Mining on Alzheimer’s diseases related knowledge graph to identity potential AD-related semantic triples for drug repurposing

Nian, Yi; Hu, Xinyue; Zhang, Rui; Feng, Jingna; Du, Jingcheng; Li, Fang; Chen, Yong; Tao, Cui

doi:10.1186/s12859-022-04934-1

Cited by 10 publications

(4 citation statements)

References 58 publications

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“…To accomplish this task, corrections are required, such as data cleaning, identification and management of outliers, handling of missing data, and correction of errors in the information. These actions ensure the consistency and reliability of the data, thus establishing a solid basis for moving forward in the analysis process [34], [35]. The information collected in our research will be subjected to this phase to ensure that the data obtained is reliable and of high quality, thus allowing the transition to the next stage, which is modeling.…”

Section: Modifymentioning

confidence: 98%

Predictive models in Alzheimer's disease: an evaluation based on data mining techniques

Andrade-Arenas,

Rubio-Paucar,

Yactayo-Arias

2024

IJECE

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The increasing prevalence of Alzheimer's disease in older adults has raised significant concern in recent years. Aware of this challenge, this research set out to develop predictive models that allow early identification of people at risk for Alzheimer's disease, considering several variables associated with the disease. To achieve this objective, data mining techniques were employed, specifically the decision tree algorithm, using the RapidMiner Studio tool. The sample explore modify model and assess (SEMMA) methodology was implemented systematically at each stage of model development, ensuring an orderly and structured approach. The results obtained revealed that 45.00% of people with dementia present characteristics that identify them as candidates for confirmation of a diagnosis of Alzheimer's disease. In contrast, 52.78% of those who do not have dementia show no danger of contracting the disease. In the conclusion of the research, it was noted that most patients diagnosed with Alzheimer's are older than 65 years, indicating that this stage of life tends to trigger brain changes associated with the disease. This finding underscores the importance of considering age as a key factor in the early identification of the disease.

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Section: Modifymentioning

confidence: 98%

Predictive models in Alzheimer's disease: an evaluation based on data mining techniques

Andrade-Arenas,

Rubio-Paucar,

Yactayo-Arias

2024

IJECE

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“…Experiments showed that iDPath could identify explicit critical paths that were consistent with clinical evidence. Nian et al [67] utilized semantic triples in SemMedDB for KG construction and drug-disease link prediction. They filtered the most relevant semantic triples for Alzheimer's disease (AD) using a BERT-based classifier and some rule-based methods, and trained graph embedding algorithms, such as TransE [68], DistMult [69], and ComplEx [70], to predict drug/chemical/ food supplement candidates that may be helpful for AD treatment or prevention.…”

Section: In Silico Drug Repurposingmentioning

confidence: 99%

Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions

Li,

Nian,

Sun

et al. 2023

Yearb Med Inform

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Objectives: Graph representation learning (GRL) has emerged as a pivotal field that has contributed significantly to breakthroughs in various fields, including biomedicine. The objective of this survey is to review the latest advancements in GRL methods and their applications in the biomedical field. We also highlight key challenges currently faced by GRL and outline potential directions for future research. Methods: We conducted a comprehensive search of multiple databases, including PubMed, Web of Science, IEEE Xplore, and Google Scholar, to collect relevant publications from the past two years (2021-2022). The studies selected for review were based on their relevance to the topic and the publication quality. Results: A total of 78 articles were included in our analysis. We identified three main categories of GRL methods and summarized their methodological foundations and notable models. In terms of GRL applications, we focused on two main topics: drug and disease. We analyzed the study frameworks and achievements of the prominent research. Based on the current state-of-the-art, we discussed the challenges and future directions. Conclusions: GRL methods applied in the biomedical field demonstrated several key characteristics, including the utilization of attention mechanisms to prioritize relevant features, a growing emphasis on model interpretability, and the combination of various techniques to improve model performance. There are also challenges needed to be addressed, including mitigating model bias, accommodating the heterogeneity of large-scale knowledge graphs, and improving the availability of high-quality graph data. To fully leverage the potential of GRL, future efforts should prioritize these areas of research.

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“…Zhu [34]'s work exemplifies this trend by creating diseasespecific knowledge graphs, including for AD, from PubMed abstracts, employing advanced models like Att-BiLSTM-CRF [35] for named entity recognition and a combination of BiLSTM [36] and ResNet [37] for relation extraction. Similarly, Nian [38]'s research utilizes literaturederived knowledge graphs, extracting AD-related triplets from SemMedDB [39,40] to explore connections between AD and various entities, showcasing the growing emphasis on data-driven methodologies in constructing knowledge graphs for AD research.…”

Section: Introductionmentioning

confidence: 99%

Alzheimer’s Disease Knowledge Graph Enhances Knowledge Discovery and Disease Prediction

Yang,

Yu,

Gao

et al. 2024

Preprint

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BackgroundAlzheimer’s disease (AD), a progressive neurodegenerative disorder, continues to increase in prevalence without any effective treatments to date. In this context, knowledge graphs (KGs) have emerged as a pivotal tool in biomedical research, offering new perspectives on drug repurposing and biomarker discovery by analyzing intricate network structures. Our study seeks to build an AD-specific knowledge graph, highlighting interactions among AD, genes, variants, chemicals, drugs, and other diseases. The goal is to shed light on existing treatments, potential targets, and diagnostic methods for AD, thereby aiding in drug repurposing and the identification of biomarkers.ResultsWe annotated 800 PubMed abstracts and leveraged GPT-4 for text augmentation to enrich our training data for named entity recognition (NER) and relation classification. A comprehensive data mining model, integrating NER and relationship classification, was trained on the annotated corpus. This model was subsequently applied to extract relation triplets from unannotated abstracts. To enhance entity linking, we utilized a suite of reference biomedical databases and refine the linking accuracy through abbreviation resolution. As a result, we successfully identified 3,199,276 entity mentions and 633,733 triplets, elucidating connections between 5,000 unique entities. These connections were pivotal in constructing a comprehensive Alzheimer’s Disease Knowledge Graph (ADKG). We also integrated the ADKG constructed after entity linking with other biomedical databases. The ADKG served as a training ground for Knowledge Graph Embedding models with the high-ranking predicted triplets supported by evidence, underscoring the utility of ADKG in generating testable scientific hypotheses. Further application of ADKG in predictive modeling using the UK Biobank data revealed models based on ADKG outperforming others, as evidenced by higher values in the areas under the receiver operating characteristic (ROC) curves.ConclusionThe ADKG is a valuable resource for generating hypotheses and enhancing predictive models, highlighting its potential to advance AD’s disease research and treatment strategies.

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Mining on Alzheimer’s diseases related knowledge graph to identity potential AD-related semantic triples for drug repurposing

Cited by 10 publications

References 58 publications

Predictive models in Alzheimer's disease: an evaluation based on data mining techniques

Predictive models in Alzheimer's disease: an evaluation based on data mining techniques

Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions

Alzheimer’s Disease Knowledge Graph Enhances Knowledge Discovery and Disease Prediction

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