Dyport: dynamic importance-based biomedical hypothesis generation benchmarking technique.

BACKGROUND: Automated hypothesis generation (HG) focuses on uncovering hidden connections within the extensive information that is publicly available. This domain has become increasingly popular, thanks to modern machine learning algorithms. However, the automated evaluation of HG systems is still an open problem, especially on a larger scale. RESULTS: This paper presents a novel benchmarking framework Dyport for evaluating biomedical hypothesis generation systems. Utilizing curated datasets, our approach tests these systems under realistic conditions, enhancing the relevance of our evaluations. We integrate knowledge from the curated databases into a dynamic graph, accompanied by a method to quantify discovery importance. This not only assesses hypotheses accuracy but also their potential impact in biomedical research which significantly extends traditional link prediction benchmarks. Applicability of our benchmarking process is demonstrated on several link prediction systems applied on biomedical semantic knowledge graphs. Being flexible, our benchmarking system is designed for broad application in hypothesis generation quality verification, aiming to expand the scope of scientific discovery within the biomedical research community. CONCLUSIONS: Dyport is an open-source benchmarking framework designed for biomedical hypothesis generation systems evaluation, which takes into account knowledge dynamics, semantics and impact. All code and datasets are available at: https://github.com/IlyaTyagin/Dyport .

AI-based mining of biomedical literature: Applications for drug repurposing for the treatment of dementia.

Neurodegenerative pathologies such as Alzheimer's disease, Parkinson's disease, Huntington's disease, Amyotrophic lateral sclerosis, Multiple sclerosis, HIV-associated neurocognitive disorder, and others significantly affect individuals, their families, caregivers, and healthcare systems. While there are no cures yet, researchers worldwide are actively working on the development of novel treatments that have the potential to slow disease progression, alleviate symptoms, and ultimately improve the overall health of patients. Huge volumes of new scientific information necessitate new analytical approaches for meaningful hypothesis generation. To enable the automatic analysis of biomedical data we introduced AGATHA, an effective AI-based literature mining tool that can navigate massive scientific literature databases, such as PubMed. The overarching goal of this effort is to adapt AGATHA for drug repurposing by revealing hidden connections between FDA-approved medications and a health condition of interest. Our tool converts the abstracts of peer-reviewed papers from PubMed into multidimensional space where each gene and health condition are represented by specific metrics. We implemented advanced statistical analysis to reveal distinct clusters of scientific terms within the virtual space created using AGATHA-calculated parameters for selected health conditions and genes. Partial Least Squares Discriminant Analysis was employed for categorizing and predicting samples (122 diseases and 20889 genes) fitted to specific classes. Advanced statistics were employed to build a discrimination model and extract lists of genes specific to each disease class. Here we focus on drugs that can be repurposed for dementia treatment as an outcome of neurodegenerative diseases. Therefore, we determined dementia-associated genes statistically highly ranked in other disease classes. Additionally, we report a mechanism for detecting genes common to multiple health conditions. These sets of genes were classified based on their presence in biological pathways, aiding in selecting candidates and biological processes that are exploitable with drug repurposing. Author Summary: This manuscript outlines our project involving the application of AGATHA, an AI-based literature mining tool, to discover drugs with the potential for repurposing in the context of neurocognitive disorders. The primary objective is to identify connections between approved medications and specific health conditions through advanced statistical analysis, including techniques like Partial Least Squares Discriminant Analysis (PLSDA) and unsupervised clustering. The methodology involves grouping scientific terms related to different health conditions and genes, followed by building discrimination models to extract lists of disease-specific genes. These genes are then analyzed through pathway analysis to select candidates for drug repurposing.

AI-based mining of biomedical literature: Applications for drug repurposing for the treatment of dementia.

Neurodegenerative pathologies such as Alzheimer's disease, Parkinson's disease, Huntington's disease, Amyotrophic lateral sclerosis, Multiple sclerosis, HIV-associated neurocognitive disorder, and others significantly affect individuals, their families, caregivers, and healthcare systems. While there are no cures yet, researchers worldwide are actively working on the development of novel treatments that have the potential to slow disease progression, alleviate symptoms, and ultimately improve the overall health of patients. Huge volumes of new scientific information necessitate new analytical approaches for meaningful hypothesis generation. To enable the automatic analysis of biomedical data we introduced AGATHA, an effective AI-based literature mining tool that can navigate massive scientific literature databases, such as PubMed. The overarching goal of this effort is to adapt AGATHA for drug repurposing by revealing hidden connections between FDA-approved medications and a health condition of interest. Our tool converts the abstracts of peer-reviewed papers from PubMed into multidimensional space where each gene and health condition are represented by specific metrics. We implemented advanced statistical analysis to reveal distinct clusters of scientific terms within the virtual space created using AGATHA-calculated parameters for selected health conditions and genes. Partial Least Squares Discriminant Analysis was employed for categorizing and predicting samples (122 diseases and 20889 genes) fitted to specific classes. Advanced statistics were employed to build a discrimination model and extract lists of genes specific to each disease class. Here we focus on drugs that can be repurposed for dementia treatment as an outcome of neurodegenerative diseases. Therefore, we determined dementia-associated genes statistically highly ranked in other disease classes. Additionally, we report a mechanism for detecting genes common to multiple health conditions. These sets of genes were classified based on their presence in biological pathways, aiding in selecting candidates and biological processes that are exploitable with drug repurposing.

