Epigenetic regulation through histone acetylation plays a critical role in gene expression and cancer progression. Because of its pivotal role in chromatin remodeling, Histone deacetylase 3 (HDAC3) has become a promising therapeutic target. In this study, an artificial intelligence (AI)-driven strategy was utilized to prioritize potential HDAC3 inhibitors among FDA-approved compounds to accelerate drug repurposing for cancer therapy. Existing HDAC3 inhibitors were identified in the BindingDB and were used to develop a machine learning (ML) model trained on the most potent inhibitors to identify molecular descriptor patterns associated with HDAC3 inhibition. The ML workflow then screened 1615 FDA-approved compounds, yielding 120 candidates with predicted HDAC3 inhibitory activity. Among these, known HDAC inhibitors, including romidepsin, vorinostat, and panobinostat, were selected, suggesting that the workflow can recover known HDAC inhibitors during virtual screening. Interestingly, tyrosine kinase inhibitors such as imatinib and osimertinib were also identified, indicating potential structural overlap between kinase- and HDAC3-binding pharmacophores. The analysis of the predicted docking scores also supported the prioritization results since the top 10 compounds had more negative predicted docking scores than the bottom 10 (p = 0.0074). This shows that the suggested workflow is useful for prioritizing FDA-approved compounds as potential HDAC3 inhibitors for further study.

Current dementia diagnostic methods can be costly, invasive, or limited in their ability to distinguish between disorders with overlapping clinical symptoms. Dysregulated microRNAs (miRNAs) have emerged as promising noninvasive biomarkers for neurodegenerative disease, but individual miRNA changes alone may not capture the complex molecular patterns needed for accurate disease classification. Machine learning provides a way to integrate multiple layers of miRNA-derived information and identify disease-specific biomarker signatures. In this study, we developed machine learning models to classify dysregulated miRNAs associated with Alzheimer’s disease dementia (AD), Lewy body dementia (LBD), and frontotemporal dementia (FTD). Each miRNA was represented using sequence-derived descriptors, predicted gene targets, and KEGG pathway features. The highest-performing models trained on AD, LBD, and FTD achieved 10-fold cross-validation accuracies of 90.6%, 92.9%, and 100%, respectively. When evaluated on independent datasets, the AD, LBD, and FTD models achieved accuracies of 88.9%, 77.8%, and 90.9%, respectively. Cross-disease testing showed reduced performance when models were applied across dementia types, suggesting partially disease-specific miRNA patterns while also indicating overlap among the molecular signatures of AD, LBD, and FTD. These results suggest that machine learning-based integration of miRNA sequence, target-gene, and pathway information can improve the identification of dementia-associated biomarker signatures and may support the future development of noninvasive diagnostic tools for dementia.

Background/Objectives: Autism Spectrum Disorder (ASD) is a set of neurological and neurodevelopmental disorders characterized by difficulties in social communication and interaction, repetitive behaviors, and sensory processing differences. Recent studies have shown that circRNAs play a crucial role in the pathophysiology of ASD. In this study, we present an exploratory machine learning framework integrating circRNA sequence features, miRNA interactions, gene targets, and pathway enrichment analysis to investigate ASD-associated molecular signatures. Methods: Differential circRNAs were identified from human peripheral blood datasets, and informative features were selected using attribute-based filtering and Information Gain ranking. Machine learning models were developed using the WEKA platform. Results: The HyperPipes classifier achieved the highest performance (92.5% accuracy under cross-validation). Analysis using an independent ASD gene expression dataset showed consistent discriminative patterns of the derived gene-level signatures across multiple machine learning classifiers. The competitive endogenous RNA network and enriched gene pathways were also analyzed. Conclusions: Overall, this study provides a computational, preliminary framework for analyzing circRNA-associated molecular patterns in ASD. Findings should be interpreted in the context of limited sample size and dataset availability.

