Recommended papers for CPSC/AMTH/CBB 745 - Advanced Topics in Machine Learning & Data Mining - Spring 2017 Yale
Yale University CPSC745 - S2017


Advanced Topics in Machine Learning & Data Mining

Spring 2017

Alex Cloninger ♦ Smita Krishnaswamy ♦ Guy Wolf

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Recommended papers

The following is a tentative list of recommended papers for student presentations:

1.   Babak Alipanahi, Andrew Delong, Matthew T Weirauch, and Brendan J Frey. Predicting the sequence specificities of dna-and rna-binding proteins by deep learning. Nature biotechnology, 2015.

2.   El-ad David Amir, Kara L Davis, Michelle D Tadmor, Erin F Simonds, Jacob H Levine, Sean C Bendall, Daniel K Shenfeld, Smita Krishnaswamy, Garry P Nolan, and Dana Pe’er. visne enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nature biotechnology, 31(6):545–552, 2013.

3.   Alexandre Barachant, Stéphane Bonnet, Marco Congedo, and Christian Jutten. Classification of covariance matrices using a riemannian-based kernel for bci applications. Neurocomputing, 112:172–178, 2013.

4.   Laurent Bonnet, Fabien Lotte, and Anatole Lécuyer. Two brains, one game: design and evaluation of a multiuser bci video game based on motor imagery. IEEE Transactions on Computational Intelligence and AI in games, 5(2):185–198, 2013.

5.   Pablo G Camara, Daniel IS Rosenbloom, Kevin J Emmett, Arnold J Levine, and Raul Rabadan. Topological data analysis generates high-resolution, genome-wide maps of human recombination. Cell Systems, 3(1):83–94, 2016.

6.   Ronald R Coifman, Yoel Shkolnisky, Fred J Sigworth, and Amit Singer. Graph laplacian tomography from unknown random projections. IEEE Transactions on Image Processing, 17(10):1891–1899, 2008.

7.   Carl Doersch. Tutorial on variational autoencoders. arXiv preprint arXiv:1606.05908, 2016.

8.   Carmeline J Dsilva, Ronen Talmon, Ronald R Coifman, and Ioannis G Kevrekidis. Parsimonious representation of nonlinear dynamical systems through manifold learning: A chemotaxis case study. Applied and Computational Harmonic Analysis, 2015.

9.   Dominique Duncan, Ronen Talmon, Hitten P Zaveri, and Ronald R Coifman. Identifying preseizure state in intracranial EEG data using diffusion kernels. Mathematical Biosciences and Engineering, 10(3):579–590, 2013.

10.   Laleh Haghverdi, Maren Buettner, F Alexander Wolf, Florian Buettner, and Fabian J Theis. Diffusion pseudotime robustly reconstructs lineage branching. bioRxiv, page 041384, 2016.

11.   Tatsunori Hashimoto, David Gifford, and Tommi Jaakkola. Learning population-level diffusions with generative rnns. In Proceedings of The 33rd International Conference on Machine Learning, pages 2417–2426, 2016.

12.   Ran Kafri, Jason Levy, Miriam B Ginzberg, Seungeun Oh, Galit Lahav, and Marc W Kirschner. Dynamics extracted from fixed cells reveal feedback linking cell growth to cell cycle. Nature, 494(7438):480–483, 2013.

13.   Jared Katzman, Uri Shaham, Alexander Cloninger, Jonathan Bates, Tingting Jiang, and Yuval Kluger. Deep survival: A deep cox proportional hazards network. arXiv preprint arXiv:1606.00931, 2016.

14.   Diederik P Kingma and Max Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013.

15.   Smita Krishnaswamy, Matthew H Spitzer, Michael Mingueneau, Sean C Bendall, Oren Litvin, Erica Stone, Dana Peer, and Garry P Nolan. Conditional density-based analysis of t cell signaling in single-cell data. Science, 346(6213):1250689, 2014.

16.   Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097–1105, 2012.

