Informatics in Medicine Unlocked

INTRODUCTION: Throughout history, viruses have been infecting humans, leaving genomic traces called endogenous viral elements (EVEs). This study explores the infectious hypothesis for Alzheimer's disease (AD) that suggests that viral infections may contribute to AD by analyzing EVEs in genomes of AD patients and cognitively healthy centenarians (CHCs) measured by long-read sequencing. METHODS: We implemented an EVE detection pipeline that screened the genomes of 248 AD patients and 240 CHCs. EVEs were measured based on EVE count, length, and chromosome location. RESULTS: The genomes of AD patients contain significantly more EVEs than those of CHCs (p=3.2x10-4). Moreover, certain chromosomal regions in AD patients show a higher EVE count, particularly of the Orthoherpesviridae family, near genes differentially expressed in AD brains. DISCUSSION: These findings suggest that viral infections may increase susceptibility to AD, highlighting that preventive measures against infections potentially reduce AD risk.

Cerebral ischemia is a significant concern during high-risk surgeries, such as carotid endarterectomy (CEA). Continuous electroencephalography, monitored by neurophysiological experts, is used to detect cerebral ischemia during surgery; however, real-time visual interpretation is resource-intensive and error-prone. We evaluated machine learning (ML) models, including random forest (RF), eXtreme Gradient Boosting with a random forest base classifier (XGB), elastic-net logistic regression (LR), support vector classifier (SVC) with a radial basis function kernel, and naive Bayes (NB) classifier, for automated detection of cerebral ischemia during CEA using quantitative electroencephalographic (qEEG) features. RF achieved the highest sensitivity (0.79-0.83) and an area under the precision-recall curve (AUPRC) of 0.44, while XGB demonstrated the highest specificity (0.93-0.96) with an AUPRC of 0.36. Both models showed high negative predictive values and high area under the receiver operating characteristic (AUROC) scores. Feature-importance analysis identified alpha-band activity and hemispheric asymmetry as the most discriminative qEEG predictors of ischemia. These results highlight the potential of ML-assisted monitoring to support neurophysiology experts and enhance patient safety during high-risk surgical procedures.

Background: Precise localisation of intracranial electrode contacts is essential for the interpretation of stereoelectroencephalography recordings and planning epilepsy surgery. In current clinical practice, this is typically a manual process, which is time-consuming and prone to variability. Existing automated solutions are often fragmented across multiple tools requiring technical expertise, limiting their adoption in routine clinical workflows. This study presents an open-source extension for 3D Slicer that provides an integrated, user-friendly standalone solution for the direct automatic detection of electrode contacts within a widely used medical imaging platform. Results: The proposed method combines anchor bolt-based initialisation, probabilistic segmentation of electrode structures, and non-linear modelling to precisely track true electrode trajectories. The approach was evaluated on a dataset comprising 78 cases from 73 patients, including 1,078 electrodes with 14,480 contacts. The method achieved high localisation accuracy, with a median (interquartile range) deviation of 0.10 (0.06, 0.15) mm. Only 7/1078 (0.65%) electrodes required manual correction; these specific cases were handled using tools provided within the proposed extension. Conclusions: The presented extension enables fast, accurate, and reproducible electrode contact localisation within a single integrated environment. By combining automation with intuitive user interaction, it significantly reduces processing time while maintaining clinical reliability. The tool's free availability as an extension in 3D Slicer lowers the barrier to adoption and supports the standardisation of workflows across clinical and research centres.

Esophageal cancer (EC) is characterized by complex transcriptional alterations and therapeutic resistance, posing challenges for traditional computational methods. In this study, we propose a deep learning (DL)-based computational framework to identify important genes and biologically relevant pathways in bulk cell RNA-seq data (GSE234304 and GSE273848), which comprise tumor and non-tumor esophageal tissue samples. A fully connected feedforward neural network was trained for binary classification, and two feature selection strategies were implemented: Neural Network followed by Support Vector Regression (NN+SVR) and Integrated Gradients combined with SVR (IG+SVR). The genes were then ranked according to their weights in SVR deriving the importance scores, and the top 100 genes were subjected to enrichment analysis using Enrichr and Metascape. The proposed DL-based approaches identified a greater number of expressed genes across established esophageal cancer cell lines than LIMMA, SAM, and the t-test did. Specifically, in the GSE234304 dataset, IG + SVR, our best method, identified a total of 9 expressed genes while the best baseline method, LIMMA, identified a total of 3 expressed genes. In terms of GSE273848 dataset, IG + SVR was also the best identifying a total of 11 expressed genes while the best baseline method, t-test, had a total of 7 expressed genes. The key genes identified included CEBPB, SUMO1, RORA, STAT1, GATA, OCT1, RUNX1, and NR3C1, as well as pathways related to nucleoprotein maturation, collagen fibril organization, the immunoglobulin-mediated immune response, immune regulation, and insulin signaling. These results show that combining neural networks and attribution-based regression creates an effective and interpretable framework for selecting genes in esophageal cancer research.