Anelloviruses are among the most prevalent viruses within the human virome, detected in >90% of healthy individuals. Despite their ubiquity, the role of anelloviruses in human health remains elusive. This review examines the potential associations of the anelloviruses torque teno virus (TTV), torque teno midi virus (TTMDV), and torque teno mini virus (TTMV) with various cancers, highlighting the mixed conclusions from current epidemiological studies. Anelloviruses may modulate cancer development through abnormal gene fusion, immune response, and Toll-like receptor 9 (TLR9) activation. On the other hand, anelloviruses might suppress tumor formation through TTV-derived apoptosis-inducing protein (TAIP) and NF-κB signaling inhibition. The high prevalence of anelloviruses in cancer patients could also be attributed to their immunocompromised status rather than a direct causative role of the viruses. This review underscores the need for more comprehensive studies, including in vitro and in vivo experiments, to clarify the role of anelloviruses in cancer development and progression.

Background: COVID-19 has taken millions of lives and continues to affect people worldwide. Post-Acute Sequelae of SARS-CoV-2 Infection (also known as Post-Acute Sequelae of COVID-19 (PASC) or more commonly, Long COVID) occurs in the aftermath of COVID-19 and is poorly understood despite its widespread effects. Methods: We created a machine-learning model that distinguishes PASC from PASC-similar diseases. The model was trained to recognize PASC-dysregulated metabolites (p ≤ 0.05) using molecular descriptors. Results: Our multi-layer perceptron model accurately recognizes PASC-dysregulated metabolites in the independent testing set, with an AUC-ROC of 0.8991, and differentiates PASC from myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), Lyme disease, postural orthostatic tachycardia syndrome (POTS), and irritable bowel syndrome (IBS). However, it was unable to differentiate fibromyalgia (FM) from PASC. Conclusions: By creating and testing models pairwise on each of these diseases, we elucidated the unique strength of the similarity between FM and PASC relative to other PASC-similar diseases. Our approach is unique to PASC diagnosis, and our use of molecular descriptors enables our model to work with any metabolite where molecular descriptors can be identified, as these descriptors can be generated and compared for any metabolite. Our study presents a novel approach to PASC diagnosis that partially circumvents the lengthy process of exclusion, potentially facilitating faster interventions and improved patient outcomes.

Diagnostic practices for schizophrenia are unreliable due to the lack of a stable biomarker. However, machine learning holds promise in aiding in the diagnosis of schizophrenia and other neurological disorders. Dysregulated miRNAs were extracted from public sources. Datasets of miRNAs selected from the literature and random miRNAs with designated gene targets along with related pathways were assigned as descriptors of machine-learning models. These data were preprocessed and classified using WEKA and TensorFlow, and several classifiers were tested to train the model. The Sequential neural network developed by authors performed the best of the classifiers tested, achieving an accuracy of 94.32%. Naïve Bayes was the next best model, with an accuracy of 72.23%. MLP achieved an accuracy of 65.91%, followed by Hoeffding tree with an accuracy of 64.77%, Random tree with an accuracy of 63.64%, Random forest, which achieved an accuracy of 61.36%, and lastly ADABoostM1, which achieved an accuracy of 53.41%. The Sequential neural network and Naïve Bayes classifier were tested to validate the model as they achieved the highest accuracy. Naïve Bayes achieved a validation accuracy of 72.22%, whereas the sequential neural network achieved an accuracy of 88.88%. Our results demonstrate the practicality of machine learning in psychiatric diagnosis. Dysregulated miRNA combined with machine learning can serve as a diagnostic aid to physicians for schizophrenia and potentially other neurological disorders as well.