Exposure to angiotensin-converting enzyme inhibitors that cross the blood-brain barrier and the risk of dementia among patients with human immunodeficiency virus.

More than one million people in the United States and over 38 million people worldwide are living with human immunodeficiency virus (HIV) infection. Antiretroviral therapy (ART) greatly improves the health of people living with HIV (PLWH); however, the increased life longevity of PLWH has revealed consequences of HIV-associated comorbidities. HIV can enter the brain and cause inflammation even in individuals with well-controlled HIV infection. The quality of life for PLWH can be compromised by cognitive deficits and memory loss, termed HIV-associated neurological disorders (HAND). HIV-associated dementia is a related but distinct diagnosis. Common causes of dementia in PLWH are similar to the general population and can affect cognition. There is an urgent need to identify treatments for the aging PWLH population. We previously developed AI-based biomedical literature mining systems to uncover a potential novel connection between HAND the renin-angiotensin system (RAAS), which is a pharmacological target for hypertension. RAAS-targeting anti-hypertensives are gaining attention for their protective benefits in several neurocognitive disorders. To our knowledge, the effect of RAAS-targeting drugs on the cognition of PLWH development of dementia has not previously been analyzed. We hypothesized that exposure to angiotensin-converting enzyme inhibitors (ACEi) that cross the blood brain barrier (BBB) reduces the risk/occurrence of dementia in PLWH. We report a retrospective cohort study of electronic health records (EHRs) to examine the proposed hypothesis using data from the United States Department of Veterans Affairs, in which a primary outcome of dementia was measured in controlled cohorts of patients exposed to BBB-penetrant ACEi versus those unexposed to BBB-penetrant ACEi. The results reveal a statistically significant reduction in dementia diagnosis for PLWH exposed to BBB-penetrant ACEi. These results suggest there is a potential protective effect of BBB ACE inhibitor exposure against dementia in PLWH that warrants further investigation.

Exposure to angiotensin-converting enzyme inhibitors that cross the blood-brain barrier and the risk of dementia among People Living with HIV.

BACKGROUND: The decreased mortality of people living with HIV (PLWH) has revealed non-HIV-associated comorbidities such as neurocognitive disorders (e.g., dementia). There is an urgency to discover therapeutics to prevent or delay neurocognitive decline among PLWH. METHODS: The artificial intelligence platform Automatic Graph-mining And Transformer based Hypothesis Generation Approach (AGATHA) was utilized to seek potential drugs to be repurposed for the management of non-HIV-associated dementia. AGATHA revealed angiotensin-converting enzyme inhibitors that cross the blood-brain barrier (BBB ACEi) as a target for decreasing dementia. Subsequently, we conducted a retrospective study evaluating incident dementia using the VA Informatics and Computing Infrastructure (VINCI) evaluating ACE inhibitors. Cox proportional hazards models were fit and hazard ratios (HR) with corresponding 95% confidence intervals (CIs) are presented. FINDINGS: A total 9,419 PLWH exposed to an BBB ACE inhibitor (ACEi) and 8,831 PLWH unexposed demonstrated that PLWH exposed to BBB ACEi had a 21.4% (univariate) and 15.2% (multivariate) lower hazard of dementia. The propensity score matched analysis demonstrated a 14.3% lower hazard of incident dementia compared to BBB ACEi unexposed (HR 0.857, 95% CI 0.747-0.984). INTERPRETATION: An artificial intelligence-based literature mining system (AGATHA) was utilized to uncover a medication with potential to be repurposed. AGATHA demonstrated that BBB ACEi as a target for decreasing dementia among PLWH. Additionally, we conducted a retrospective study demonstrating a decrease in incident dementia among PLWH exposed to BBB ACEi. Future research is needed to explore further and understand the relationship of dementia among PLWH exposed to ACEi.