Recently, drug repurposing has emerged as an effective and resource-efficient
paradigm for AD drug discovery. Among various methods for drug repurposing,
network-based methods have shown promising results as they are capable of
leveraging complex networks that integrate multiple interaction types, such as
protein-protein interactions, to more effectively identify candidate drugs.
However, existing approaches typically assume paths of the same length in the
network have equal importance in identifying the therapeutic effect of drugs.
Other domains have found that same length paths do not necessarily have the
same importance. Thus, relying on this assumption may be deleterious to drug
repurposing attempts. In this work, we propose MPI (Modeling Path Importance),
a novel network-based method for AD drug repurposing. MPI is unique in that it
prioritizes important paths via learned node embeddings, which can effectively
capture a network's rich structural information. Thus, leveraging learned
embeddings allows MPI to effectively differentiate the importance among paths.
We evaluate MPI against a commonly used baseline method that identifies anti-AD
drug candidates primarily based on the shortest paths between drugs and AD in
the network. We observe that among the top-50 ranked drugs, MPI prioritizes
20.0% more drugs with anti-AD evidence compared to the baseline. Finally, Cox
proportional-hazard models produced from insurance claims data aid us in
identifying the use of etodolac, nicotine, and BBB-crossing ACE-INHs as having
a reduced risk of AD, suggesting such drugs may be viable candidates for
repurposing and should be explored further in future studies.

最近，药物再利用作为一种有效和资源高效的方法已经成为 AD 药物发现的一种范例。其中，网络方法在药物再利用中显示出了很好的效果，因为它们能够利用复杂网络来整合多种相互作用类型（如蛋白质相互作用），以更有效地识别候选药物。然而，现有方法通常假设网络中具有相同长度的路径对于识别药物的治疗效果具有相等的重要性。和其他领域的研究发现，相同长度的路径并不一定具有相同的重要性。因此，依赖于这种假设可能对药物再利用尝试造成不利影响。在本研究中，我们提出了一种被称为 MPI（Modeling Path Importance）的新型网络方法用于 AD 药物再利用。MPI 的独特之处在于，它通过学习到的节点嵌入来优先考虑重要路径，这样能够有效地捕捉网络的丰富结构信息。因此，利用学到的嵌入使得 MPI 能够有效区分路径的重要性。我们将 MPI 与一种常用的基准方法进行评估，该方法主要根据网络中药物与 AD 之间的最短路径来识别抗 AD 药物候选物。我们观察到，在排名前 50 的候选药物中，与基准方法相比，MPI 优先考虑了 20.0% 更多具有抗 AD 证据的药物。最后，我们利用保险索赔数据生成的 Cox 比例危险模型有助于我们识别使用依托拉他、尼古丁和越过血脑屏障的 ACE-INHs 可降低 AD 风险，这表明这些药物可能是可行的再利用候选物，并应在未来的研究中进一步探索。

建模重要路径以进行有效的阿尔茨海默病药物再利用

Modeling Path Importance for Effective Alzheimer's Disease Drug  Repurposing

Track one of CTI competition is on click-through rate (CTR) prediction. The
dataset contains millions of records and each field-wise feature in a record
consists of hashed integers for privacy. For this task, the keys of
network-based methods might be type-wise feature extraction and information
fusion across different fields. Multi-layer perceptrons (MLPs) are able to
extract field feature, but could not efficiently fuse features. Motivated by
the natural fusion characteristic of cross attention and the efficiency of
transformer-based structures, we propose simple plug-in mixers for
field/type-wise feature fusion, and thus construct an field&type-wise ensemble
model, namely EMOFM (Ensemble MLP mOdel with Feature-based Mixers). In the
experiments, the proposed model is evaluated on the dataset, the optimization
process is visualized and ablation studies are explored. It is shown that EMOFM
outperforms compared baselines. In the end, we discuss on future work. WARNING:
The comparison might not be fair enough since the proposed method is designed
for this data in particular while compared methods are not. For example, EMOFM
especially takes different types of interactions into consideration while
others do not. Anyway, we do hope that the ideas inside our method could help
other developers/learners/researchers/thinkers and so on.

使用简单的插件混合器构建了基于字段 / 类型的特征融合的集成 MLP 模型 EMOFM（具有特征混合器的集成 MLP 模型），并通过实验验证其优于基线模型的表现。

EMOFM: 带有基于特征混合器的集成 MLP 模型用于点击率预测

EMOFM: Ensemble MLP mOdel with Feature-based Mixers for Click-Through  Rate Prediction

Deep learning has been applied to compressive sensing (CS) of images
successfully in recent years. However, existing network-based methods are often
trained as the black box, in which the lack of prior knowledge is often the
bottleneck for further performance improvement. To overcome this drawback, this
paper proposes a novel CS method using non-local prior which combines the
interpretability of the traditional optimization methods with the speed of
network-based methods, called NL-CS Net. We unroll each phase from iteration of
the augmented Lagrangian method solving non-local and sparse regularized
optimization problem by a network. NL-CS Net is composed of the up-sampling
module and the recovery module. In the up-sampling module, we use learnable
up-sampling matrix instead of a predefined one. In the recovery module,
patch-wise non-local network is employed to capture long-range feature
correspondences. Important parameters involved (e.g. sampling matrix, nonlinear
transforms, shrinkage thresholds, step size, $etc.$) are learned end-to-end,
rather than hand-crafted. Furthermore, to facilitate practical implementation,
orthogonal and binary constraints on the sampling matrix are simultaneously
adopted. Extensive experiments on natural images and magnetic resonance imaging
(MRI) demonstrate that the proposed method outperforms the state-of-the-art
methods while maintaining great interpretability and speed.

本文提出了一种使用非局部先验的 CS 方法，称为 NL-CS Net，通过学习解决非局部和稀疏正则化优化问题的扩展 Lagrangian 方法的每个阶段来解决现有网络方法的不足，其中重要参数是端对端学习的，并且在自然图像和磁共振成像实验中表现出更好的性能。