I recently read
your 2003 paper titled "Spectral biclustering of microarray data: Coclustering
genes and conditions".
I would like to investigate implementing your approach on a GPU.
Is there any code (Matlab? Python?) you would be willing to share as a result of the paper?
Sorry we’re just using simple SVD routines in matlab. No meaningful code available. -marK
I just read your recently published paper on OrthoClust approach. It is a well grounded work in both practically and mathematically point of views.
I ran your R scripts for my own data and It worked perfectly fine, however I am wondering how can I use the script for more than two species?
It could be appreciated if you help me to find the solution.
Thanks for your interest in OrthoClust. Orthoclust definitely works on more than 2. The R script is a primitive version for illustrating the concept outlined in the paper. We understand the importance of N-species generalization. We have put a new MATLAB code for N-species. It made use of an efficient code written by Mucha and Porter that implemented the Louvain algorithm for modularity optimization. The 3rd party code as well as our wrapper is now in the gersteinlab github.
Apart from MATLAB, we are planning to provide wrapper for Python or R later.
The N-species code is not exactly the thing we did for the paper. So if you find any bug or question, please let me know. we are trying to make a more user friendly package anyway.
I came across your paper ‘The GENCODE pseudogene resource’.
It is a great paper.
Could you please tell me where I would be able to find the list of psuedogenes mentioned in the paper?
I didn’t find any downloadable database in the paper.
see psicube.pseudogene.org & http://pseudogene.org/psidr
As describe in your paper entitled "Classification of human genomic regions based on experimentally determined binding sites of more than 100 transcription-related factors", it is mentioned "we identified 13,539 potential enhancers (full list available in the Additional files), among which 50 were randomly chosen". But in the additional files, only 50 enhancer co-ordinates are mentioned. Can you please provide me either the source/list of the all 13,539 enhancers.
Many thanks in anticipation of your quick reply,
I recently have been working on constructing human regulatory networks. After reading your paper <Genomic analysis of the hierarchical structure of regulatory networks> published on PNAS, I found it very amazing and useful, which may be applied for my study. I want to construct hierarchical structure of transcription factors (TFs) in humans, and my data is the expression level of these TFs and their targets obtained by RNA sequencing. Can we use your BFS method to construct the network? As we know little about the computational algorithm of BFS, would you please provide related scripts or equations for implementing it easily?
Thank you very much for occupying your precious time reading my letter and I’m looking forward to your guidance.
Hi, see http://info.gersteinlab.org/Hierarchy
I work for Novartis Institutes For Biomedical Research Inc, in Cambridge, MA, a commercial entity.
Could you use your PEMer software ? ( I remember you allowed me to try your translocation detection software in 2011, but I did not archive that email in 2011?
this is fine.
I am currently working on
a network science project studying properties of heterogenous networks and greatly intrigued by your 2011 paper in PLoS
Construction and Analysis of an Integrated Regulatory Network Derived from High-Throughput Sequencing Data
I am planning to employ the integrated human TF-miRNA-gene regulatory network constructed in your work to verify the
utility of information flow – based techniques in understanding the mapping between network topology and function. However,
the full network does not appear to be available in the supplementary information. I am writing to kindly ask if I could obtain
a copy of the human network data (e.g. CSV format edge list) for my research. I would be more than honored to be able to use
the original dataset in my work, and my apologies if it is against your plans to disclose it or it is available somewhere else that
I am not aware of. Thank you very much!
you can certainly get the network.
The data behind it and a closely related network is available from :
(see website links)