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

在过去的一年里，我们一直致力于通过使用循环神经网络来识别完整的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，希望对这些区域有更全面的了解，并确定一些未知的增强子。结果虽然明显好于随机，但并不像希望的那样好。最先进的增强剂鉴定成功率达到90%以上，但我们的结果徘徊在60%左右。虽然这绝对是一个可以在未来解决的问题，但相对较小的可用数据集（与基因组的大小相反）使得当前的机器学习技术难以工作：它们需要强大的基础事实和大数据集。遗憾的是，人们希望现有的数据足够多，但这与现实不符。从那时起，研究方向略有不同，目的是能够确定哪种增强子在哪种细胞系中是活跃的。我们相信我们使用遗传算法研究motif查找问题的经验在这个方向上是有效的，因此我们将在剩余的学期中探索这种可能性。

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.