Carotid endarterectomy carries the risk of intraoperative cerebral ischemia, which is monitored by expert neurophysiologists through continuous electroencephalography (cEEG). Because expert availability is limited, we developed a hybrid novice-artificial intelligence (AI) system that detects ischemia using novice monitors with limited cEEG training. The hybrid system dynamically weights novice and AI inputs to arrive at a final output. Using four novices, we compared hybrid systems against experts alone, novices alone, and AI alone. Hybrid systems were statistically non-inferior to experts in sensitivity and false-positive rate (FPR), whereas novices alone were not. At 80% sensitivity, hybrid systems reduced FPR by half compared with the AI-only system, with similar benefits at 90% sensitivity. Further, the area under the precision-recall curve improved from 0.546 to 0.610-0.726, the area under the receiver operating characteristic curve improved from 0.957 to 0.967-0.971, and calibration improved compared with AI alone. These results highlight the potential of a hybrid system to monitor intraoperative cerebral ischemia.

Background: Uveal melanoma (UM) and cutaneous melanoma (CM) both originate from the same cell line. This proposes the possibility of a shared mechanism between entities, requiring explicit investigation. Methods: Data from GWAS Catalog and DisGeNET were used to identify shared variation-disease associations (VDAs) between UM and CM. The results were validated using the Ensembl database. In the next step, the STRING database was used to identify the protein-protein interaction. Results: Subsequently, 109 unique VDAs were identified for UM and 880 for CM. However, only 2 VDAs were found to be shared among UM and CM in different ethnic groups. These shared VDAs were rs12203592 of the IRF4 gene, rs12913832 of the HECT and RLD domain-containing E3 ubiquitin protein ligase 2 (HERC2) gene. Notably, PPI network assessment through STRING showcased that OCA2 and IRF4 directly interacted with HERC2. Conclusion: While HERC2 acts as a poor prognostic factor in uveal melanoma, IRF4 status is a key prognostic indicator in both UM and CM. Identifying IRF4 allele contributions enables a better understanding of melanoma pathogenesis and fosters the development of disease-specific approaches.

AO_SCPLOWBSTRACTC_SCPLOWElectroencephalography (EEG) is a promising tool for automated detection of mild cognitive impairment (MCI) and dementia, but comparisons across studies are limited by inconsistent datasets and evaluation protocols. This study benchmarks ten deep learning models across four resting-state EEG datasets and eight binary classification tasks using a unified preprocessing pipeline and five-fold subject-wise cross-validation. Each experiment was repeated ten times. SCCNet obtained the highest mean subject-level accuracy, sensitivity, and F1 score, while ShallowConvNet achieved the highest mean segment-level accuracy, specificity, and precision. Subject-level aggregation improved mean accuracy for all evaluated models, and performance varied substantially across datasets and diagnostic tasks. Higher computational cost did not consistently correspond to better classification performance, with several compact architectures remaining competitive with substantially larger models. The results provide a reproducible reference for comparing EEG-based dementia classification models under consistent subject-independent evaluation conditions.