The V-RAF murine sarcoma viral oncogene homolog B1 (BRAF) gene’s V600E mutation disrupts the MAP kinase/ERK-signaling pathway and is present in approximately 50

BACKGROUND AND OBJECTIVES:Prior studies have shown that small non-coding RNAs (sncRNAs) are associated with cancer occurrence or development. Recently, a newly discovered class of small ncRNAs known as PIWI-interacting RNAs (piRNAs) have been found to play a vital role in physiological processes and cancer initiation. This study aims to utilize piRNAs as innovative, noninvasive diagnostic biomarkers for breast cancer. Our objective is to develop computational methods that leverage piRNA attributes for breast cancer prediction and its application in diagnostics. METHODS:We created a set of piRNA sequence descriptors using information extracted from the piRNA sequences. To ensure accuracy, we found a path to convert non-standard piRNA names to standard ones to enable precise identification of these sequences. Using these descriptors, we applied machine-learning (ML) techniques in WEKA (Waikato Environment for Knowledge Analysis) to a dataset of piRNA to assess the predictive accuracy of the following classifiers: Logistic Regression model, Sequential Minimal Optimization (SMO), Random Forest classifier, and Logistic Model Tree (LMT). Furthermore, we performed Shapley additive explanations (SHAP) Analysis to understand which descriptors were the most relevant to the prediction accuracy. The ML models were then validated on an independent dataset to evaluate their effectiveness in predicting breast cancer. RESULTS:The top three performing classifiers in WEKA were Logistic Regression, SMO, and LMT. The Logistic Regression model achieved an accuracy of 90.7% in predicting breast cancer, while SMO and LMT attained 89.7% and 85.65%, respectively. CONCLUSIONS:Our study demonstrates the effectiveness of using ML-based piRNA classifiers in diagnosing breast cancer and contributes to the growing body of evidence supporting piRNAs as biomarkers in cancer diagnosis. However, additional research is needed to validate these findings and further assess the clinical applicability of this approach.

Glioblastoma (GBM) represents one of the most aggressive brain tumors with a poor prognosis despite decades of research. Epigenetic regulation has emerged as a promising strategy for managing aggressive cancers, such as GBM, by modulating pro-tumorigenic gene expression. The role of pro-tumorigenic genes, such as oligodendrocyte transcription factor 2 (OLIG2), has been heavily associated with cancer progression and treatment resistance and is a potential target for GBM. The objective of this study is to analyze the effectiveness of various epigenetic regulators, including histone modifiers, DNA methylases, chromatin remodelers, and miRNAs, on OLIG2 expression, including the effectiveness of individual epigenetic regulators and their combinations. The effects of epigenetic regulators in GBM that are found in the literature were reviewed for their survival and co-expression with OLIG2. We found that KDM6B, BRG1, DNMT1, and HDAC2 were associated with significant co-expression with OLIG2 and decreased survival in GBM patients, reinforcing their suitability as targets. Additionally, miR-17-3p miRNAs associated with silencing OLIG2 as gene expression was downregulated in GBM. Additionally, this paper highlights the potential of combination therapies targeting multiple epigenetic pathways simultaneously. A kinase inhibitor (alisertib), together with JQ1, reduced the tumor growth of GBM cells in vivo more than either treatment alone, making combination therapies a promising solution.

According to the Vision Health Initiative Project, glaucoma, a common eye disease that damages the optic nerve, is the second leading cause of blindness worldwide. Glaucoma, if treated at its initial stages, allows one to avoid permanent vision impairment. The problem arises because, at early stages, there are no visible symptoms, which prevent correct diagnostics. Our goal is to develop reliable methods to diagnose the early stages of glaucoma using ‘omics data and machine learning (ML). Collecting metabolomic data from public sources—articles and databases, we preprocessed them, calculated descriptors, and then fed the data into different WEKA’s algorithms, creating the models and training and testing them. We also created datasets of random metabolites independent of glaucoma. The testing of several models shows that the best is the Hoeffding tree model, revealing an accuracy of 90.91

