Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A eukaryotic gene consists of multiple exons (protein coding regions) and introns (non-coding regions), and a splice junction refers to the boundary beten a pair of exon and intron. Precise identification of spice junctions on a gene is important for deciphering its primary structure, function, and interaction. Experimental techniques for determining exon/intron boundaries include RNA-seq, which is often accompanied by computational approaches. Canonical splicing signals are known, but computational junction prediction still remains challenging because of a large number of false positives and other complications. ,  exploit deep recurrent neural networks (RNNs) to model DNA sequences and to detect splice junctions thereon.  test various RNN units and architectures including long short-term memory units, gated recurrent units, and recently proposed iRNN for in-depth design space exploration. According to our experimental results, the proposed approach significantly outperforms not only conventional machine learning-based methods but also a recent state-of-the-art deep belief network-based technique in terms of prediction accuracy.
['Byunghan Lee', 'Taehoon Lee', 'Byunggook Na', 'Sungroh Yoon']
I am writing a research paper on a idea DNA-Level Splice Junction Prediction using Deep Recurrent Neural Networks
[{"from": "human", "value": "I am writing a research paper on a idea DNA-Level Splice Junction Prediction using Deep Recurrent Neural Networks"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A eukaryotic gene consists of multiple exons (protein coding regions) and introns (non-coding regions), and a splice junction refers to the boundary beten a pair of exon and intron. Precise identification of spice junctions on a gene is important for deciphering its primary structure, function, and interaction. Experimental techniques for determining exon/intron boundaries include RNA-seq, which is often accompanied by computational approaches. Canonical splicing signals are known, but computational junction prediction still remains challenging because of a large number of false positives and other complications. ,  exploit deep recurrent neural networks (RNNs) to model DNA sequences and to detect splice junctions thereon.  test various RNN units and architectures including long short-term memory units, gated recurrent units, and recently proposed iRNN for in-depth design space exploration. According to our experimental results, the proposed approach significantly outperforms not only conventional machine learning-based methods but also a recent state-of-the-art deep belief network-based technique in terms of prediction accuracy."}]