Deep learning models for electroencephalography (EEG) pathology detection achieve competitive accuracy but offer limited interpretability. In this paper, we introduce concept bottleneck models (CBMs) for EEG pathology detection which use clinical EEG biomarkers computed analytically from the signal as a bottleneck to be used for prediction. Those concepts include band power, spectral ratios, alpha peak frequency and amplitude, hemispheric asymmetry and phase locking values. With the backbone based on shallow convolutional neural network (CNN), we compare a black box baseline with two variants of CBMs. The hybrid variant is a classifier receives a residual projection of the backbone features. The pure variant is a CBM where the classifier receives only the predicted concepts. Models are evaluated on abnormality detection and dementia classification followed by concept prediction quality and leakage analysis. Both CBMs approach the baseline on TUAB (82.7-82.9% vs 83.6%) and are 2-5 percentage points lower on CAUEEG (57.9-60.5% vs 62.7%). The pure CBM exhibits concept fidelity concern where the predicted concepts deviate from their ground truth values. Replacing them with their true values causes severe cost on accuracy by 26 and 22 points on TUAB and CAUEEG. The concept fidelity concern is mitigated by increasing the weight of the concept loss to match the ground truth concepts. the mitigation comes with small accuracy cost on TUAB but a relatively larger cost on CAUEEG. The code of the experiments and full logs are available at https://www.github.com/mhmdrdwn/cbm.

In this work, we present a machine learning model for identifying pathogenic DNA variants. The model was learned from the analysis of normal and pathogenic sequences extracted from the ClinVar database (supported by NCBI). This analysis was based on a conceptual semantic model of DNA sequences converted to peptide sequences (amino acid sequences) governed by a well-defined grammar, which allowed us to apply NLP techniques, specifically Part of Speech tagging (POS tagging). Our predictive model was built by combining two techniques: CRF (from the Markov model family), which performs the sequencing, and BiLSTM (a deep learning model) which captures the past and future content of the sequences. The training space was created with the sequences of 105 genes associated with approximately 27,000 pathogenic variants. The model was evaluated using the metrics precision, P-R and ROC curves, AUC, and confusion matrices. Its performance was also compared against five known methods for predicting pathogenic variants. The results show exceptional performance that exceeds expectations and places this new method at the state of the art for predicting pathogenic DNA sequences.

The Dengue virus (DENV), a major global health concern, causes dengue fever, predominantly affecting tropical and subtropical regions. The NS2B-NS3 protease complex of DENV is critical for viral replication, making it a promising target for antiviral drug development. This study investigates the structural dynamics of the NS2B-NS3 protease under varying pH conditions, integrating experimental and computational approaches. The recombinant NS2B-NS3 protease was expressed in E. coli, purified using affinity chromatography, and analysed for purity via SDS-PAGE. Circular dichroism (CD) spectroscopy revealed pH-dependent secondary structural changes, indicating stability at neutral to slightly basic pH and destabilization under acidic and highly basic conditions. Dynamic light scattering (DLS) analysis demonstrated protein aggregation and structural heterogeneity under extreme pH levels. Complementary in silico techniques, including homology modelling and molecular dynamics (MD) simulations, provided detailed insights into the conformational changes of the protease. The modelled structure, validated and refined through computational tools, revealed structural stability at physiological pH, with notable disruptions at pH extremes. DSSP (Dictionary of Secondary Structure of Proteins) and principal component analyses highlighted significant secondary structural transitions, especially at acidic pH where -helices and {beta}-sheets transformed into random coils Docking and MD simulation was carried out with the small molecule for therapeutic analysis between Dengue NS2bNS3 and small molecule. This study emphasizes the pH-dependent conformational plasticity of the NS2B-NS3 protease, contributing to the understanding of its functional mechanisms and providing a foundation for the rational design of pH-specific inhibitors. These findings underscore the importance of structural biology in advancing therapeutic strategies against dengue fever.

Automated surveillance systems can prove to be extremely useful in detecting outbreaks of respiratory diseases that need further epidemiological investigation. In order for the system to be effective, careful attention has to be paid to the baseline used, absence of the observations, and the use of proper statistical methods. As a solution, the paper describes EpiAlert, a weekly surveillance system based on CDC FluView ILINet. The system is able to transform the number of ILI visits and the total number of visits into a ILI prevalence for the region, create the first reference from the three previous years using a +/- 2-week interval, transform the rate value, and apply two different detectors - CUSUM and EWMA. CUSUM is set at k=0.5 and h=5, whereas EWMA uses {lambda}=0.2 and H=2.7 values. Among the 55 districts, the system gives 45,138 pieces of information about the disease by transforming 44,297 pieces of data into alerts. It is found that there are 2,820 alerts in total (out of which 2,711 alerts used CUSUM, 402 used EWMA, and 293 used both detectors). Additionally, the experiments show that the sensitivity of the device depends greatly on the detector. The latest statistics can be tracked in the report for week 37 of 2026 that recently concluded in West.