INTRODUCTION:Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease characterized by loss of motor neurons. Current medications are largely ineffective, associated with side effects, and hindered by a lack of agreement over treatment pathways. The time-intensive process and high costs further limit the development of therapeutics. Therefore, this research aimed to identify FDA-approved drugs that inhibit three proteins (Casein kinase 1, Protein tyrosine kinase 2, Ephrin type-A receptor 4) associated with ALS. METHODS:A machine learning (ML) model was trained for each protein to identify an inputted compound as an active inhibitor of that protein. The FDA-approved drugs were then screened through these models, and 18 drugs were identified as likely inhibitors for all three proteins. The results were validated through protein-ligand docking of each drug to its respective protein(s). RESULTS:Risperidone was the most active drug, with an average ML score of 1 and binding affinity of -8.9. The ML scores and binding affinities had a strong correlation, indicating reliability. CONCLUSION:This research predicted multiple drugs that can simultaneously target many proteins involved in ALS, creating more effective treatment options at a lower cost. This procedure can be applied to efficiently discover drugs for other diseases in the future.

JOURNAL/atin/04.03/02274269-202506000-00002/figure1/v/2026-04-23T113433Z/r/image-tiff Autism spectrum disorder is a neurodevelopmental disorder characterized by differences in social behaviors, intellectual disabilities, and various mental health conditions. It is often undiagnosed due to overlapping symptoms with other disorders and the challenging, subjective nature of behavioral analysis. However, recent studies have identified dysregulated microRNAs as potential biomarkers for autism spectrum disorder, which could enable more accurate quantitative diagnoses. This study aimed to develop a machine learning model to predict whether dysregulation of a specific miRNA is associated with autism spectrum disorder. We selected an even number of autism spectrum disorder-associated miRNAs and randomly chosen miRNAs for analysis. Data was collected on amino acid sequences, gene targets, and predicted pathway attributes to classify each microRNA. Feature selection was then performed to identify the optimal number of features for achieving the highest accuracy. Only statistically significant predictions ( P < 0.05) were included in the training dataset. The sequential model with two hidden layers emerged as the best classifier, achieving an accuracy of 95.24% for microRNA biomarkers. This model was further validated with an independent, unseen dataset, which achieved 81.67% accuracy. The study also explored the genes and pathways of significance to understand better potential causes of autism spectrum disorder, particularly those involved in regulating the pluripotency of stem cells. This study presents a rapid and efficient method for classifying microRNAs as potential biomarkers for autism spectrum disorder based on their biological characteristics. By screening for dysregulated microRNAs in patients’ blood or serum samples, this approach can enhance early diagnosis and timely intervention.

Medulloblastoma (MB) is the most common malignant brain tumor in children. Current diagnostic methods, such as MRI and lumbar puncture, are invasive and not sensitive enough, making early diagnosis challenging. MicroRNAs (miRNAs) have emerged as promising biomarkers for cancer diagnosis due to their dysregulated expression in tumors. This study aims to develop a novel machine learning (ML)-based diagnostic tool for MB using miRNA biomarkers. We collected miRNAs associated with MB and random controls, generating sequence- and target gene-based descriptors. We employed the WEKA software to evaluate several ML models, including logistic regression, naïve Bayes, and multilayer perceptron (MLP). Attribute selection reduced noise by selecting the most significant 24 features. Model performance was evaluated using 10-fold cross-validation and independent test datasets. Logistic regression achieved the highest training accuracy (96.2

JOURNAL/atin/04.03/02274269-202512000-00001/figure1/v/2025-10-03T110833Z/r/image-tiff Epileptic patients experience recurrent seizures, which can lead to loss of consciousness or death. Recent studies have shown that microRNAs (miRNAs) play a crucial role in the onset of epilepsy. In this study, we developed machine learning models for diagnosing epilepsy using miRNA biomarkers. We used nucleotide sequence information and related target genes as biomarkers for each miRNA. This study utilized epilepsy-associated miRNAs and miRNAs from healthy individuals. We obtained a classification accuracy of 95.45% with the Naïve Bayes Multinomial classifier. We also used other classifiers, including Hoeffding Tree, Sequential Minimal Optimization, Random Tree, and Logistic Regression classifiers, which achieved reasonably high accuracies. These models were then tested on the epilepsy miRNAs from two independent datasets, and reasonable classification accuracies were observed. These findings indicate that the high performance of these models developed in this study suggests strong correlations between miRNA sequence information, target gene patterns, and their association with epilepsy. By combining our machine learning approach with a cost-effective miRNA testing strategy, this approach could be utilized as a novel method for epilepsy diagnosis.

