scholarly journals An Integrative Network Science and Artificial Intelligence Drug Repurposing Approach for Muscle Atrophy in Spaceflight Microgravity

2021 ◽  
Vol 9 ◽  
Author(s):  
Vidya Manian ◽  
Jairo Orozco-Sandoval ◽  
Victor Diaz-Martinez
Keyword(s):  
Neural Network ◽  
Muscle Atrophy ◽  
Link Prediction ◽  
Preferential Attachment ◽  
Drug Repurposing ◽  
New Drugs ◽  
Gradient Boosting ◽  
Knowledge Graph ◽  
Drug Knowledge ◽  
Prediction Approach

Muscle atrophy is a side effect of several terrestrial diseases which also affects astronauts severely in space missions due to the reduced gravity in spaceflight. An integrative graph-theoretic network-based drug repurposing methodology quantifying the interplay of key gene regulations and protein–protein interactions in muscle atrophy conditions is presented. Transcriptomic datasets from mice in spaceflight from GeneLab have been extensively mined to extract the key genes that cause muscle atrophy in organ muscle tissues such as the thymus, liver, and spleen. Top muscle atrophy gene regulators are selected by Bayesian Markov blanket method and gene–disease knowledge graph is constructed using the scalable precision medicine knowledge engine. A deep graph neural network is trained for predicting links in the network. The top ranked diseases are identified and drugs are selected for repurposing using drug bank resource. A disease drug knowledge graph is constructed and the graph neural network is trained for predicting new drugs. The results are compared with machine learning methods such as random forest, and gradient boosting classifiers. Network measure based methods shows that preferential attachment has good performance for link prediction in both the gene–disease and disease–drug graphs. The receiver operating characteristic curves, and prediction accuracies for each method show that the random walk similarity measure and deep graph neural network outperforms the other methods. Several key target genes identified by the graph neural network are associated with diseases such as cancer, diabetes, and neural disorders. The novel link prediction approach applied to the disease drug knowledge graph identifies the Monoclonal Antibodies drug therapy as suitable candidate for drug repurposing for spaceflight induced microgravity. There are a total of 21 drugs identified as possible candidates for treating muscle atrophy. Graph neural network is a promising deep learning architecture for link prediction from gene–disease, and disease–drug networks.

scholarly journals CTKG: A Knowledge Graph for Clinical Trials

2021 ◽  
Author(s):  
Ziqi Chen ◽  
Bo Peng ◽  
Vassilis N. Ioannidis ◽  
Mufei Li ◽  
George Karypis ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Success Rate ◽  
Similarity Search ◽  
Drug Repurposing ◽  
New Drugs ◽  
Knowledge Graph ◽  
New Treatments

Effective and successful clinical trials are essential in developing new drugs and advancing new treatments. However, clinical trials are very expensive and easy to fail. The high cost and low success rate of clinical trials motivate research on inferring knowledge from existing clinical trials in innovative ways for designing future clinical trials. In this manuscript, we present our efforts on constructing the first publicly available Clinical Trials Knowledge Graph, denoted as CTKG. CTKG includes nodes representing medical entities in clinical trials (e.g., studies, drugs and conditions), and edges representing the relations among these entities (e.g., drugs used in studies). Our embedding analysis demonstrates the potential utilities of CTKG in various applications such as drug repurposing and similarity search, among others.

Drug Repurposing for Parkinson’s Disease by Integrating Knowledge Graph Completion Model and Knowledge Fusion of Medical Literature

2021 ◽  
Vol 13 (1) ◽  
pp. 14
Author(s):  
Xiaolin Zhang ◽  
Chao Che
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Knowledge Base ◽  
Medical Literature ◽  
Medical Knowledge ◽  
Drug Repurposing ◽  
New Drugs ◽  
Knowledge Graph ◽  
Knowledge Fusion ◽  
Effective Drugs