The automatic detection of thoracic pathologies remains a challenge when the visual evidence present in the chest X-ray (CXR) is subtle, ambiguous, or practically imperceptible, and the available textual information is limited, especially in the early phases of patient care. To address this limitation, in this work a clinically motivated subset of MIMIC-CXR is taken as the study set, composed of four diseases associated with dyspnea presentations in the emergency department (heart failure, pulmonary infection, COPD/asthma, and pulmonary embolism), along with a control class. This selection allows evaluating the multimodal fusion of visual and textual information in a clinically diverse set where the contribution of each modality can vary depending on the considered pathology and the quality of the available information. In this work, a comparative study of different combinations of visual and textual encoders is presented, with the objective of identifying the most suitable configuration for multimodal fusion. Based on this evaluation, we propose Neural Gated Fusion, a GMU-based architecture that incorporates an adapted Gated Multimodal Unit to regulate the contribution of visual and textual representations, in contrast to strategies based on static feature concatenation or late decision-level fusion. This architecture uses a neural gate to dynamically regulate the contribution of the visual representation of the radiograph and the textual representation of the clinical report before the multi-label classification stage. The experiments conducted on 25,245 multimodal pairs show that the ViT + ClinicalBERT combination with modulated fusion achieves a macro ROC-AUC of 0.8545 and a macro F1-score of 0.6528, outperforming unimodal models, decision-level fusion, and static early fusion. The per-class analysis suggests that the designed fusion strategy especially improves performance in pathologies with limited visual evidence, such as pulmonary embolism, without compromising the detection of normal cases. These results suggest that the dynamic regulation between image and clinical context is an effective strategy for improving the robustness of multimodal thoracic pathology detection systems.

Resting-state electroencephalography (EEG) can capture the slowing of neural oscillations associated with Alzheimers disease (AD), but many machine-learning studies remain difficult to inspect, reproduce, or test. This study developed an interpretable, subject-level AD versus healthy-control classifier from the dataset OpenNeuro ds004504 and deployed it as a public research prototype. Preprocessed eyes-closed EEG recordings from 36 people with AD and 29 healthy controls were analysed. Welch power spectral density estimates were used to generate absolute and relative bandpower summaries, theta/alpha and delta/alpha ratios, a slow/fast ratio, and signal-level descriptors. Five-fold stratified cross-validation compared two Random Forest configurations, EEG Logistic Regression, an age-only baseline, and an EEG-plus-age model. EEG Logistic Regression gave the strongest mean fold-wise performance: accuracy 0.846, balanced accuracy 0.848, F1 score 0.849, and ROC-AUC 0.948. Age alone was near chance, while adding age did not improve the EEG-only model. The feature pattern was consistent with AD-related EEG slowing, including higher slow-wave-related ratios and lower relative alpha power. The trained pipeline was deployed as a Gradio application on Hugging Face Spaces. The result is a reproducible research and educational prototype, not a clinical diagnostic device, and requires external validation before any clinical interpretation.

Barmah Forest Virus (BFV), an arthropod-born virus transmitted by mosquitoes, is of significant public health concern in Australia and regions in the Pacific. Recent climate change and globalization raise the potential for BFV to extend its geographic distribution. Despite the rising importance of BFV, its vector dynamics remain poorly understood, particularly concerning the vector competence of different mosquito species. This study aims to investigate the vector competence of BFV across various mosquito species, beyond those endemic to the Australasian region, especially focusing on global relevant vector species. No transmission was observed for Culex quinquefasciatus and Cx. torrentium as well as Anopheles stephensi. In contrast, both investigated Aedes species, Ae. aegypti as well as Ae. albopictus, exhibited BFV-positive saliva across all four temperature profiles (18{degrees}C, 21{degrees}C, 24{degrees}C or 27{degrees}C) examined. These two invasive mosquito species must therefore be classified as potential vectors for BFV, indicating the potential risk of BFV transmission outside of Australia.