Background: Traditional diagnostic methods like excisional biopsy, fine needle aspiration (FNA), and core needle biopsy (CNB) are often challenged by sampling errors and high false-negative rates. Our research shifts focus to microRNA (miRNA) expression profiling, leveraging the stability of miRNA molecules and advanced RNA extraction methods. Although the oncogenic potential of miRNAs in B-cell lymphoma has been studied since 2005, and various dysregulated miRNAs in diffuse large B-cell lymphoma (DLBCL) patients have been reported in the scientific literature, there has been limited research investigating these miRNAs using ML algorithms. Methods: This study presents an innovative approach to the diagnosis of DLBCL using a machine-learning (ML) system based on miRNA analysis. We first identified 54 miRNAs associated with DLBCL, combining them with 54 random miRNAs to create a training dataset for ML classifiers. This dataset was processed using various ML classifiers through the Waikato Environment for Knowledge Analysis (WEKA) software. In addition to miRNA profiling, our study also explored the biological pathways associated with these miRNAs using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Results: Our training model achieved a notable accuracy of 93.52%. The performance was further validated with three independent datasets derived from actual tumor samples, showing best accuracies from 86.36% to 100%. We identified several enriched pathways, such as the PI3K and FoxO signaling pathways, that are significantly implicated in DLBCL. These findings not only validate known associations but also reveal potential new avenues for understanding DLBCL pathogenesis. Conclusions: Our paper demonstrates that ML-assisted miRNA analysis can serve not only as a diagnostic tool for the onset of DLBCL but also as a discovery tool to predict specific genes, pathways, and sequence motifs as targets for further investigation.

The diagnosis of neurological diseases can be expensive, invasive, and inaccurate, as it is often difficult to distinguish between different types of diseases with similar motor symptoms. However, the dysregulation of miRNAs can be used to create a robust machine-learning model for a reliable diagnosis of neurological diseases. We used miRNA sequence descriptors and gene target data to create machine-learning models that can be used as diagnostic tools. The top-performing machine-learning models, trained on filtered miRNA datasets for Amyotrophic Lateral Sclerosis, Alzheimer’s and Parkinson’s Diseases of this research yielded 94, 97, and 96, percent accuracies, respectively. Analysis of dysregulated miRNA in neurological diseases elucidated novel biomarkers that could be used to diagnose and distinguish between the diseases. Machine-learning models developed using sequence and gene target descriptors of miRNA biomarkers can achieve favorable accuracies for disease classification and attain a robust discerning capability of neurological diseases.

Life-threatening thrombotic events and neurological symptoms are prevalent in COVID-19 and are persistent in patients with long COVID experiencing post-acute sequelae of SARS-CoV-2 infection1-4. Despite the clinical evidence1,5-7, the underlying mechanisms of coagulopathy in COVID-19 and its consequences in inflammation and neuropathology remain poorly understood and treatment options are insufficient. Fibrinogen, the central structural component of blood clots, is abundantly deposited in the lungs and brains of patients with COVID-19, correlates with disease severity and is a predictive biomarker for post-COVID-19 cognitive deficits1,5,8-10. Here we show that fibrin binds to the SARS-CoV-2 spike protein, forming proinflammatory blood clots that drive systemic thromboinflammation and neuropathology in COVID-19. Fibrin, acting through its inflammatory domain, is required for oxidative stress and macrophage activation in the lungs, whereas it suppresses natural killer cells, after SARS-CoV-2 infection. Fibrin promotes neuroinflammation and neuronal loss after infection, as well as innate immune activation in the brain and lungs independently of active infection. A monoclonal antibody targeting the inflammatory fibrin domain provides protection from microglial activation and neuronal injury, as well as from thromboinflammation in the lung after infection. Thus, fibrin drives inflammation and neuropathology in SARS-CoV-2 infection, and fibrin-targeting immunotherapy may represent a therapeutic intervention for patients with acute COVID-19 and long COVID.

