Project Title: Predicting the epigenomic and clinical phenotypic effects of genomic mutations through deep learning
Supervisor: Dr Nathan Skene
Location: Sir Michael Uren Hub, White City Campus

About Me

As a researcher with experiences in a variety of fields, I am a believer in the innovation and insights that can be gained from highly inter-disciplinary research. In 2011, I earned my ScB in Cognitive Neuroscience from Brown University (Providence, RI). The core focus of my work was and continues to be the investigation of human susceptibility to diseases and disorders in order to impact human health and alleviate suffering.

In order to uncover the evolutionary underpinnings of human-specific cognitive abilities and neurobiological susceptibilities, I then took on jobs in two labs simultaneously (as Lab Manager and Research Assistant, respectively) at The George Washington University (Washington, DC). At the same institution, I went on to earn my MPhil in Human Paleobiology (2017) investigating the neuroanatomical, transcriptomic and genomic evolution of the hippocampus, adult neurogenesis and episodic memory. During this time, I became increasingly interested in applying programming and computational techniques to expand the scale and scope of the questions that I’m able to address.

In the pursuit of further advancing my skills as a computational researcher, I thereafter accepted a position as a Bioinformatician at the Icahn School of Medicine at Mount Sinai (New York City, NY). There I helped to develop a number of open-access bioinformatics tools; e.g. X2K Web, GeneshotcatalogueR, and echolocatoR. My collaborators and I also published work revealing cell-type-specific neurogenomic mechanisms underlying neurological conditions (e.g. Parkinson’s and Alzheimer’s Disease) through the application of statistical and machine learning techniques to large-scale multi-omics and clinical data.

I am now pursuing my PhD in Clinical Medicine Research at Imperial College London under the advisorship of Dr. Nathan Skene. Outside of research, I also enjoy ultra-marathon running and music production.

Qualifications

  • 2017-2020: Bioinformatician II, Icahn School of Medicine at Mount Sinai
  • 2017: MPhil in Human Paleobiology (with a focus on Evolutionary Neurogenomics), The George Washington University
  • 2011-2013: Lab Manager and Research Assistant, The George Washington University
  • 2011: ScB in Cognitive Neuroscience (with a focus on Neurological Diseases and Disorders), Brown University 

Research Interests

More data was generated in the last 2 years than in all of human history, and the fields of science and medicine are no exception. Effectively and strategically utilizing these mountains of data is a huge challenge, but one with undoubtedly even greater rewards. At Imperial College London, I am integrating high-throughput genomic, transcriptomic, epigenomic, and phenotypic data to further disentangle the cell-type-specific and developmentally-contextualized mechanisms underlying neurodegenerative diseases. This will be accomplished through the application and development of machine learning (specifically deep learning) and statistical techniques to accurately predict regulatory and clinical phenotype outcomes of complex genomic features and computationally identify novel therapeutics.

Selected publications

Ramdhani, E Navarro, E Udine, AG Efthymiou, BM Schilder, M Parks, A Goate, T Raj (2020). Tensor decomposition of stimulated monocyte and macrophage gene expression profiles identifies neurodegenerative disease-specific trans-eQTLs. PLOS Genetics, 16 (9), e1008549, https://doi.org/10.1101/499509

BM Schilder, P Hof, H Petry (2019). Evolutionary shifts dramatically reorganized the human hippocampal complex. Journal of Comparative Neurology, https://doi.org/10.1002/cne.24822

D Clarke, L Wang, A Jones, M Wojciechowicz, D Torre, K Jagodnik, S Jenkins, P McQuilton, Z Flamholz, M Silverstein, BM Schilder…A Ma’ayan (2019) FAIRshake: Toolkit to Evaluate the Findability, Accessibility, Interoperability, and Reusability of Research Digital Resources. Chosen as ‘Featured Frontmatter’ article in Cell Systems, 9, https://doi.org/10.1016/j.cels.2019.09.011

A Lachmann, BM Schilder, ML Wojciechowicz, D Torre, MV Kuleshov, AB Keenan, A Ma’ayan (2019). Geneshot: search engine for ranking genes from arbitrary text queries. Nucleic Acids Research, 1–7, https://doi.org/10.1093/nar/gkz393

DJB Clarke, MV Kuleshov, BM Schilder, D Torre, ME Duffy, AB Keenan, A Lachmann, AS Feldmann, GW Gundersen, MC Silverstein, Z Wang (2018) eXpression2Kinases (X2K) Web: linking expression signatures to upstream cell signaling networks. Nucleic Acids Research, 46 (W1), pW171-W179 https://doi.org/10.1093/nar/gky458

F Subiaul, L Zimmerman, E Renner, BM Schilder, R Barr (2015) Defining elemental imitation mechanisms: A comparison of cognitive and motor-spatial imitation learning across object- and computer-based tasks. Journal of Cognition and Development, 17 (2), p221-243, https://doi.org/10.1080/15248372.2015.1053483

KA Phillips, MK Hambright, K Hewes, BM Schilder, CN Ross, SD Tardif (2015) Take the monkey and run. Journal of Neuroscience Methods, 248, p27–31. http://doi.org/10.1016/j.jneumeth.2015.03.023

F Subiaul, EM Patterson, BM Schilder, E Renner, R Barr (2014) Becoming a high-fidelity - super - imitator: what are the contributions of social and individual learning? Developmental Science, 18 (6), p1025-1035, http://doi.org/10.1111/desc.12276

F Subiaul, BM Schilder (2014) Working memory constraints on imitation and emulation. Journal of Experimental Child Psychology, 128, p190-200, http://doi.org/10.1016/j.jecp.2014.07.005

Professional memberships

Leakey Foundation Research Grantee
Cosmos Club Scholar

Contact Details

Email: b.schilder20@imperial.ac.uk 
LinkedInhttps://www.linkedin.com/in/brian-schilder