Graph embedding-based link prediction for literature-based discovery in Alzheimer’s Disease

Pu, Yonglin; Beck, Daniel; Verspoor, Karin

doi:10.1016/j.jbi.2023.104464

Cited by 10 publications

(5 citation statements)

References 45 publications

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“…In the following research, there is some concordance with our proposed model as machine learning algorithms are applied. However, this study focuses on predicting factors associated with the onset of Alzheimer's in the elderly, according to the authors [12], [13]. Another study aligns with our results by conducting a thorough diagnosis of the disease to determine personalized treatments, focusing on key concepts such as accuracy, prediction, recall, and training time [14], [15].…”

Section: Discussionmentioning

confidence: 64%

“…The results provide new evidence on factors associated with dementia in the elderly. This information will facilitate the early detection and development of preventive measures for possible signs of dementia [12], [13]. According to this research, an exhaustive analysis of the diagnosis of Alzheimer's disease is carried out in order to determine the most appropriate treatment for each patient.…”

Section: Literature Reviewmentioning

confidence: 99%

“…A higher gain value indicates a better split. It is used to select the optimal feature to divide the data set at a given node established as stated in (13).…”

Section: Modelmentioning

confidence: 99%

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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: Discussionmentioning

confidence: 64%

Section: Literature Reviewmentioning

confidence: 99%

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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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“…Early LBD tools (Swanson, 1986;Smalheiser and Swanson, 1996) require manual effort in organizing information from the passages. Recent studies (Pu et al, 2023) approach LBD as a link prediction task over knowledge bases, where new knowledge is discovered as predicting new links between concepts. Our system is more like the early LBD tools.…”

Section: Related Workmentioning

confidence: 99%

Descriptive Knowledge Graph in Biomedical Domain

Zhu,

Huang,

Chang

2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

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We present a novel system that automatically extracts and generates informative and descriptive sentences from the biomedical corpus and facilitates the efficient search for relational knowledge. Unlike previous search engines or exploration systems that retrieve unconnected passages, our system organizes descriptive sentences as a relational graph, enabling researchers to explore closely related biomedical entities (e.g., diseases treated by a chemical) or indirectly connected entities (e.g., potential drugs for treating a disease). Our system also uses ChatGPT and a fine-tuned relation synthesis model to generate concise and reliable descriptive sentences from retrieved information, reducing the need for extensive human reading effort. With our system, researchers can easily obtain both high-level knowledge and detailed references and interactively steer to the information of interest. We spotlight the application of our system in COVID-19 research, illustrating its utility in areas such as drug repurposing and literature curation. 1

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“…To inform clinical decision-making, knowledge graphs have been built from experimental data, public databases, and literature to augment and enrich proteomics data 17 . Few other studies have used knowledge graph completion models for drug repositioning in cancer and auto-immune conditions 16 , 18 , 19 .…”

Section: Introductionmentioning

confidence: 99%

An experimentally validated approach to automated biological evidence generation in drug discovery using knowledge graphs

Sudhahar,

Ozer,

Chang

et al. 2024

Nat Commun

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Explaining predictions for drug repositioning with biological knowledge graphs is a challenging problem. Graph completion methods using symbolic reasoning predict drug treatments and associated rules to generate evidence representing the therapeutic basis of the drug. Yet the vast amounts of generated paths that are biologically irrelevant or not mechanistically meaningful within the context of disease biology can limit utility. We use a reinforcement learning based knowledge graph completion model combined with an automatic filtering approach that produces the most relevant rules and biological paths explaining the predicted drug’s therapeutic connection to the disease. In this work we validate the approach against preclinical experimental data for Fragile X syndrome demonstrating strong correlation between automatically extracted paths and experimentally derived transcriptional changes of selected genes and pathways of drug predictions Sulindac and Ibudilast. Additionally, we show it reduces the number of generated paths in two case studies, 85% for Cystic fibrosis and 95% for Parkinson’s disease.

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Graph embedding-based link prediction for literature-based discovery in Alzheimer’s Disease

Cited by 10 publications

References 45 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

Descriptive Knowledge Graph in Biomedical Domain

An experimentally validated approach to automated biological evidence generation in drug discovery using knowledge graphs

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