改善疾病的护理标准是关于更好的治疗方法，反过来依赖于寻找和开发新药。然而，药物发现是一个复杂且昂贵的过程。通过机器学习的方法采用了利用域固有的互连性质的药物发现知识图的创建。基于图形的数据建模，结合知识图形嵌入式提供了更直观的域表示，适用于推理任务，例如预测缺失链路。一个这样的例子将产生对给定疾病的可能相关基因的排名列表，通常被称为目标发现。因此，这是关键的，即这些预测不仅是相关的，而且是生物学上的有意义的。然而，知识图形可以直接偏向，由于集成的底层数据源，或者由于图形构造中的建模选择，其中的一个结果是某些实体可以在拓扑上超越。我们展示了知识图形嵌入模型可能受到这种结构不平衡的影响，导致无论上下文都要高度排名的密集连接实体。我们在不同的数据集，模型和预测任务中提供对此观察的支持。此外，我们展示了如何通过随机，生物学上无意义的信息扰乱图形拓扑结构以人为地改变基因的等级。这表明这种模型可能会受到实体频率而不是在关系中编码的生物学信息的影响，当实体频率不是基础数据的真实反射时，创建问题。我们的结果突出了数据建模选择的重要性，并强调了从业者在解释模型输出和知识图形组合期间时要注意这些问题。

药物发现和发展是一个复杂和昂贵的过程。正在研究机器学习方法，以帮助提高药物发现管道多个阶段的有效性和速度。其中，使用知识图表（kg）的那些在许多任务中具有承诺，包括药物修复，药物毒性预测和靶基因疾病优先级。在药物发现kg中，包括基因，疾病和药物在内的关键因素被认为是实体，而它们之间的关系表示相互作用。但是，为了构建高质量的KG，需要合适的数据。在这篇综述中，我们详细介绍了适用于构建聚焦KGS的药物发现的公开使用来源。我们的目标是帮助引导机器学习和kg从业者对吸毒者发现领域应用新技术，但是谁可能不熟悉相关的数据来源。通过严格的标准选择数据集，根据包含内部包含的主要信息类型，并基于可以提取的信息来进行分类以构建kg。然后，我们对现有的公共药物发现KGS进行了比较分析，并评估了文献中所选择的激励案例研究。此外，我们还提出了众多和与域及其数据集相关的众多挑战和问题，同时突出了关键的未来研究方向。我们希望本综述将激励KGS在药物发现领域的关键和新兴问题中使用。

该药物发现​​和开发过程是一个漫长而昂贵的过程，每次药物平均耗资超过10亿美元，需要10 - 15年的时间。为了减少在整个过程中的高水平流失量，在最近十年中，越来越多地将机器学习方法应用于药物发现和发育的各个阶段，尤其是在最早鉴定可药物疾病基因的阶段。在本文中，我们开发了一种新的张量分解模型，以预测用于治疗疾病的潜在药物靶标（基因或蛋白质）。我们创建了一个三维数据张量，该数据张量由1,048个基因靶标，860个疾病和230,0111111111111111111111111111111的证据属性和临床结果，并使用从开放式目标和药物数据库中提取的数据组成。我们用从药物发现的知识图中学到的基因目标表示丰富了数据，并应用了我们提出的方法来预测看不见的基因靶标和疾病对的临床结果。我们设计了三种评估策略来衡量预测性能，并将几个常用的机器学习分类器与贝叶斯矩阵和张量分解方法进行了基准测试。结果表明，合并知识图嵌入可显着提高预测准确性，并与密集的神经网络一起训练张量分解优于所有其他基线。总而言之，我们的框架结合了两种积极研究的机器学习方法，用于疾病目标识别，即张量分解和知识图表示学习，这可能是在数据驱动的药物发现中进一步探索的有希望的途径。

对于人工智能在生物学和药物中产生更大的影响，这是一个至关重要的是，建议都是准确和透明的。在其他域中，已经显示了关于知识图表的多跳推理的神经统计学方法，以产生透明的解释。然而，缺乏研究将其应用于复杂的生物医学数据集和问题。在本文中，探讨了药物发现的方法，以利用其适用性的稳定结论。我们首次系统地将其应用于多种生物医学数据集和具有公平基准比较的推荐任务。发现该方法以平均水平的21.7％优于21.7％，同时产生新颖，生物学相关的解释。

