Reviewer response for version 1
The authors presented yet another neural network-based disorder prediction tool written in R, trained on PDB data and benchmarked on CASP10 dataset and they claim that the tool outperforms other existing tools in terms of both calculation speed and performance.
We tried using the tool for predicting the known disordered sequences and found that the predictions are accurate and similar to other tools such as IUPRED, DISOPRED3 servers for the well-known disordered sequences such as p53 and Histatin5.
In terms of concerns, I have following comments to make:
- In general, I find the paper does not describe the motivation, methods and the results in a self-sufficient manner and these could be elaborated further.
- As the authors state in the paper, there are over 60 tools already existing for disorder prediction.T
The justification for requiring another tool is not clearly stated.
- The authors mention they have used PDB data for training the neural network. 
Do they take all the currently available PDB datasets for training? 
Does any overlap exist between the datasets trained and benchmarked? 
The reason why I am asking this is the CASP10 dataset that the authors used for benchmarking has been released in 2012, which would be a subset of the training PDB dataset if they have taken all the PDB data published till date.
- The authors claim that their method is faster than the existing methods. 
It would be nice to provide evidence towards that and provide some benchmarking data.
- AUC and balance accuracy are the two metrics used for evaluating the performance of the tool. 
However, a clear definition of these terms are not described in the method section.
- The tool should be tested and bench marked against a larger data set such as Disorder-723, which contains 723 disorder sequences.
Structure
Recap
Strength
Structure
Weakness
Recap
Weakness
Recap
Todo
Todo
Other
Recap
Todo
Recap
Weakness
Todo