Motivation: The diagnosis and monitoring of Castrate Resistant Prostate
Cancer (CRPC) are crucial for cancer patients, but the current models (such as
P-NET) have limitations in terms of parameter count, generalization, and cost.
Results: To address the above issues, we develop a more accurate and efficient
Prostate Cancer patient condition prediction model, named PR-NET. By
compressing and optimizing the network structure of P-NET, the model complexity
is reduced while maintaining high accuracy and interpretability. The PR-NET
demonstrated superior performance in predicting prostate cancer patient
outcomes, outshining P-NET and six other traditional models with a significant
margin. In our rigorous evaluation, PR-NET not only achieved impressive average
AUC and Recall scores of 0.94 and 0.83, respectively, on known data but also
maintained robust generalizability on five unknown datasets with a higher
average AUC of 0.73 and Recall of 0.72, compared to P-NET's 0.68 and 0.5.
PR-NET's efficiency was evidenced by its shorter average training and inference
times, and its gene-level analysis revealed 46 key genes, demonstrating its
enhanced predictive power and efficiency in identifying critical biomarkers for
prostate cancer. Future research can further expand its application domains and
optimize the model's performance and reliability.

通过压缩和优化 P-NET 的网络结构，我们开发了一种更准确高效的前列腺癌患者病情预测模型 PR-NET，该模型在预测前列腺癌患者的结果方面表现出色，相比 P-NET 和其他六种传统模型有显著的优势，并显示出在识别前列腺癌关键生物标志物方面的增强预测能力和效率。

PR-NET：利用经过调整的途径网络结构进行前列腺癌患者状况预测

PR-NET: Leveraging Pathway Refined Network Structures for Prostate  Cancer Patient Condition Prediction

Point set registration is defined as a process to determine the spatial
transformation from the source point set to the target one. Existing methods
often iteratively search for the optimal geometric transformation to register a
given pair of point sets, driven by minimizing a predefined alignment loss
function. In contrast, the proposed point registration neural network (PR-Net)
actively learns the registration pattern as a parametric function from a
training dataset, consequently predict the desired geometric transformation to
align a pair of point sets. PR-Net can transfer the learned knowledge (i.e.
registration pattern) from registering training pairs to testing ones without
additional iterative optimization. Specifically, in this paper, we develop
novel techniques to learn shape descriptors from point sets that help formulate
a clear correlation between source and target point sets. With the defined
correlation, PR-Net tends to predict the transformation so that the source and
target point sets can be statistically aligned, which in turn leads to an
optimal spatial geometric registration. PR-Net achieves robust and superior
performance for non-rigid registration of point sets, even in presence of
Gaussian noise, outliers, and missing points, but requires much less time for
registering large number of pairs. More importantly, for a new pair of point
sets, PR-Net is able to directly predict the desired transformation using the
learned model without repetitive iterative optimization routine. Our code is
available at this https URL

该论文介绍了一种基于神经网络的点集配准方法 PR-Net，该方法通过学习训练数据集中的点集的形状描述符，预测了点集之间的空间变换，以最优的方式配准点集，具有较强的鲁棒性，并可以应用于非刚性配准，对于新的点集配准，可以直接使用训练好的模型预测所需的变换。