人蛋白质组包含一个庞大的相互作用激酶和底物网络。即使某些激酶被证明是治疗靶标的非常有用的，但大多数仍在研究中。在这项工作中，我们提出了一种新颖的知识图表示方法，以预测研究研究的新型相互作用伙伴。我们的方法使用通过整合IPTMNET，蛋白质本体论，基因本体论和BIOKG的数据构建的磷蛋白知识图。通过在三元组上进行定向的随机步行，与修改后的Skipgram或CBOW模型一起进行定向的随机步行，从而学习了该知识图中激酶和底物的表示。然后，这些表示形式被用作监督分类模型的输入，以预测研究不细的激酶的新型相互作用。我们还提供了对预测相互作用的后预测分析和对磷酸蛋白质学知识图的消融研究，以了解对研究的激酶的生物学的见解。

To date, there are no effective treatments for most neurodegenerative diseases. Knowledge graphs can provide comprehensive and semantic representation for heterogeneous data, and have been successfully leveraged in many biomedical applications including drug repurposing. Our objective is to construct a knowledge graph from literature to study relations between Alzheimer's disease (AD) and chemicals, drugs and dietary supplements in order to identify opportunities to prevent or delay neurodegenerative progression. We collected biomedical annotations and extracted their relations using SemRep via SemMedDB. We used both a BERT-based classifier and rule-based methods during data preprocessing to exclude noise while preserving most AD-related semantic triples. The 1,672,110 filtered triples were used to train with knowledge graph completion algorithms (i.e., TransE, DistMult, and ComplEx) to predict candidates that might be helpful for AD treatment or prevention. Among three knowledge graph completion models, TransE outperformed the other two (MR = 13.45, Hits@1 = 0.306). We leveraged the time-slicing technique to further evaluate the prediction results. We found supporting evidence for most highly ranked candidates predicted by our model which indicates that our approach can inform reliable new knowledge. This paper shows that our graph mining model can predict reliable new relationships between AD and other entities (i.e., dietary supplements, chemicals, and drugs). The knowledge graph constructed can facilitate data-driven knowledge discoveries and the generation of novel hypotheses.

最近公布的知识图形嵌入模型的实施，培训和评估的异质性已经公平和彻底的比较困难。为了评估先前公布的结果的再现性，我们在Pykeen软件包中重新实施和评估了21个交互模型。在这里，我们概述了哪些结果可以通过其报告的超参数再现，这只能以备用的超参数再现，并且无法再现，并且可以提供洞察力，以及为什么会有这种情况。然后，我们在四个数据集上进行了大规模的基准测试，其中数千个实验和24,804 GPU的计算时间。我们展示了最佳实践，每个模型的最佳配置以及可以通过先前发布的最佳配置进行改进的洞察。我们的结果强调了模型架构，训练方法，丢失功能和逆关系显式建模的组合对于模型的性能来说至关重要，而不仅由模型架构决定。我们提供了证据表明，在仔细配置时，若干架构可以获得对最先进的结果。我们制定了所有代码，实验配置，结果和分析，导致我们在https://github.com/pykeen/pykeen和https://github.com/pykeen/benchmarking中获得的解释

由于对高效有效的大数据分析解决方案的需求，医疗保健行业中数据分析的合并已取得了重大进展。知识图（KGS）已在该领域证明了效用，并且植根于许多医疗保健应用程序，以提供更好的数据表示和知识推断。但是，由于缺乏代表性的kg施工分类法，该指定领域中的几种现有方法不足和劣等。本文是第一个提供综合分类法和鸟类对医疗kg建筑的眼光的看法。此外，还对与各种医疗保健背景相关的学术工作中最新的技术进行了彻底的检查。这些技术是根据用于知识提取的方法，知识库和来源的类型以及合并评估协议的方法进行了严格评估的。最后，报道和讨论了文献中的一些研究发现和现有问题，为这个充满活力的地区开放了未来研究的视野。

生物医学网络是与疾病网络的蛋白质相互作用的普遍描述符，从蛋白质相互作用，一直到医疗保健系统和科学知识。随着代表学习提供强大的预测和洞察的显着成功，我们目睹了表现形式学习技术的快速扩展，进入了这些网络的建模，分析和学习。在这篇综述中，我们提出了一个观察到生物学和医学中的网络长期原则 - 而在机器学习研究中经常出口 - 可以为代表学习提供概念基础，解释其当前的成功和限制，并告知未来进步。我们综合了一系列算法方法，即在其核心利用图形拓扑到将网络嵌入到紧凑的向量空间中，并捕获表示陈述学习证明有用的方式的广度。深远的影响包括鉴定复杂性状的变异性，单细胞的异心行为及其对健康的影响，协助患者的诊断和治疗以及制定安全有效的药物。

