Here I am outlining the ideas for a potential workshop paper for NIPS 2016.

Paper ideas:

1st paper

• compare FFN + index encoding vs. FFN(seq[:4] + seq[-4:]) vs. LSTM
• the effect of learning rates on all these architectures

2nd paper

• pimp LSTM so that it comes close to or outperforms FFNN.

3rd paper

• apply LSTM to MHC class II prediction

Things to include in the paper(s)

• a page of exposition on the data + problem
• the challenge of reducing short sequences into fixed input size models
• talk about how LSTM would be ideal, and why it is a candidate
• the importance of the learning rate

Some random ideas:

• view LSTM as encoding technique, get LSTM to mimick kmer_index_encoding() to get comparabale result, then tweak it to beat it.
• add attentional gate to LSTM
• consider deep LSTMs
• check what is going on with forget, and input gate.