The Training component of the National Resource for Network Biology (NRNB) aims to offer direct, hands-on training in NRNB bioinformatic technologies, enabling researchers to carry out network analyses within the context of their own study and datasets. It also aims to more generally teach the concepts of network biology to a broad audience of domain specialists who may or may not have considered such techniques beforehand. The Training Coordinator conducts the majority of training events for NRNB tool users and potential developers. Training events vary in format and include lectures and seminars, hands-on workshops, one-on-one training and bootcamps. These activities leverage materials developed over many years, including the 64-page white paper on Network Biology, aka The Book, and a 60-slide presentation deck covering a survey of network biology concepts and uses and an introduction to Cytoscape. One of our most successful training activities is our participation as a mentoring organization in the Google Summer of Code (GSoC) program. Over the past 4 years, we have recruited over 50 mentors and attracted over 100 students around the world to apply to work on our network biology projects. The NRNB has ranked in the top 5-13% in terms of the number of slots allocated by Google across all accepted organizations over the past 4 years. This performance places the NRNB in the company of other world-class open source efforts such as Apache, Mozilla, R, and Python. Some metrics representing our ongoing training efforts are as follows: Google funded 18 students for the NRNB in 2014 (top 8% of mentoring organizations) NRNB staff conducted 39 hands-on workshops NRNB staff presented 15 lectures and participated in 38 courses 70 GSoC and NRNB Academy projects with 60 unique students Overall acceptance rate of 94% on GSoC projects Publication of 10 student co-authored papers New in this proposal is an effort to train Bioinformatics Core personnel at various research institutions to leverage their unique positions in collaborating with a broad, ever-changing set of research projects. We will implement additional mechanisms for tracking the impact of our training activities, including surveys and innovative new metrics.
| Buckley, Alexandra R; Ideker, Trey; Carter, Hannah et al. (2018) Exome-wide analysis of bi-allelic alterations identifies a Lynch phenotype in The Cancer Genome Atlas. Genome Med 10:69 |
| Wang, Sheng; Ma, Jianzhu; Zhang, Wei et al. (2018) Typing tumors using pathways selected by somatic evolution. Nat Commun 9:4159 |
| Zhang, Wei; Ma, Jianzhu; Ideker, Trey (2018) Classifying tumors by supervised network propagation. Bioinformatics 34:i484-i493 |
| Nikolayeva, Iryna; Guitart Pla, Oriol; Schwikowski, Benno (2018) Network module identification-A widespread theoretical bias and best practices. Methods 132:19-25 |
| Zhang, Wei; Bojorquez-Gomez, Ana; Velez, Daniel Ortiz et al. (2018) A global transcriptional network connecting noncoding mutations to changes in tumor gene expression. Nat Genet 50:613-620 |
| Reznik, Ed; Luna, Augustin; Aksoy, Bülent Arman et al. (2018) A Landscape of Metabolic Variation across Tumor Types. Cell Syst 6:301-313.e3 |
| Huang, Justin K; Jia, Tongqiu; Carlin, Daniel E et al. (2018) pyNBS: a Python implementation for network-based stratification of tumor mutations. Bioinformatics 34:2859-2861 |
| MacParland, Sonya A; Liu, Jeff C; Ma, Xue-Zhong et al. (2018) Single cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations. Nat Commun 9:4383 |
| Pai, Shraddha; Bader, Gary D (2018) Patient Similarity Networks for Precision Medicine. J Mol Biol 430:2924-2938 |
| Ebhardt, H Alexander; Root, Alex; Liu, Yansheng et al. (2018) Systems pharmacology using mass spectrometry identifies critical response nodes in prostate cancer. NPJ Syst Biol Appl 4:26 |
Showing the most recent 10 out of 145 publications