Plaque assays are the gold standard for quantifying infectious virus, yet plaque enumeration is still routinely performed manually, making virus titration labor-intensive, subjective, and difficult to standardize across analysts and laboratories. Existing automated methods primarily address individual tasks, such as plaque segmentation or counting, but do not provide an integrated workflow from plate images to biological quantification. In this paper we present Titra, an end-to-end workflow for automated cytopathic effect (CPE)-based virus titration from standard photographs of plaque assay plates. The workflow combines automatic well detection, plaque segmentation, post-processing for instance separation, plaque counting, and plaque-forming units per millilitre (PFU/mL) estimation within a single web-based platform that also enables experiment management and expert review. The approach was evaluated using images from three viral species (Mayaro virus, Coxsackievirus B3, and vaccinia virus), two plate formats (6- and 12-well), and heterogeneous image acquisition conditions, including both a newly curated dataset and the public VACVPlaque dataset. Automated plaque counts showed strong agreement with manual annotations (Pearson correlation coefficients of 0.98 for MAYV/CVB3 and 0.88 for VACV), while PFU/mL estimates closely matched manual calculations for the MAYV/CVB3 dataset (Pearson r=0.975). Comparative experiments against U-Net, StarDist, HSD-WBR, and PyPlaque demonstrated competitive segmentation and counting performance, with the proposed approach achieving the highest Dice and mAP on the public VACVPlaque dataset.

Haemoglobinopathy registries support research and outcome monitoring. Still, reuse is limited due to heterogeneous structures, registry-specific coding, and incomplete semantic representation. We designed and implemented a FAIRification workflow for the INHERENT haemoglobinopathy platform, an international genotype-phenotype registry, as part of the HemaFAIR project. The workflow was extended to the Cyprus Haemoglobinopathy Patient Registry to demonstrate its applicability across a second registry. Source data and metadata were transformed through two independent but complementary harmonisation branches executed in parallel: one producing an OMOP CDM representation, and the other generating a CARE-SM representation. The workflow generated graph-based semantic resources, predefined query services, public aggregate dashboards, and application programming interface (API) endpoints. Registry metadata were published through the European Rare Disease Registry Infrastructure and a FAIR Data Point. These outputs support findability, interoperable analysis and controlled reuse while preserving existing governance over patient-level data.

PURPOSEPancreatic ductal adenocarcinoma (PDAC) is a deadly malignancy with poor prognosis. A transcriptomic-based algorithm called Purity Independent Subtyping of Tumors (PurIST) relies on expression profiling of a defined set of genes to stratify PDAC into classical and basal-like molecular subtypes and assigns a confidence level to the subtypes as either "strong," "likely," or "lean." In this study, we apply PurIST to Appalachian and non-Appalachian patient cohorts and assess demographic variables between groups that may influence treatment outcomes. PATIENTS AND METHODSThis 13-site study includes 780 PDAC patients. Patients were divided into Appalachian and non-Appalachian cohorts according to Appalachian Regional Commission guidelines, and differences in PurIST classification, tobacco use, and alcohol use were analyzed. RESULTSIndividuals from Appalachian regions were more likely to receive a subtype classification from PurIST with "strong" confidence as opposed to "likely" or "lean" (P=0.0175; odds ratio=1.66). We did not observe any significant difference between basal and classical subtype classification in Appalachia (P=0.1457), yet an exploratory analysis showed that individuals aged [≥]85 were significantly more likely to have basal-like tumor classifications (P=0.005817; odds ratio=3.79). Appalachian patients were more likely to have a history of smoking (59.6% vs 49.9%; P=0.01756) and less likely to report alcohol consumption (50.5% vs 65.6%; P=0.000229). CONCLUSIONWe conducted the largest comparison of the PurIST algorithm in Appalachia to date and found PurIST is more likely to have higher confidence in molecular subtype classifications for Appalachian PDAC tumors compared to non-Appalachian tumors. If PurIST is to be considered as a prognostic or decision-making tool for PDAC patients, we must account for variability in different populations and model the likelihood that tumors will have a strong molecular signature.