多跳跃逻辑推理是在知识图（KGS）上学习领域的一个已建立问题。它涵盖了单跳连接预测以及其他更复杂的逻辑查询类型。现有的算法仅在经典的三重基图上运行，而现代KG经常采用超相关的建模范式。在此范式中，键入的边缘可能具有几对键值对，称为限定符，可为事实提供细粒度的环境。在查询中，此上下文修改了关系的含义，通常会减少答案集。经常在现实世界中的应用程序中观察到超相关的查询，并且现有的近似查询答案方法无法使用预选赛对。在这项工作中，我们弥合了这一差距，并将多跳的推理问题扩展到了超级关系的KG，允许解决这一新类型的复杂查询。在图形神经网络和查询嵌入技术的最新进展之下，我们研究了如何嵌入和回答超相关的连词查询。除此之外，我们还提出了一种回答此类查询并在我们的实验中证明的方法，即预选赛可以改善对各种查询模式的查询回答。

Biomedical knowledge graphs (KG) are heterogenous networks consisting of biological entities as nodes and relations between them as edges. These entities and relations are extracted from millions of research papers and unified in a single resource. The goal of biomedical multi-hop question-answering over knowledge graph (KGQA) is to help biologist and scientist to get valuable insights by asking questions in natural language. Relevant answers can be found by first understanding the question and then querying the KG for right set of nodes and relationships to arrive at an answer. To model the question, language models such as RoBERTa and BioBERT are used to understand context from natural language question. One of the challenges in KGQA is missing links in the KG. Knowledge graph embeddings (KGE) help to overcome this problem by encoding nodes and edges in a dense and more efficient way. In this paper, we use a publicly available KG called Hetionet which is an integrative network of biomedical knowledge assembled from 29 different databases of genes, compounds, diseases, and more. We have enriched this KG dataset by creating a multi-hop biomedical question-answering dataset in natural language for testing the biomedical multi-hop question-answering system and this dataset will be made available to the research community. The major contribution of this research is an integrated system that combines language models with KG embeddings to give highly relevant answers to free-form questions asked by biologists in an intuitive interface. Biomedical multi-hop question-answering system is tested on this data and results are highly encouraging.

Drug repositioning holds great promise because it can reduce the time and cost of new drug development. While drug repositioning can omit various R&D processes, confirming pharmacological effects on biomolecules is essential for application to new diseases. Biomedical explainability in a drug repositioning model can support appropriate insights in subsequent in-depth studies. However, the validity of the XAI methodology is still under debate, and the effectiveness of XAI in drug repositioning prediction applications remains unclear. In this study, we propose GraphIX, an explainable drug repositioning framework using biological networks, and quantitatively evaluate its explainability. GraphIX first learns the network weights and node features using a graph neural network from known drug indication and knowledge graph that consists of three types of nodes (but not given node type information): disease, drug, and protein. Analysis of the post-learning features showed that node types that were not known to the model beforehand are distinguished through the learning process based on the graph structure. From the learned weights and features, GraphIX then predicts the disease-drug association and calculates the contribution values of the nodes located in the neighborhood of the predicted disease and drug. We hypothesized that the neighboring protein node to which the model gave a high contribution is important in understanding the actual pharmacological effects. Quantitative evaluation of the validity of protein nodes' contribution using a real-world database showed that the high contribution proteins shown by GraphIX are reasonable as a mechanism of drug action. GraphIX is a framework for evidence-based drug discovery that can present to users new disease-drug associations and identify the protein important for understanding its pharmacological effects from a large and complex knowledge base.

We present the OPEN GRAPH BENCHMARK (OGB), a diverse set of challenging and realistic benchmark datasets to facilitate scalable, robust, and reproducible graph machine learning (ML) research. OGB datasets are large-scale, encompass multiple important graph ML tasks, and cover a diverse range of domains, ranging from social and information networks to biological networks, molecular graphs, source code ASTs, and knowledge graphs. For each dataset, we provide a unified evaluation protocol using meaningful application-specific data splits and evaluation metrics. In addition to building the datasets, we also perform extensive benchmark experiments for each dataset. Our experiments suggest that OGB datasets present significant challenges of scalability to large-scale graphs and out-of-distribution generalization under realistic data splits, indicating fruitful opportunities for future research. Finally, OGB provides an automated end-to-end graph ML pipeline that simplifies and standardizes the process of graph data loading, experimental setup, and model evaluation. OGB will be regularly updated and welcomes inputs from the community. OGB datasets as well as data loaders, evaluation scripts, baseline code, and leaderboards are publicly available at https://ogb.stanford.edu.

