Items where Subject is "machine learning"

Paper

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, Mikhail, Hsu, Daniel, Mitra, Partha (June 2018) Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate. arXiv. (Submitted)

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)

Biderman, Dan, Whiteway, Matthew R, Hurwitz, Cole, Greenspan, Nicholas, Lee, Robert S, Vishnubhotla, Ankit, Warren, Richard, Pedraja, Federico, Noone, Dillon, Schartner, Michael M, Huntenburg, Julia M, Khanal, Anup, Meijer, Guido T, Noel, Jean-Paul, Pan-Vazquez, Alejandro, Socha, Karolina Z, Urai, Anne E, International Brain Laboratory, Cunningham, John P, Sawtell, Nathaniel B, Paninski, Liam (July 2024) Lightning Pose: improved animal pose estimation via semi-supervised learning, Bayesian ensembling and cloud-native open-source tools. Nature Methods, 21 (7). pp. 1316-1328. ISSN 1548-7091

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)

Carter, J. A., Preall, J. B., Grigaityte, K., Goldfless, S. J., Jeffery, E., Briggs, A. W., Vigneault, F., Atwal, G. S. (July 2019) Single T Cell Sequencing Demonstrates the Functional Role alpha beta TCR Pairing in Cell Lineage and Antigen Specificity. Frontiers in Immunology, 10. Article Number:1516. ISSN 1664-3224

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

Chen, Y., McElvain, L. E., Tolpygo, A. S., Ferrante, D., Friedman, B., Mitra, P. P., Karten, H. J., Freund, Y., Kleinfeld, D. (March 2019) An active texture-based digital atlas enables automated mapping of structures and markers across brains. Nat Methods, 16 (4). pp. 341-350. ISSN 1548-7091

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

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)

Fischer, Stephan, Gillis, Jesse (October 2022) Defining the extent of gene function using ROC curvature. Bioinformatics. btac692. ISSN 1367-4803

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)

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

Hejase, H. A., Salman-Minkov, A., Campagna, L., Hubisz, M. J., Lovette, I. J., Gronau, I., Siepel, A. (December 2020) Genomic islands of differentiation in a rapid avian radiation have been driven by recent selective sweeps. Proc Natl Acad Sci U S A, 117 (48). pp. 30554-30565. ISSN 0027-8424 (Print)0027-8424

Hejase, Hussein A, Mo, Ziyi, Campagna, Leonardo, Siepel, Adam (November 2021) A Deep-Learning Approach for Inference of Selective Sweeps from the Ancestral Recombination Graph. Molecular Biology and Evolution. ISSN 0737-4038

Hu, Haifei, Scheben, Armin, Wang, Jian, Li, Fangping, Li, Chengdao, Edwards, David, Zhao, Junliang (November 2023) Unravelling inversions: Technological advances, challenges, and potential impact on crop breeding. Plant Biotechnology Journal. ISSN 1467-7644

Huang, Yi-Fei, Siepel, Adam (June 2019) Estimation of allele-specific fitness effects across human protein-coding sequences and implications for disease. Genome Research, 29 (8). pp. 1310-1321. ISSN 10889051 (ISSN)

Kaczmarzyk, Jakub R, Gupta, Rajarsi, Kurc, Tahsin M, Abousamra, Shahira, Saltz, Joel H, Koo, Peter K (September 2023) ChampKit: A framework for rapid evaluation of deep neural networks for patch-based histopathology classification. Computer Methods and Programs in Biomedicine, 239. p. 107631. ISSN 0169-2607

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)

Klindt, David A, Hyvärinen, Aapo, Levy, Axel, Miolane, Nina, Poitevin, Frédéric (July 2024) Towards interpretable Cryo-EM: disentangling latent spaces of molecular conformations. Frontiers in Molecular Biosciences, 11. p. 1393564. ISSN 2296-889X

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

Majdandzic, Antonio, Koo, Peter K (May 2022) Statistical correction of input gradients for black box models trained with categorical input features. BioRxiv. (Unpublished)

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, Partha P (March 2018) Fast Convergence for Stochastic and Distributed Gradient Descent in the Interpolation Limit. (Submitted)

