2022年8月19日 · To facilitate the discovery of novel target (combinations), we developed DSCN (double-target selection guided by CRISPR screening and network) that utilize single target-level CRISPR screening data and expression profiles for predicting target combinations by connecting cell-line omics-data with tissue omics-data.

全局状态管理是构建复杂 Tauri 2.0 应用程序的关键。 本文深入探讨了 Tauri 2.0 中的全局状态管理，介绍了 Tauri 2.0 的内置状态管理机制以及如何使用第三方状态管理库。

To facilitate the discovery of novel tar-get (combinations), we developed DSCN (double-target selection guided by CRISPR screening and network) that utilize single target-level CRISPR screening data and expres-sion profiles for predicting target combinations by connecting cell-line omics-data with tissue omics-data.

2021年9月6日 · To facilitate the prediction of target combinations, we developed DSCN (double-target selection guided by CRISPR screening and network) that utilize single target-level CRISPR screening data and expression profiles for predicting target combinations by connecting cell-line omics-data with tissue omics-data.

2022年8月19日 · In DSCN, a sub-sampling approach is developed to model first-target knockdown and its impact on the PPI network, and it also facilitates the selection of a second target.

该篇论文利用Siamese网络作为特征提取网络，为了利用像素点之间的信息关系添加了 KNN 算法 用来提高变化检测性能。 由于本篇论文提出时间较早，很多方法已经不适用，但其思想指的借鉴。 该篇论文提出的损失 函数 添加了频率用来解决数据不平衡（变化的像素点数量往往远小于未变化的像素点数量），在利用特征网络进行特征提取后添加了KNN和使用 Transformer 、 CBAM 都是为了更加精细的寻找变化点。 举个例子，一个地方一年前没有楼但现在有了一栋楼，在一年 …

2022年8月19日 · We have therefore developed DSCN (double-target selection guided by CRISPR screening and network), a method that matches expression levels in patients and gene essentialities in cell lines through spectral-clustered protein-protein interaction (PPI) network.

DSCN：由CRISPR筛选和网络引导的双目标选择 bioRxiv - BioinformaticsPub Date : 2021-09-06, DOI: 10.1101/2021.09.06.459081 Enze Liu , Xue Wu , Lei Wang , Yang Huo , Lang Li , Lijun Cheng

2024年6月6日 · 本文介绍了YOLOv10的改进版，独创可变形条带卷积 (DSCN)，作为轻量级DCNv3的替代方案，减少了计算量，提高了性能。 DSCN通过限制变形采样核在单轴上，降低了计算负荷。

To improve the model accuracy of deep stochastic configuration network (DSCN) in multitarget robust parameter modeling tasks, this paper presents a multitarget