Publications

In the work below, I’ve marked my own favourites with a dagger (†) and elaborated a bit about why I like them.

Takemura, S., Xu, C. S., et al. (2015). Synaptic circuits and their variations within different columns in the visual system of Drosophila. PNAS 112 (44), 13711-13716

Van der Walt, S., Schönberger, J. L., Nunez-Iglesias, J., Boulogne F., Warner, J. D., Yager, N., Gouillart, E., Yu, T., and the scikit-image contributors (2014). scikit-image: image processing in Python. PeerJ 2:e453, PeerJ PrePrints 2:e336v2

Developing tools is not often the most glamorous part of science, but it can be among the most influential. Scikit-image is a fantastic image processing library that I have had the privilege of working on extensively. If you are taking part in the revolution in imaging in biology, read this to see if scikit-image can help you make sense of your data.

Nunez-Iglesias, J., Kennedy, R., Plaza, S. M., Chakraborty, A., and Katz, W. T. (2014). Graph-based active learning of agglomeration (GALA): a Python library to segment 2D and 3D neuroimages. Frontiers in neuroinformatics 8, 34. doi:10.3389/fninf.2014.00034.

† Nunez-Iglesias, J., Kennedy, R., Parag, T., Shi, J., and Chklovskii, D. B. (2013). Machine Learning of Hierarchical Clustering to Segment 2D and 3D Images. PLoS ONE 8, e71715. doi:10.1371/journal.pone.0071715.

I developed an algorithm, graph-based active learning of agglomeration (GALA), that can learn hierarchical agglomeration at all levels of the hierarchy, which is important for multi-scale image segmentation problems. It produced exceptional results in segmentation accuracy. In the SNEMI3D challenge, our second submission was ranked 1st for 8 months in 2013. In Apr 2014 Neal Donnelly modified the open source implementation (detailed in the Frontiers in Neuroinformatics paper above) to regain first place, by a wide margin.

Hu, T., Nunez-Iglesias, J., Vitaladevuni, S., Scheffer, L., Xu, S., Bolorizadeh, M., Hess, H., Fetter, R., and Chklovskii, D. (2013). Electron Microscopy Reconstruction of Brain Structure Using Sparse Representations over Learned Dictionaries. IEEE transactions on medical imaging. doi:10.1109/TMI.2013.2276018.

Grover, D.*, and Nunez-Iglesias, J.* (2012). Betamax: towards optimal sampling strategies for high-throughput screens. J. Comput. Biol. 19, 776–784. doi:10.1089/cmb.2012.0036.

Glasner, D., Hu, T., Nunez-Iglesias, J., Scheffer, L., Xu, S., Hess, H., Fetter, R., Chklovskii, D. B., and Basri, R. (2011). High resolution segmentation of neuronal tissues from low depth-resolution EM imagery. EMMCVPR ’11, 1–12.

Nunez-Iglesias, J., Liu, C.-C., Morgan, T. E., Finch, C. E., and Zhou, X. J. (2010). Joint genome-wide profiling of miRNA and mRNA expression in Alzheimer’s disease cortex reveals altered miRNA regulation. PLoS ONE 5, e8898. doi:10.1371/journal.pone.0008898.

Mehan, M. R., Nunez-Iglesias, J., Dai, C., Waterman, M. S., and Zhou, X. J. (2010). An integrative modular approach to systematically predict gene-phenotype associations. BMC Bioinformatics 11 Suppl 1, S62. doi:10.1186/1471-2105-11-S1-S62.

Mehan, M. R., Nunez-Iglesias, J., Kalakrishnan, M., Waterman, M. S., and Zhou, X. J. (2009). An integrative network approach to map the transcriptome to the phenome. J. Comput. Biol. 16, 1023–1034. doi:10.1089/cmb.2009.0037.

Wang, W.*, Nunez-Iglesias, J.*, Luan, Y., and Sun, F. (2009). Usefulness and limitations of dK random graph models to predict interactions and functional homogeneity in biological networks under a pseudo-likelihood parameter estimation approach. BMC Bioinformatics 10, 277. doi:10.1186/1471-2105-10-277.

Xu, M., Kao, M.-C. J., Nunez-Iglesias, J., Nevins, J. R., West, M., and Zhou, X. J. (2008). An integrative approach to characterize disease-specific pathways and their coordination: a case study in cancer. BMC Genomics 9 Suppl 1, S12. doi:10.1186/1471-2164-9-S1-S12.

Pan, F., Chiu, C.-H., Pulapura, S., Mehan, M. R., Nunez-Iglesias, J., Zhang, K., Kamath, K., Waterman, M. S., Finch, C. E., and Zhou, X. J. (2007). Gene Aging Nexus: a web database and data mining platform for microarray data on aging. Nucleic Acids Res 35, D756–9. doi:10.1093/nar/gkl798.

Pan, F., Kamath, K., Zhang, K., Pulapura, S., Achar, A., Nunez-Iglesias, J., Huang, Y., Yan, X., Han, J., Hu, H., et al. (2006). Integrative Array Analyzer: a software package for analysis of cross-platform and cross-species microarray data. Bioinformatics 22, 1665–1667. doi:10.1093/bioinformatics/btl163.

Noble, B., Abada, P., Nunez-Iglesias, J., and Cannon, P. M. (2006). Recruitment of the adaptor protein 2 complex by the human immunodeficiency virus type 2 envelope protein is necessary for high levels of virus release. J. Virol. 80, 2924–2932. doi:10.1128/JVI.80.6.2924-2932.2006.

Zhou, X. J., Kao, M.-C. J., Huang, H., Wong, A., Nunez-Iglesias, J., Primig, M., Aparicio, O. M., Finch, C. E., Morgan, T. E., and Wong, W. H. (2005). Functional annotation and network reconstruction through cross-platform integration of microarray data. Nat. Biotechnol. 23, 238–243. doi:10.1038/nbt1058.

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