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Currently I'm working on a project, which combines deep learning with RNA sequences. I'll try to predict pseudotorsion angles [1] from raw rna sequence. The ideas is to train a neural network with raw rna sequences and for each nucleotide their corresponding pseudotorsion angles, and than to predict the angles of the remaining sequences in the test set.

How my data is structured:
example:

seq1: A C G G U A C
Eta: 169 87 110 87 45 187 78
Theta: 123 10 45 168 132 34 100 

[1] These are angles describing the backbone conformation of a rna molecule.

I'm pretty new to the field of deep learning, and so far I build a simple feedforward neural network, but it's prediction accuracy is pretty low with only one percent.

Has anyone some tips for me how to improve this?
How do I preprocess this kind of data correctly for deep learning?

I appreciate any help.

The question of folding of RNA sequences looks slightly similar to protein folding - perhaps searching in this domain might bring more suggestions.

An example of (current) state of the art of deep learning approach to protein folding is AlphaFold developed by DeepMind for CASP competition . They basically decompose the angles to an image representation and look for structures in the proximity of each atom. While it requires a significant processing power, RNA structures might be easier due to the size of the sequence.

I come from a protein background and this problem is analogous to protein torsion angle prediction, which in turn is a variant of protein secondary structure prediction.

Conventional ways to go here would be to use a sliding window of x bases either side to make a prediction for each point, or use a CNN/LSTM-type model to run over the whole sequence and output angles.

There are other technical concerns such as how to split the training and test sets, how to output the angles from the model (usually transformed by sin or cos), and whether to predict a single value or the probability of the value being in certain ranges.

The reference to protein tertiary prediction and CASP in another answer isn't directly relevant here unless you intend to build 3D models from your torsion angles, which you might very well do.

Some references on torsion angle prediction for proteins:

• 
  A recent review.


• 
  A recent state of the art method.


• 
  A classic paper describing some of the approaches.

I think the solution is more in the DL.

I would look at simpler ML, using a random forest as a starting point and then maybe SVC. If you get some reasonable predictive power on this then move towards neural network/Tensorflow type calculation.