The prevalence of Parkinson’s disease increases a tremendous medical and economic burden to society. Therefore, the effective drugs are urgently required. However, the traditional development of effective drugs is costly and risky. Drug repurposing, which identifies new applications for existing drugs, is a feasible strategy for discovering new drugs for Parkinson’s disease. Drug repurposing is based on sufficient medical knowledge. The local medical knowledge base with manually labeled data contains a large number of accurate, but not novel, medical knowledge, while the medical literature containing the latest knowledge is difficult to utilize, because of unstructured data. This paper proposes a framework, named Drug Repurposing for Parkinson’s disease by integrating Knowledge Graph Completion method and Knowledge Fusion of medical literature data (DRKF) in order to make full use of a local medical knowledge base containing accurate knowledge and medical literature with novel knowledge. DRKF first extracts the relations that are related to Parkinson’s disease from medical literature and builds a medical literature knowledge graph. After that, the literature knowledge graph is fused with a local medical knowledge base that integrates several specific medical knowledge sources in order to construct a fused medical knowledge graph. Subsequently, knowledge graph completion methods are leveraged to predict the drug candidates for Parkinson’s disease by using the fused knowledge graph. Finally, we employ classic machine learning methods to repurpose the drug for Parkinson’s disease and compare the results with the method only using the literature-based knowledge graph in order to confirm the effectiveness of knowledge fusion. The experiment results demonstrate that our framework can achieve competitive performance, which confirms the effectiveness of our proposed DRKF for drug repurposing against Parkinson’s disease. It could be a supplement to traditional drug discovery methods.

scholarly journals Knowledge-driven drug repurposing using a comprehensive drug knowledge graph

2020 ◽  
Vol 26 (4) ◽  
pp. 2737-2750 ◽  
Author(s):  
Yongjun Zhu ◽  
Chao Che ◽  
Bo Jin ◽  
Ningrui Zhang ◽  
Chang Su ◽  
...  
Keyword(s):  
Large Scale ◽  
De Novo ◽  
Rapid Development ◽  
Drug Repurposing ◽  
Data Representation ◽  
Knowledge Bases ◽  
Knowledge Graph ◽  
Multiple Drug ◽  
Treatment Information ◽  
Drug Knowledge

Due to the huge costs associated with new drug discovery and development, drug repurposing has become an important complement to the traditional de novo approach. With the increasing number of public databases and the rapid development of analytical methodologies, computational approaches have gained great momentum in the field of drug repurposing. In this study, we introduce an approach to knowledge-driven drug repurposing based on a comprehensive drug knowledge graph. We design and develop a drug knowledge graph by systematically integrating multiple drug knowledge bases. We describe path- and embedding-based data representation methods of transforming information in the drug knowledge graph into valuable inputs to allow machine learning models to predict drug repurposing candidates. The evaluation demonstrates that the knowledge-driven approach can produce high predictive results for known diabetes mellitus treatments by only using treatment information on other diseases. In addition, this approach supports exploratory investigation through the review of meta paths that connect drugs with diseases. This knowledge-driven approach is an effective drug repurposing strategy supporting large-scale prediction and the investigation of case studies.

scholarly journals ParamE: Regarding Neural Network Parameters as Relation Embeddings for Knowledge Graph Completion

2020 ◽  
Vol 34 (03) ◽  
pp. 2774-2781
Author(s):  
Feihu Che ◽  
Dawei Zhang ◽  
Jianhua Tao ◽  
Mingyue Niu ◽  
Bocheng Zhao
Keyword(s):  
Neural Network ◽  
Link Prediction ◽  
State Of The Art ◽  
Graph Embedding ◽  
Feature Space ◽  
Knowledge Graph ◽  
Network Parameters ◽  
New Knowledge ◽  
Fitting Ability ◽  
Translational Models

We study the task of learning entity and relation embeddings in knowledge graphs for predicting missing links. Previous translational models on link prediction make use of translational properties but lack enough expressiveness, while the convolution neural network based model (ConvE) takes advantage of the great nonlinearity fitting ability of neural networks but overlooks translational properties. In this paper, we propose a new knowledge graph embedding model called ParamE which can utilize the two advantages together. In ParamE, head entity embeddings, relation embeddings and tail entity embeddings are regarded as the input, parameters and output of a neural network respectively. Since parameters in networks are effective in converting input to output, taking neural network parameters as relation embeddings makes ParamE much more expressive and translational. In addition, the entity and relation embeddings in ParamE are from feature space and parameter space respectively, which is in line with the essence that entities and relations are supposed to be mapped into two different spaces. We evaluate the performances of ParamE on standard FB15k-237 and WN18RR datasets, and experiments show ParamE can significantly outperform existing state-of-the-art models, such as ConvE, SACN, RotatE and D4-STE/Gumbel.

scholarly journals Task-Driven Knowledge Graph Filtering Improves Prioritizing Drugs for Repurposing

