Items where Subject is "bioinformatics > computational biology > algorithms > machine learning"

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Number of items at this level: 18.

B

Belkin, M., Hsu, D., Mitra, P. P. (December 2018) Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate. In: 32nd Conference on Neural Information Processing Systems, NeurIPS 2018, Montreal, Canada.

Berlow, N. E., Rikhi, R., Geltzeiler, M., Abraham, J., Svalina, M. N., Davis, L. E., Wise, E., Mancini, M., Noujaim, J., Mansoor, A., Quist, M. J., Matlock, K. L., Goros, M. W., Hernandez, B. S., Doung, Y. C., Thway, K., Tsukahara, T., Nishio, J., Huang, E. T., Airhart, S., Bult, C. J., Gandour-Edwards, R., Maki, R. G., Jones, R. L., Michalek, J. E., Milovancev, M., Ghosh, S., Pal, R., Keller, C. (June 2019) Probabilistic modeling of personalized drug combinations from integrated chemical screen and molecular data in sarcoma. BMC Cancer, 19 (1). p. 593. ISSN 1471-2407

C

Carter, J. A., Preall, J. B., Atwal, G. S. (October 2019) Bayesian Inference of Allelic Inclusion Rates in the Human T Cell Receptor Repertoire. Cell Syst. ISSN 2405-4712 (Public Dataset)

Chandrasekaran, S., Navlakha, S., Audette, N. J., McCreary, D. D., Suhan, J., Bar-Joseph, Z., Barth, A. L. (December 2015) Unbiased, High-Throughput Electron Microscopy Analysis of Experience-Dependent Synaptic Changes in the Neocortex. J Neurosci, 35 (50). pp. 16450-62. ISSN 0270-6474

D

Dasgupta, S., Sheehan, T. C., Stevens, C. F., Navlakha, S. (December 2018) A neural data structure for novelty detection. Proc Natl Acad Sci U S A, 115 (51). pp. 13093-13098. ISSN 0027-8424 (Public Dataset)

Derkarabetian, S., Castillo, S., Koo, P. K., Ovchinnikov, S., Hedin, M. (October 2019) A demonstration of unsupervised machine learning in species delimitation. Mol Phylogenet Evol, 139. p. 106562. ISSN 1055-7903

F

Fang, Han, Huang, Yi-Fei, Radhakrishnan, Aditya, Siepel, Adam, Lyon, Gholson J., Schatz, Michael C. (February 2018) Scikit-ribo Enables Accurate Estimation and Robust Modeling of Translation Dynamics at Codon Resolution. Cell Systems, 6 (2). pp. 180-191. ISSN 2405-4712

Fleischer, J. G., Schulte, R., Tsai, H. H., Tyagi, S., Ibarra, A., Shokhirev, M. N., Huang, L., Hetzer, M. W., Navlakha, S. (December 2018) Predicting age from the transcriptome of human dermal fibroblasts. Genome Biol, 19 (1). p. 221. ISSN 1474-7596 (Public Dataset)

K

Koo, P. K., Weitzman, M., Sabanaygam, C. R., van Golen, K. L., Mochrie, S. G. (October 2015) Extracting Diffusive States of Rho GTPase in Live Cells: Towards In Vivo Biochemistry. PLoS Comput Biol, 11 (10). e1004297. ISSN 1553-734x

Koo, Peter K., Anand, Praveen, Paul, Steffan B., Eddy, Sean R. (2018) Inferring Sequence-Structure Preferences of RNA-Binding Proteins with Convolutional Residual Networks. bioRxiv. p. 418459. (Unpublished)

Koo, Peter K., Eddy, Sean R. (2019) Representation Learning of Genomic Sequence Motifs with Convolutional Neural Networks. bioRxiv. p. 362756. (Unpublished)

M

Malta, T. M., Sokolov, A., Gentles, A. J., Burzykowski, T., Poisson, L., Weinstein, J. N., Kaminska, B., Huelsken, J., Omberg, L., Gevaert, O., Colaprico, A., Czerwinska, P., Mazurek, S., Mishra, L., Heyn, H., Krasnitz, A., Godwin, A. K., Lazar, A. J., Stuart, J. M., Hoadley, K. A., Laird, P. W., Noushmehr, H., Wiznerowicz, M. (April 2018) Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation. Cell, 173 (2). 338-354.e15. ISSN 0092-8674

Mitra, P. P. (November 2018) Fast convergence for stochastic and distributed gradient descent in the interpolation limit. European Signal Processing Conference, EUSIPCO, pp. 1890-1894. ISBN 22195491 (ISSN); 9789082797015 (ISBN)

N

Navlakha, S. (February 2017) Learning the Structural Vocabulary of a Network. Neural Comput, 29 (2). pp. 287-312. ISSN 0899-7667

Navlakha, S., Suhan, J., Barth, A. L., Bar-Joseph, Z. (July 2013) A high-throughput framework to detect synapses in electron microscopy images. Bioinformatics, 29 (13). i9-i17. ISSN 13674803 (ISSN) (Public Dataset)

R

Richards, B. A., Lillicrap, T. P., Beaudoin, P., Bengio, Y., Bogacz, R., Christensen, A., Clopath, C., Costa, R. P., de Berker, A., Ganguli, S., Gillon, C. J., Hafner, D., Kepecs, A., Kriegeskorte, N., Latham, P., Lindsay, G. W., Miller, K. D., Naud, R., Pack, C. C., Poirazi, P., Roelfsema, P., Sacramento, J., Saxe, A., Scellier, B., Schapiro, A. C., Senn, W., Wayne, G., Yamins, D., Zenke, F., Zylberberg, J., Therien, D., Kording, K. P. (November 2019) A deep learning framework for neuroscience. Nat Neurosci, 22 (11). pp. 1761-1770. ISSN 1097-6256

T

Tran, Ngoc, Kepple, Daniel, Shuvaev, Sergey A., Koulakov, Alexei A. (June 2019) DeepNose: Using artificial neural networks to represent the space of odorants. Proceedings of the 36th International Conference on Machine Learning, 97. pp. 6305-6314.

Z

Ziamtsov, I., Navlakha, S. (October 2019) Machine learning approaches to improve three basic plant phenotyping tasks using 3D point clouds. Plant Physiol. ISSN 0032-0889

This list was generated on Sun Dec 8 13:15:53 2019 EST.
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