The development of deep neural networks has improved representation learning in various domains, including textual, graph structural, and relational triple representations. This development opened the door to new relation extraction beyond the traditional text-oriented relation extraction. However, research on the effectiveness of considering multiple heterogeneous domain information simultaneously is still under exploration, and if a model can take an advantage of integrating heterogeneous information, it is expected to exhibit a significant contribution to many problems in the world. This thesis works on Drug-Drug Interactions (DDIs) from the literature as a case study and realizes relation extraction utilizing heterogeneous domain information. First, a deep neural relation extraction model is prepared and its attention mechanism is analyzed. Next, a method to combine the drug molecular structure information and drug description information to the input sentence information is proposed, and the effectiveness of utilizing drug molecular structures and drug descriptions for the relation extraction task is shown. Then, in order to further exploit the heterogeneous information, drug-related items, such as protein entries, medical terms and pathways are collected from multiple existing databases and a new data set in the form of a knowledge graph (KG) is constructed. A link prediction task on the constructed data set is conducted to obtain embedding representations of drugs that contain the heterogeneous domain information. Finally, a method that integrates the input sentence information and the heterogeneous KG information is proposed. The proposed model is trained and evaluated on a widely used data set, and as a result, it is shown that utilizing heterogeneous domain information significantly improves the performance of relation extraction from the literature.

在本文中，我们提供了针对深度学习（DL）模型的结构化文献分析，该模型用于支持癌症生物学的推论，并特别强调了多词分析。这项工作着重于现有模型如何通过先验知识，生物学合理性和解释性，生物医学领域的基本特性来解决更好的对话。我们讨论了DL模型的最新进化拱门沿整合先前的生物关系和网络知识的方向，以支持更好的概括（例如途径或蛋白质 - 蛋白质相互作用网络）和解释性。这代表了向模型的基本功能转变，该模型可以整合机械和统计推断方面。我们讨论了在此类模型中整合域先验知识的代表性方法。该论文还为解释性和解释性的当代方法提供了关键的看法。该分析指向编码先验知识和改善解释性之间的融合方向。

专门的基于变形金刚的模型（例如生物Biobert和Biomegatron）适用于基于公共可用的生物医学语料库的生物医学领域。因此，它们有可能编码大规模的生物学知识。我们研究了这些模型中生物学知识的编码和表示，及其支持癌症精度医学推断的潜在实用性 - 即，对基因组改变的临床意义的解释。我们比较不同变压器基线的性能；我们使用探测来确定针对不同实体的编码的一致性；我们使用聚类方法来比较和对比基因，变异，药物和疾病的嵌入的内部特性。我们表明，这些模型确实确实编码了生物学知识，尽管其中一些模型在针对特定任务的微调中丢失了。最后，我们分析了模型在数据集中的偏见和失衡方面的行为。

发现新药是寻求并证明因果关系。作为一种新兴方法利用人类的知识和创造力，数据和机器智能，因果推论具有减少认知偏见并改善药物发现决策的希望。尽管它已经在整个价值链中应用了，但因子推理的概念和实践对许多从业者来说仍然晦涩难懂。本文提供了有关因果推理的非技术介绍，审查了其最新应用，并讨论了在药物发现和开发中采用因果语言的机会和挑战。

Knowledge graph (KG) is used to represent data in terms of entities and structural relations between the entities. This representation can be used to solve complex problems such as recommendation systems and question answering. In this study, a set of candidate drugs for COVID-19 are proposed by using Drug repurposing knowledge graph (DRKG). DRKG is a biological knowledge graph constructed using a vast amount of open source biomedical knowledge to understand the mechanism of compounds and the related biological functions. Node and relation embeddings are learned using knowledge graph embedding models and neural network and attention related models. Different models are used to get the node embedding by changing the objective of the model. These embeddings are later used to predict if a candidate drug is effective to treat a disease or how likely it is for a drug to bind to a protein associated to a disease which can be modelled as a link prediction task between two nodes. RESCAL performed the best on the test dataset in terms of MR, MRR and Hits@3.

学术知识图（KGS）提供了代表科学出版物编码的知识的丰富的结构化信息来源。随着出版的科学文学的庞大，包括描述科学概念的过多的非均匀实体和关系，这些公斤本质上是不完整的。我们呈现Exbert，一种利用预先训练的变压器语言模型来执行学术知识图形完成的方法。我们将知识图形的三元组模型为文本并执行三重分类（即，属于KG或不属于KG）。评估表明，在三重分类，链路预测和关系预测的任务中，Exbert在三个学术kg完成数据集中表现出其他基线。此外，我们将两个学术数据集作为研究界的资源，从公共公共公报和在线资源中收集。

我们根据生态毒理学风险评估中使用的主要数据来源创建了知识图表。我们已经将这种知识图表应用于风险评估中的重要任务，即化学效果预测。我们已经评估了在该预测任务的各种几何，分解和卷积模型中嵌入模型的九个知识图形嵌入模型。我们表明，使用知识图形嵌入可以提高与神经网络的效果预测的准确性。此外，我们已经实现了一种微调架构，它将知识图形嵌入到效果预测任务中，并导致更好的性能。最后，我们评估知识图形嵌入模型的某些特征，以阐明各个模型性能。