Mitra, Partha P (March 2021) Fitting Elephants. arXiv. (Submitted)

Mitra, Partha P, Sire, Clément (December 2023) AI without networks. bioRxiv. (Submitted)

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

Mo, Ziyi, Siepel, Adam (September 2023) Domain-adaptive neural networks improve supervised machine learning based on simulated population genetic data. bioRxiv. (Submitted)

Mo, Ziyi, Siepel, Adam (November 2023) Domain-adaptive neural networks improve supervised machine learning based on simulated population genetic data. PLoS Genetics, 19 (11). e1011032. ISSN 1553-7390

Molik, David C, Tomlinson, DeAndre, Davitt, Shane, Morgan, Eric L, Sisk, Matthew, Roche, Benjamin, Meyers, Natalie, Pfrender, Michael E (April 2021) Combining natural language processing and metabarcoding to reveal pathogen-environment associations. PLoS Neglected Tropical Diseases, 15 (4). e0008755. ISSN 1935-2735

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

Patel, H, Zanos, T, Hewitt, DB (January 2024) Deep Learning Applications in Pancreatic Cancer. Cancers, 16 (2). p. 436. ISSN 2072-6694

Reizinger, Patrik, Bizeul, Alice, Juhos, Attila, Vogt, Julia, Balestriero, Randall, Brendel, Wieland, Klindt, David (October 2024) Cross-Entropy Is All You Need To Invert the Data Generating Process. arXiv. ISSN 2331-8422 (Submitted)

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

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

Shen, Yang, Wang, Julia, Navlakha, Saket (August 2021) A Correspondence between Normalization Strategies in Artificial and Biological Neural Networks. Neural Computation. pp. 1-25. ISSN 0899-7667

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

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)

Toneyan, Shushan, Tang, Ziqi, Koo, Peter K (December 2022) Evaluating deep learning for predicting epigenomic profiles. Nature Machine Intelligence, 4 (12). pp. 1088-1100. ISSN 2522-5839

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.

Weinstein, Jonathan Yaacov, Martí-Gómez, Carlos, Lipsh-Sokolik, Rosalie, Hoch, Shlomo Yakir, Liebermann, Demian, Nevo, Reinat, Weissman, Haim, Petrovich-Kopitman, Ekaterina, Margulies, David, Ivankov, Dmitry, McCandlish, David M, Fleishman, Sarel J (May 2023) Designed active-site library reveals thousands of functional GFP variants. Nature Communications, 14 (1). p. 2890. ISSN 2041-1723

Yang, T, Alessandri-Haber, N, Fury, W, Schaner, M, Breese, R, LaCroix-Fralish, M, Kim, J, Adler, C, Macdonald, LE, Atwal, GS, Bai, Y (October 2021) AdRoit is an accurate and robust method to infer complex transcriptome composition. Communications Biology, 4 (1). ISSN 2399-3642 (In Press)

Yu, Yiyang, Muthukumar, Shivani, Koo, Peter K (February 2024) EvoAug-TF: Extending evolution-inspired data augmentations for genomic deep learning to TensorFlow. Bioinformatics. ISSN 1367-4811

Zhu, Jiening, Oh, Jung Hun, Simhal, Anish K, Elkin, Rena, Norton, Larry, Deasy, Joseph O, Tannenbaum, Allen (September 2023) Geometric graph neural networks on multi-omics data to predict cancer survival outcomes. Computers in Biology and Medicine, 163. p. 107117. ISSN 0010-4825

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

Ziamtsov, I., Navlakha, S. (March 2020) Plant 3D (P3D): A Plant Phenotyping Toolkit for 3D Point Clouds. Bioinformatics. ISSN 1367-4803 (Public Dataset)

Conference or Workshop Item

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.

Book

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)

Thesis

O’Neill, Kathryn Shea (June 2022) Investigations into the contribution of retrotransposon activation in neurodegenerative disease. PhD thesis, Cold Spring Harbor Laboratory.

Ziyi, Mo (January 2024) Scalable and robust deep-learning methods power evolutionary-genetic studies of biobank-scale population genomic data. PhD thesis, Cold Spring Harbor Laboratory.