機械学習と遺伝的アルゴリズムを用いたエンハンサーの同定とモデル化

过去的一年一直致力于通过使用递归神经网络来尝试和识别完整人类基因组中的增强子区域。通过考虑整个基因组，而不仅仅是一些非常有限和特定的区域，希望能对这些区域达成更全面的理解，并确定一些未知的增强子。结果虽然明显好于随机结果，但并不是那么好。最先进的增强子识别成功率达到90%以上，但我们的结果徘徊在60%左右。虽然这肯定是一个未来可以解决的问题，但可用的相对较小的数据集(与基因组的大小相反)使得当前的机器学习技术太难奏效：它们既需要强有力的基础事实，也需要大型数据集。可悲的是，希望现有的数据足够与现实不符。从那时起，研究发展到了一个略有不同的方向，目标是能够识别哪些细胞系中哪种增强子是有效的。我们相信，我们使用遗传算法研究基序发现问题的经验将在这个方向上有效，因此我们将在剩余的任期内探索这一可能性。

The past year has been dedicated to try and identify enhancer regions in the complete human genome by using recurrent neural networks. By taking the whole genome in consideration, and not just some very limited and specific regions, the hope was to reach a more comprehensive understanding of those regions and identify some as of yet unknownenhancers.The results, while significantly better than random, were not as good ashoped. State of the art enhancer identification reaches a success rate of more than 90%, but our results hovered around 60%. While it is definitely a problem that could be tackled in the future, the relatively small dataset available (in opposition to the size of the genome) madeit too difficult for the current machine learning techniques to work: they require both strong ground truth and a big dataset. Sadly the hope that the available data would be enough didn't match reality.The research as since then evolved into a slightly different direction, aiming at being able to identify which enhancer is active in which cell lines. We believe that our experience for studying the motif finding problem using the genetic algorithm would be effective in this direction and thus we will explore this possibility during the remaining term.