Items where Subject is "machine learning"

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Number of items at this level: 34.

B

Banerjee, Samik, Magee, Lucas, Wang, Dingkang, Li, Xu, Huo, Bing-Xing, Jayakumar, Jaikishan, Matho, Katherine, Lin, Meng-Kuan, Ram, Keerthi, Sivaprakasam, Mohanasankar, Huang, Josh, Wang, Yusu, Mitra, Partha P (October 2020) Semantic segmentation of microscopic neuroanatomical data by combining topological priors with encoder-decoder deep networks. Nature Machine Intelligence, 2 (10). 585-+. ISSN 2522-5839

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 (Public Dataset)

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, 9 (5). pp. 475-482. 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

Fischer, Stephan, Gillis, Jesse (September 2021) Defining the extent of gene function using ROC curvature. BioRxiv. (Unpublished)

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)

H

Hejase, H. A., Dukler, N., Siepel, A. (January 2020) From Summary Statistics to Gene Trees: Methods for Inferring Positive Selection. Trends Genet. ISSN 0168-9525 (Print)0168-9525

K

Kawaguchi, Risa K., Takahashi, Masamichi, Miyake, Mototaka, Kinoshita, Manabu, Takahashi, Satoshi, Ichimura, Koichi, Hamamoto, Ryuji, Narita, Yoshitaka, Sese, Jun (July 2021) Assessing Versatile Machine Learning Models for Glioma Radiogenomic Studies across Hospitals. Cancers, 13 (14). p. 3611. ISSN 2072-6694

Kawaguchi, Risa Karakida, Tang, Ziqi, Fischer, Stephan, Tripathy, Rohit, Koo, Peter, Gillis, Jesse (April 2021) Exploiting marker genes for robust classification and characterization of single-cell chromatin accessibility. BioRxiv. (Unpublished)

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)

Koo, PK, Ploenzke, M (February 2020) Deep learning for inferring transcription factor binding sites. Current Opinion in Systems Biology, 19. pp. 16-23. ISSN 2452-3100

Koo, Peter, Majdandzic, Antonio, Ploenzke, Matthew, Anand, Praveen, Paul, Steffan (September 2020) Global Importance Analysis: An Interpretability Method to Quantify Importance of Genomic Features in Deep Neural Networks. BioRxiv. (Unpublished)

Koo, Peter K, Majdandzic, Antonio, Ploenzke, Matthew, Anand, Praveen, Paul, Steffan B (May 2021) Global importance analysis: An interpretability method to quantify importance of genomic features in deep neural networks. PLoS Computational Biology, 17 (5). e1008925. ISSN 1553-7358

Koo, Peter K, Ploenzke, Matt (March 2021) Improving representations of genomic sequence motifs in convolutional networks with exponential activations. Nature Machine Intelligence, 3 (3). pp. 258-266. ISSN 2522-5839

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)

Mitra, PP (May 2021) Fitting elephants in modern machine learning by statistically consistent interpolation. Nature Machine Intelligence, 3 (5). pp. 378-386. ISSN 2522-5839

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)

Navlakha, Saket, Morjaria, Sejal, Perez-Johnston, Rocio, Zhang, Allen, Taur, Ying (May 2021) Projecting COVID-19 disease severity in cancer patients using purposefully-designed machine learning. BMC Infectious Diseases, 21 (1). p. 391. ISSN 1471-2334

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

S

Sharma, Ashika, Jayakumar, Jaikishan, Mitra, Partha P, Chakraborti, Sutanu, Kumar, P Sreenivasa (June 2021) Application of Supervised Machine Learning to Extract Brain Connectivity Information from Neuroscience Research Articles. Interdisciplinary Sciences: Computational Life Sciences. ISSN 1913-2751

Shuvaev, Sergey A, Tran, Ngoc B, Stephenson-Jones, Marcus, Li, Bo, Koulakov, Alexei A (January 2021) Neural Networks With Motivation. Frontiers in Systems Neuroscience, 14. p. 609316. ISSN 1662-5137

Silva, Talita M, Borniger, Jeremy C, Alves, Michele Joana, Alzate Correa, Diego, Zhao, Jing, Fadda, Paolo, Toland, Amanda Ewart, Takakura, Ana C, Moreira, Thiago S, Czeisler, Catherine M, Otero, José Javier (April 2021) Machine learning approaches reveal subtle differences in breathing and sleep fragmentation in Phox2b-derived astrocytes ablated mice. Journal of Neurophysiology, 125 (4). pp. 1164-1179. ISSN 0022-3077

T

Tareen, Ammar, Kinney, Justin (November 2019) Biophysical models of cis-regulation as interpretable neural networks. BioRxiv. (Unpublished)

Tareen, Ammar, Posfai, Anna, Ireland, William, McCandlish, David, Kinney, Justin (July 2020) MAVE-NN: learning genotype-phenotype maps from multiplex assays of variant effect. BioRxiv. (Unpublished)

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 Mon Oct 18 03:59:29 2021 EDT.
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