Spectral Learning of Large Structured HMMs for Comparative Epigenomics
Authors: Chicheng Zhang, Jimin Song, Kamalika Chaudhuri, Kevin Chen
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide sample complexity bounds for our algorithm and evaluate it experimentally on biological data from nine human cell types. Finally we implement our algorithm and evaluate it on biological data from nine human cell types [7]. |
| Researcher Affiliation | Academia | Chicheng Zhang UC San Diego chz038@eng.ucsd.edu Jimin Song Rutgers University song@dls.rutgers.edu Kevin C Chen Rutgers University kcchen@dls.rutgers.edu Kamalika Chaudhuri UC San Diego kamalika@eng.ucsd.edu |
| Pseudocode | Yes | Algorithm 1 shows how to recover the observation matrices Ou at each node u. Once the Ous are recovered, one can use standard techniques to recover T and W; details are described in Algorithm 2 in the Appendix. ... (See Algorithm 3 in the Appendix for details). |
| Open Source Code | No | The paper does not provide any statement about open-sourcing the code or a link to a code repository. |
| Open Datasets | Yes | We ran our algorithm, which we call Spectacle-Tree , on a chromatin dataset on human chromosome 1 from nine cell types (H1-h ESC, GM12878, Hep G2, HMEC, HSMM, HUVEC, K562, NHEK, NHLF) from the ENCODE project [7]. |
| Dataset Splits | No | The paper describes the dataset used and evaluation against CAGE data for promoter prediction, but it does not specify explicit training, validation, and test dataset splits with percentages or sample counts. |
| Hardware Specification | No | The paper mentions that their Python implementation ran out of memory and that Spectacle-Tree was implemented in Matlab, but it does not specify any hardware details like CPU/GPU models, memory, or specific computing environments. |
| Software Dependencies | No | The paper mentions |
| Experiment Setup | Yes | We set the number of hidden states, which we interpret as chromatin states, to m = 6, similar to the choice of ENCODE. |