17.   Steven Lemm, Benjamin Blankertz, Thorsten Dickhaus, and Klaus-Robert Müller. Introduction to machine learning for brain imaging. Neuroimage, 56(2):387–399, 2011.

18.   Jacob H Levine, Erin F Simonds, Sean C Bendall, Kara L Davis, D Amir El-ad, Michelle D Tadmor, Oren Litvin, Harris G Fienberg, Astraea Jager, Eli R Zunder, et al. Data-driven phenotypic dissection of aml reveals progenitor-like cells that correlate with prognosis. Cell, 162(1):184–197, 2015.

19.   Polina Mamoshina, Armando Vieira, Evgeny Putin, and Alex Zhavoronkov. Applications of deep learning in biomedicine. Molecular Pharmaceutics, 13(5):1445–1454, 2016.

20.   Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems, pages 3111–3119, 2013.

21.   Riccardo Miotto, Li Li, Brian A Kidd, and Joel T Dudley. Deep patient: An unsupervised representation to predict the future of patients from the electronic health records. Scientific reports, 6, 2016.

22.   Andrea Ocone, Laleh Haghverdi, Nikola S Mueller, and Fabian J Theis. Reconstructing gene regulatory dynamics from high-dimensional single-cell snapshot data. Bioinformatics, 31(12):i89–i96, 2015.

23.   Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, and Yoshua Bengio. How to construct deep recurrent neural networks. arXiv preprint arXiv:1312.6026, 2013.

24.   Alkes L Price, Nick J Patterson, Robert M Plenge, Michael E Weinblatt, Nancy A Shadick, and David Reich. Principal components analysis corrects for stratification in genome-wide association studies. Nature genetics, 38(8):904–909, 2006.

25.   Karen Sachs, Omar Perez, Dana Pe’er, Douglas A Lauffenburger, and Garry P Nolan. Causal protein-signaling networks derived from multiparameter single-cell data. Science, 308(5721):523–529, 2005.

26.   Eran Segal, Yvonne Fondufe-Mittendorf, Lingyi Chen, AnnChristine Thåström, Yair Field, Irene K Moore, Ji-Ping Z Wang, and Jonathan Widom. A genomic code for nucleosome positioning. Nature, 442(7104):772–778, 2006.

27.   Manu Setty, Michelle D Tadmor, Shlomit Reich-Zeliger, Omer Angel, Tomer Meir Salame, Pooja Kathail, Kristy Choi, Sean Bendall, Nir Friedman, and Dana Pe’er. Wishbone identifies bifurcating developmental trajectories from single-cell data. Nature biotechnology, 34(6):637–645, 2016.

28.   Uri Shalit, Fredrik Johansson, and David Sontag. Estimating individual treatment effect: generalization bounds and algorithms. arXiv preprint arXiv:1606.03976, 2016.

29.   Daniel Svozil, Vladimir Kvasnicka, and Jiri Pospichal. Introduction to multi-layer feed-forward neural networks. Chemometrics and intelligent laboratory systems, 39(1):43–62, 1997.

30.   Laurens Van Der Maaten. Accelerating t-sne using tree-based algorithms. Journal of machine learning research, 15(1):3221–3245, 2014.

31.   Alex Van Esbroeck, Landon Smith, Zeeshan Syed, Satinder Singh, and Zahi Karam. Multi-task seizure detection: addressing intra-patient variation in seizure morphologies. Machine Learning, 102(3):309–321, 2016.

32.   Hau-tieng Wu, Ronen Talmon, and Yu-Lun Lo. Assess sleep stage by modern signal processing techniques. IEEE Transactions on Biomedical Engineering, 62(4):1159–1168, 2015.

33.   Rui Xu, Steven Damelin, Boaz Nadler, and Donald C Wunsch. Clustering of high-dimensional gene expression data with feature filtering methods and diffusion maps. Artificial intelligence in medicine, 48(2):91–98, 2010.

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