Edinburgh Research Explorer

Institute for Adaptive and Neural Computation

Organisational unit: Research Institute

  1. scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells

    Clark, S. J., Argelaguet, R., Kapourani, A., Stubbs, T. M., Lee, H. J., Alda-Catalinas, C., Krueger, F., Sanguinetti, G., Kelsey, G., Marioni, J. C., Stegle, O. & Reik, W., 22 Feb 2018, In : Nature Communications. 9, 17 p., 781.

    Research output: Contribution to journalArticle

  2. riboviz: analysis and visualization of ribosome profiling datasets

    Carja, O., Xing, T., Wallace, E. W. J., Plotkin, J. B. & Shah, P., 25 Oct 2017, In : BMC Bioinformatics. 18, 461

    Research output: Contribution to journalArticle

  3. qpMerge: Merging different peptide isoforms using a motif centric strategy

    Hindle, M. M., Le Bihan, T., Krahmer, J., Martin, S. F., Noordally, Z. B., Simpson, T. I. & Millar, A. J., 5 Apr 2016, (Submitted) bioRxiv, at Cold Spring Harbor Laboratory, 9 p.

    Research output: Working paper

  4. puma: a Bioconductor package for propagating uncertainty in microarray analysis

    Pearson, R., Liu, X., Sanguinetti, G., Milo, M., Lawrence, N. & Rattray, M., 2009, In : BMC Bioinformatics. 10, 1, 10 p.

    Research output: Contribution to journalArticle

  5. mRNA Cap Methyltransferase, RNMT-RAM, Promotes RNA Pol II-Dependent Transcription

    Varshney, D., Lombardi, O., Schweikert, G., Dunn, S., Suska, O. & Cowling, V. H., 2 May 2018, In : Cell Reports. 23, 5, p. 1530-1542 14 p.

    Research output: Contribution to journalArticle

  6. iBehave - applications of supervised machine learning to behaviour analysis.

    Heward, J. A., Crook, P. A., Lukins, T. C. & Armstrong, D., 2008, Proceedings of Measuring Behaviour 2008. 6th International Conference on Methods and Techniques in Behavioural Research. Spink, A., Ballintijn, M., Bogers, N., Grieco, F., Loijens, L., Noldus, L., Smit, G. & Zimmerman, P. (eds.). p. 314-315 2 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  7. fMRI correlates of state and trait effects in subjects at genetically enhanced risk of schizophrenia

    Whalley, H. C., Simonotto, E., Flett, S., Marshall, I., Ebmeier, K. P., Owens, D. G. C., Goddard, N. H., Johnstone, E. C. & Lawrie, S. M., Mar 2004, In : Brain. 127, Pt 3, p. 478-90 13 p.

    Research output: Contribution to journalArticle

  8. eCAT: Online electronic lab notebook for scientific research

    Goddard, N., Macneil, R. & Ritchie, J., 2009, In : Automated Experimentation. 1, p. 1-7 7 p., 4.

    Research output: Contribution to journalArticle

  9. Wrattler: Reproducible, live and polyglot notebooks

    Petricek, T., Geddes, J. & Sutton, C., 2018, 10th USENIX Workshop on Theory and Practice of Provenance (TaPP 2018). London, UK: Usenix, p. 1-4 4 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  10. Word Storms: Multiples of Word Clouds for Visual Comparison of Documents

    Castella, Q. & Sutton, C. A., 2014, Proceedings of the 23rd international conference on World wide web. International World Wide Web Conferences Steering Committee, p. 665-676 12 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  11. Width of Minima Reached by Stochastic Gradient Descent is Influenced by Learning Rate to Batch Size Ratio

    Jastrzębski, S., Kenton, Z., Arpit, D., Ballas, N., Fischer, A., Bengio, Y. & Storkey, A., Oct 2018, Proceedings of 27th International Conference on Artificial Neural Networks. Rhodes, Greece: Springer, Cham, p. 392-402 10 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  12. Wide-band information transmission at the calyx of Held

    Hennig, M., Graham, B. P., Yang, Z., Postlethwaite, M. & Forsythe, I. D., Apr 2009, In : Neural Computation. 21, 4, p. 991-1017 27 p.

    Research output: Contribution to journalLetter

  13. Why, when, and what: Analyzing Stack Overflow questions by topic, type, and code

    Allamanis, M. & Sutton, C., 2013, Mining Software Repositories (MSR), 2013 10th IEEE Working Conference on. Institute of Electrical and Electronics Engineers (IEEE), p. 53-56 4 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  14. When Training and Test Sets Are Different: Characterizing Learning Transfer

    Storkey, A., Quiñonero-Candela, J., Sugiyama, M., Schwaighofer, A. & Lawrence, ND., Dec 2008, Dataset Shift in Machine Learning. Cambridge: Yale University Press in association with the Museum of London, p. 3-28 26 p. (Neural Information Processing Series).

    Research output: Chapter in Book/Report/Conference proceedingChapter

  15. What, if anything, are topological maps for?

    Wilson, S. P. & Bednar, J. A., 11 Feb 2015, In : Developmental neurobiology.

    Research output: Contribution to journalArticle

  16. What can MaxEnt reveal about high-density recordings and what can high-density recordings reveal about MaxEnt?

    Panas, D., Maccione, A., Berdondini, L. & Hennig, M., 2011, In : BMC Neuroscience. 12, Supplement 1, 2 p., P146.

    Research output: Contribution to journalMeeting abstract

  17. Weak Epistasis May Drive Adaptation in Recombining Bacteria

    Arnold, B. J., Gutmann, M., Grad, Y. H., Sheppard, S. K., Corander, J., Lipsitch, M. & Hanage, W. P., 1 Mar 2018, In : Genetics. 208, 3, p. 1247-1260 39 p.

    Research output: Contribution to journalArticle

  18. Volume transmission as a new homeostatic mechanism

    Sweeney, Y. A., Hellgren-Kotaleski, J. & Hennig, M., 2013.

    Research output: Contribution to conferencePoster

  19. Visual Cortex as a General-Purpose Information-Processing Device

    Bednar, J. A., 2012, Computer Vision – ECCV 2012. Workshops and Demonstrations: Florence, Italy, October 7-13, 2012, Proceedings, Part I. Fusiello, A., Murino, V. & Cucchiara, R. (eds.). Springer Berlin Heidelberg, p. 480-485 6 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  20. Visual Boundary Prediction: A Deep Neural Prediction Network and Quality Dissection

    Kivinen, J., Williams, C. K. I. & Heess, N., 2014, Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics. Reykjavik, Iceland: Journal of Machine Learning Research: Workshop and Conference Proceedings, Vol. 33. p. 512-521 10 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

Previous 1 2 3 4 5 6 7 8 ...66 Next