Q:
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.

A:
Hi, see http://info.gersteinlab.org/Hierarchy

Q:
I am currently working on
a network science project studying properties of heterogenous networks and greatly intrigued by your 2011 paper in PLoS
Computational Biology:

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!

A:
you can certainly get the network.

The data behind it and a closely related network is available from :

http://papers.gersteinlab.org/papers/mirnet

http://papers.gersteinlab.org/papers/wormawg

(see website links)

Q:
I am trying to construct a "gene co-phenotype" background network using bayesian approach which is mentioned in your paper "A Bayesian Networks Approach for Predicting Protein-Protein Interactions from Genomic Data" (science ,17th October 2003).

After reading the supplementary method related with this paper, I have a question on how to set the training data set size.
In this paper, 8250 positive /2691903 negative training gene pairs are used. It is recommended that the training data set should be balance with the true situation when we use naive bayesian method. Could you give me some instruvtions on how you set the positive/negative training dataset size. It will be very glad to hear from you.

A:

best I can do here is point you to:
http://papers.gersteinlab.org/papers/funcpred-goldstd