Background: Alpha-mannosidosis (AM) is a debilitating lysosomal storage disorder (LSD) caused by insufficient activity of alpha-mannosidase, an enzyme responsible for cleavage of alpha-linked mannose residues from N-glycan-type oligosaccharides during their degradation. As a result, specific oligosaccharides accumulate in tissues and are also detectable in body fluids. AM is a progressive multi-organ disease for which hematopoietic stem cell transplantation (HSCT) and enzyme replacement therapy (ERT) are therapeutic options, however, treatment results depend considerably on early diagnosis. Therefore, inclusion of AM into newborn screening panels would significantly contribute to improving therapeutic outcome. Methods: A rapid and fully quantitative method for the specific AM oligosaccharide biomarker Man2GlcNAc in dried blood spots (DBS) using ultra-performance liquid chromatography coupled to tandem-mass spectrometry was developed. DBS from 420 controls, 27 AM patients (including five newborn screening samples), and 35 patients with other LSDs were analyzed. Results: A distinct separation in biomarker concentrations was observed between unaffected individuals and untreated AM patients (0.1 - 0.8 vs 3.2 - 11.3 mol Man2GlcNAc/L, p<0.0001). Furthermore, Man2GlcNAc concentrations significantly differed between untreated patients and those on ERT (n = 17, p<0.0001) or after HSCT (n = 15, p<0.0001), and between both treatment groups (p<0.0001). Moreover, we demonstrated that this method is appropriate for monitoring treatment efficacy by analysis of samples from individual patients before and after initiation of therapy. We retrospectively analyzed residual samples of dried blood spots (DBS) from routine newborn screening that had been stored from patients with later-confirmed AM, which resulted in clear separation from the concentrations observed in a newborn screening control cohort (7.7 - 11.3 vs 0.1 - 0.8 mol Man2GlcNAc/L, p=0.0001). Conclusion: We present a simple and rapid method for quantitative analysis of the specific AM oligosaccharide biomarker Man2GlcNAc, which is remarkably stable compared to enzymatic assays. Thus, the method is useful for selective screening in suspected cases as well as for monitoring the response to different forms of treatment. Furthermore, Man2GlcNAc has the potential to serve as a useful biomarker for newborn screening offering the prospect of earlier treatment initiation, improved therapeutic outcome and higher quality of life for AM patients.

Nipah is a virus borne zoonotic disease that mostly occurs in Southeast Asian countries. Nipah virus (NiV) infection is potentially lethal as it can cause fatal encephalitis, and as of now, there are no vaccines or therapeutic treatments for this disease. The highly pathogenic NiV belongs to the Paramyxovirus family and the natural host of NiV is fruit bats of the Pteropodidae family. Though NiV has not been detected in the US, rather recently the Camp Hill virus (CHV) in shrew species was identified in Camp Hill, Alabama. This virus is characteristically similar to Nipah, and belongs to the henipavirus genus that include the Nipah and Hendra viruses. NiV has two surface glycoproteins namely the attachment glycoprotein (NiVG) and the fusion glycoprotein (NiVF). Being associated with host cell attachment and fusion of viral membrane, these glycoproteins tend to facilitate the viral entry to the host cell. The present paper describes the structures of the aforesaid viral glycoproteins based on the available database of protein structures and examines if they have an evolutionary link to other viral glycoproteins. Certain similar glycoproteins found in different countries are also explored. The structure-function relationship for these two NiV glycoproteins are determined using time-based molecular dynamics (MD) simulation. The interfacial ligand-receptor interactions between NiV proteins with human receptor and monoclonal antibody, Fab 66, are examined. A primary motivation for these structural studies is that the results might potentially contribute to future therapeutic developments and drug designs to effectively combat NiV.

Neurodegenerative diseases are a major threat to older adults and represent a growing global health burden as the elderly population continues to expand. The complexity of neurodegenerative diseases and the incomplete understanding of their pathophysiology limit the development of effective therapeutics. To study common neurodegenerative mechanisms and accordingly potential drug targets across Alzheimers, Parkinsons, and Huntingtons diseases, transcriptomic profiles of patients with each disease were analyzed to detect common differentially expressed genes and common enriched pathways. Common differentially expressed genes involved in the shared pathways were identified as key drug targets and validated in silico to study the impact of their dysregulation. Three pathways were enriched and upregulated across the three diseases, along with 274 common differentially expressed genes. Two of the shared pathways were involved in activation of the transcription factor nuclear factor kappa B (NF-{kappa}B), indicating inflammatory signaling. The third pathway involved regulation of BCL2L11 transcription by RUNX3, which contributes to the protective effect of neurodegenerative diseases against cancer. Five key drug targets were identified: NFKBIA, NFKB1, RELA, TRIM4, and SMAD4. They were significantly upregulated across all three diseases and involved in the shared pathways. Drugs that target the expression of these genes and previously approved by Food and Drug Administration were reported for treatment repurposing for the three neurodegenerative diseases. The resultant drug list included the conventional and commonly safe antihyperlipidemics, antihypertensives, antidiabetics, analgesics, diuretics, antiparkinsons, and antipsychotics.