equal contribution) [DSPN code]


M. J. Gandal, P. Zhang, E. Hadjimichael, R. L. Walker, C. Chen, S. Liu, H. Won, H. van Bakel, M. Varghese, Y. Wang, A. W. Shieh, J. Haney, S. Parhami, J. Belmont, M. Kim, P. Moran Losada, Z. Khan, J. Mleczko, Y. Xia, R. Dai, D. Wang, Y. T. Yang, M. Xu, K. Fish, P. R. Hof, J. Warrell, D. Fitzgerald, K. White, A. E. Jaffe, PsychENCODE Consortium, M. A. Peters, M. Gerstein, C. Liu, L. M. Iakoucheva, D. Pinto, D. H. Geschwind, 2018. Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder. Science, 362(6420), p.eaat8127.


Sushant Kumar*, Jonathan Warrell*, Shantao Li, Patrick D. McGillivray, William Meyerson, Leonidas Salichos, Arif Harmanci, Alexander Martinez-Fundichely, Calvin W.Y. Chan, Morten Muhlig Nielsen, Lucas Lochovsky, Yan Zhang, Xiaotong Li, Jakob Skou Pedersen, Carl Herrmann, Gad Getz, Ekta Khurana, Mark B. Gerstein, 2018. Passenger mutations in 2500 cancer genomes: Overall molecular functional impact and consequences. bioRxiv, p.280446. (* equal contribution, forthcoming) [Additive variance model code]

Interpretable Machine Learning


Deep-learning provides models of universal function classes, and has proved capable of learning generalizable predictive models for real-world tasks of (effectively) arbitrary complexity. In order to integrate the knowledge learnt by such models with prior scientific and cultural knowledge, techniques are required to analyze or interpret the models learnt. In the papers below, we develop techniques for extracting and representing knowledge in deep neural nets and probabilistic programming models.


J. Warrell, H. Mohsen, M. B. Gerstein, 2018. Rank Projection Trees for Multilevel Neural Network Interpretation, NeurIPS Workshop on Machine Learning for Health. [code]


J. Warrell, M. B. Gerstein, 2018. Dependent Type Networks: A Probabilistic Logic via the Curry-Howard Correspondence in a System of Probabilistic Dependent Types, Uncertainty in Artificial Intelligence Workshop on Uncertainty in Deep Learning.

Evolution and Theoretical Biology


Evolution is both a physical and a computational process. Evolutionary processes exhibit structure and are capable of information processing at a range of levels, from molecular and neural networks, to behavior and culture. In the papers below, we consider how to define evolutionary processes embedding cyclic and multilevel notions of causality, and the relationship of global and local features in certain observed cellular molecular networks.


J. Warrell and M. Mhlanga, 2017. Stability and structural properties of gene regulation networks with coregulation rules. Journal of theoretical biology, 420, pp.304-317.

J. Warrell and M. B. Gerstein, 2019. Decomposing Information and Coarse-Graining in Causal Evolutionary Processes. (forthcoming)

Music Theory and Analysis


Musical structure is created through a rich interaction of cultural, physical, abstract and psychological causes. In my thesis, I develop an integrative theory and analytical approach, focused on the music of the 20th century composer Arnold Schoenberg, which explores general notions of form and function applicable to entities across all musical dimensions.


J. Warrell, 2006. Repetition in the Music of Arnold Schoenberg. PhD thesis, King's College London.