2021 ◽  
Author(s):  
Florin Ratajczak ◽  
Mitchell Joblin ◽  
Martin Ringsquandl ◽  
Marcel Hildebrandt
Keyword(s):  
Domain Knowledge ◽  
Drug Repurposing ◽  
New Drugs ◽  
Knowledge Graph ◽  
Biomedical Data ◽  
Biomedical Knowledge ◽  
Relation Type ◽  
Knowledge Graphs ◽  
Improved Performance ◽  
Efficient Learning

Abstract Background Drug repurposing aims at finding new targets for already developed drugs. It becomes more relevant as the cost of discovering new drugs steadily increases. To find new potential targets for a drug, an abundance of methods and existing biomedical knowledge from different domains can be leveraged. Recently, knowledge graphs have emerged in the biomedical domain that integrate information about genes, drugs, diseases and other biological domains. Knowledge graphs can be used to predict new connections between compounds and diseases, leveraging the interconnected biomedical data around them. While real world use cases such as drug repurposing are only interested in one specific relation type, widely used knowledge graph embedding models simultaneously optimize over all relation types in the graph. This can lead the models to underfit the data that is most relevant for the desired relation type. We propose a method that leverages domain knowledge in the form of metapaths and use them to filter two biomedical knowledge graphs (Hetionet and DRKG) for the purpose of improving performance on the prediction task of drug repurposing while simultaneously increasing computational efficiency. Results We find that our method reduces the number of entities by 60% on Hetionet and 26% on DRKG, while leading to an improvement in prediction performance of up to 40.8% on Hetionet and 12.4% on DRKG, with an average improvement of 17.5% on Hetionet and 5.1% on DRKG. Additionally, prioritization of antiviral compounds for SARS CoV-2 improves after task-driven filtering is applied. Conclusion Knowledge graphs contain facts that are counter productive for specific tasks, in our case drug repurposing. We also demonstrate that these facts can be removed, resulting in an improved performance in that task and a more efficient learning process.

scholarly journals A Literature-Based Knowledge Graph Embedding Method for Identifying Drug Repurposing Opportunities in Rare Diseases

2019 ◽  
Author(s):  
Daniel N. Sosa ◽  
Alexander Derry ◽  
Margaret Guo ◽  
Eric Wei ◽  
Connor Brinton ◽  
...  
Keyword(s):  
Rare Diseases ◽  
Drug Repurposing ◽  
Graph Embedding ◽  
Capital Requirements ◽  
Biomedical Literature ◽  
New Drugs ◽  
Global Network ◽  
Knowledge Graph ◽  
Promising Alternative ◽  
Embedding Method

One in ten people are affected by rare diseases, and three out of ten children with rare diseases will not live past age five. However, the small market size of individual rare diseases, combined with the time and capital requirements of pharmaceutical R&D, have hindered the development of new drugs for these cases. A promising alternative is drug repurposing, whereby existing FDA-approved drugs might be used to treat diseases different from their original indications. In order to generate drug repurposing hypotheses in a systematic and comprehensive fashion, it is essential to integrate information from across the literature of pharmacology, genetics, and pathology. To this end, we leverage a newly developed knowledge graph, the Global Network of Biomedical Relationships (GNBR). GNBR is a large, heterogeneous knowledge graph comprising drug, disease, and gene (or protein) entities linked by a small set of semantic themes derived from the abstracts of biomedical literature. We apply a knowledge graph embedding method that explicitly models the uncertainty associated with literature-derived relationships and uses link prediction to generate drug repurposing hypotheses. This approach achieves high performance on a gold-standard test set of known drug indications (AUROC = 0.89) and is capable of generating novel repurposing hypotheses, which we independently validate using external literature sources and protein interaction networks. Finally, we demonstrate the ability of our model to produce explanations of its predictions.

scholarly journals Robust Embedding with Multi-Level Structures for Link Prediction

2019 ◽  
Author(s):  
Zihan Wang ◽  
Zhaochun Ren ◽  
Chunyu He ◽  
Peng Zhang ◽  
Yue Hu
Keyword(s):  
Neural Network ◽  
Recent Work ◽  
Link Prediction ◽  
Robustness Analysis ◽  
Hierarchical Structures ◽  
Knowledge Graph ◽  
Structure Information ◽  
Robustness To Noise ◽  
Multi Level ◽  
Representational Power

