Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) is a gene
editing technology that has revolutionized the fields of biology and medicine.
However, one of the challenges of using CRISPR is predicting the on-target
efficacy and off-target sensitivity of single-guide RNAs (sgRNAs). This is
because most existing methods are trained on separate datasets with different
genes and cells, which limits their generalizability. In this paper, we propose
a novel ensemble learning method for sgRNA design that is accurate and
generalizable. Our method combines the predictions of multiple machine learning
models to produce a single, more robust prediction. This approach allows us to
learn from a wider range of data, which improves the generalizability of our
model. We evaluated our method on a benchmark dataset of sgRNA designs and
found that it outperformed existing methods in terms of both accuracy and
generalizability. Our results suggest that our method can be used to design
sgRNAs with high sensitivity and specificity, even for new genes or cells. This
could have important implications for the clinical use of CRISPR, as it would
allow researchers to design more effective and safer treatments for a variety
of diseases.

该论文提出了一种新的合奏学习方法来设计单导 RNA，该方法能够准确且具有一般性地预测 CRISPR 的靶向有效性和离靶敏感性，从而改善对不同基因和细胞数据集的泛化能力。通过在一个基准数据集上进行评估，发现该方法在准确性和泛化能力方面优于现有方法，并且可以用于设计具有高灵敏度和特异性的单导 RNA，甚至对于新基因或细胞，这对于临床使用 CRISPR 技术提供了重要的启示，因为它使研究人员能够设计更有效和更安全的治疗方法来对抗各种疾病。