Knowledge Graph (KG) embedding has become crucial for the task of link prediction. Recent work applies encoder-decoder models to tackle this problem, where an encoder is formulated as a graph neural network (GNN) and a decoder is represented by an embedding method. These approaches enforce embedding techniques with structure information. Unfortunately, existing GNN-based frameworks still confront 3 severe problems: low representational power, stacking in a flat way, and poor robustness to noise. In this work, we propose a novel multi-level graph neural network (M-GNN) to address the above challenges. We first identify an injective aggregate scheme and design a powerful GNN layer using multi-layer perceptrons (MLPs). Then, we define graph coarsening schemes for various kinds of relations, and stack GNN layers on a series of coarsened graphs, so as to model hierarchical structures. Furthermore, attention mechanisms are adopted so that our approach can make predictions accurately even on the noisy knowledge graph. Results on WN18 and FB15k datasets show that our approach is effective in the standard link prediction task, significantly and consistently outperforming competitive baselines. Furthermore, robustness analysis on FB15k-237 dataset demonstrates that our proposed M-GNN is highly robust to sparsity and noise. 

scholarly journals ELPKG: A High-Accuracy Link Prediction Approach for Knowledge Graph Completion

Symmetry ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1096 ◽  
Author(s):  
Jiangtao Ma ◽  
Yaqiong Qiao ◽  
Guangwu Hu ◽  
Yanjun Wang ◽  
Chaoqin Zhang ◽  
...  
Keyword(s):  
Link Prediction ◽  
Large Scale ◽  
High Accuracy ◽  
Prediction Algorithm ◽  
Knowledge Graph ◽  
Named Entity ◽  
Knowledge Reasoning ◽  
Novel Approach ◽  
Soft Logic ◽  
Prediction Approach

Link prediction in knowledge graph is the task of utilizing the existing relations to infer new relations so as to build a more complete knowledge graph. The inferred new relations plus original knowledge graph is the symmetry of completion knowledge graph. Previous research on link predication only focuses on path or semantic-based features, which can hardly have a full insight of features between entities and may result in a certain ratio of false inference results. To improve the accuracy of link predication, we propose a novel approach named Entity Link Prediction for Knowledge Graph (ELPKG), which can achieve a high accuracy on large-scale knowledge graphs while keeping desirable efficiency. ELPKG first combines path and semantic-based features together to represent the relationships between entities. Then it adopts a probabilistic soft logic-based reasoning method that effectively solves the problem of non-deterministic knowledge reasoning. Finally, the relation between entities is completed based on the entity link prediction algorithm. Extensive experiments on real dataset show that ELPKG outperforms baseline methods on hits@1, hits@10, and MRR.

Link Prediction on a Network of Co-occurring MeSH Terms: Towards Literature-based Discovery

2016 ◽  
Vol 55 (04) ◽  
pp. 340-346 ◽  
Author(s):  
Thomas Rindflesch ◽  
Dimitar Hristovski ◽  
Andrej Kastrin
Keyword(s):  
Link Prediction ◽  
Preferential Attachment ◽  
Mesh Network ◽  
Jaccard Coefficient ◽  
Common Neighbor ◽  
Mesh Terms ◽  
Unsupervised Approach ◽  
Proximity Measures ◽  
Prediction Approach ◽  
Link Formation

Summary Objectives:Literature-based discovery (LBD) is a text mining methodology for automatically generating research hypotheses from existing knowledge. We mimic the process of LBD as a classification problem on a graph of MeSH terms. We employ unsupervised and supervised link prediction methods for predicting previously unknown connections between biomedical concepts. Methods:We evaluate the effectiveness of link prediction through a series of experiments using a MeSH network that contains the history of link formation between biomedical concepts. We performed link prediction using proximity measures, such as common neighbor (CN), Jaccard coefficient (JC), Adamic / Adar index (AA) and preferential attachment (PA). Our approach relies on the assumption that similar nodes are more likely to establish a link in the future. Results:Applying an unsupervised approach, the AA measure achieved the best performance in terms of area under the ROC curve (AUC = 0.76),gfollowed by CN, JC, and PA. In a supervised approach, we evaluate whether proximity measures can be combined to define a model of link formation across all four predictors. We applied various classifiers, including decision trees, k-nearest neighbors, logistic regression, multilayer perceptron, naïve Bayes, and random forests. Random forest classifier accomplishes the best performance (AUC = 0.87). Conclusions:The link prediction approach proved to be effective for LBD processing. Supervised statistical learning approaches clearly outperform an unsupervised approach to link prediction.

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