Objective biomarkers are crucial for early diagnosis to promote treatment and raise survival rates for diseases. With the smallest non-coding RNAs—piwi-RNAs (piRNAs)—and their transcripts, we sought to identify if these piRNAs could be used as biomarkers for colorectal cancer (CRC). Using previously published data from serum samples of patients with CRC, 13 differently expressed piRNAs were selected as potential biomarkers. With this data, we developed a machine learning (ML) algorithm and created 1020 different piRNA sequence descriptors. With the Naïve Bayes Multinomial classifier, we were able to isolate the 27 most influential sequence descriptors and achieve an accuracy of 96.4%. To test the validity of our model, we used data from piRBase with known associations with CRC that we did not use to train the ML model. We were able to achieve an accuracy of 85.7% with these new independent data. To further validate our model, we also tested data from unrelated diseases, including piRNAs with a correlation to breast cancer and no proven correlation to CRC. The model scored 44.4% on these piRNAs, showing that it can identify a difference between biomarkers of CRC and biomarkers of other diseases. The final results show that our model is an effective tool for diagnosing colorectal cancer. We believe that in the future, this model will prove useful for colorectal cancer and other diseases diagnostics.

Background: Feline mammary carcinoma (FMC) is a prevalent and fatal carcinoma that predominantly affects unspayed female cats. FMC is the third most common carcinoma in cats but is still underrepresented in research. Current diagnosis methods include physical examinations, imaging tests, and fine-needle aspiration. The diagnosis through these methods is sometimes delayed and unreliable, leading to increased chances of mortality. Objectives: The objective of this study was to identify the biomarkers, including blood metabolites and genes, related to feline mammary carcinoma, study their relationships, and develop a machine learning (ML) model for the early diagnosis of the disease. Methods: We analyzed the blood metabolites of felines with mammary carcinoma using the pathway analysis feature in MetaboAnalyst software, v. 5.0. We utilized machine-learning (ML) methods to recognize FMC using the blood metabolites of sick patients. Results: The metabolic pathways that were elucidated to be associated with this disease include alanine, aspartate and glutamate metabolism, Glutamine and glutamate metabolism, Arginine biosynthesis, and Glycerophospholipid metabolism. Furthermore, we also elucidated several genes that play a significant role in the development of FMC, such as ERBB2, PDGFA, EGFR, FLT4, ERBB3, FIGF, PDGFC, PDGFB through STRINGdb, a database of known and predicted protein-protein interactions, and MetaboAnalyst 5.0. The best-performing ML model was able to predict metabolite class with an accuracy of 85.11%. Conclusion: Our findings demonstrate that the identification of the biomarkers associated with FMC and the affected metabolic pathways can aid in the early diagnosis of feline mammary carcinoma.

Introduction: Pancreatic ductal adenocarcinoma (PDAC) has the lowest survival rate among all major cancers due to a lack of symptoms in early stages, early detection tools, and optimal therapies for late-stage patients. Thus, effective and non-invasive diagnostic tests are greatly needed. Recently, circulating miRNAs have been reported to be altered in PDAC. They are promising biomarkers because of stability in the blood, ease of non-invasive detection, and convenient screening methods. This study aimed to use blood-based miRNA biomarkers and various analysis methods in the development of a machine-learning (ML) model for PDAC. Methods: Blood-based miRNAs associated with PDAC were collected from open sources. miRNA sequences, targeted genes, and involved pathways were used to construct a set of descriptors for an ML model. Results: Bioinformatics analysis revealed that most genes in pancreatic cancer and insulin signaling pathways were targeted by the PDAC-related miRNAs. The best-performing ML model with the Random Forest classifier was able to achieve an accuracy of 88.4%. Model evaluations of an independent PDAC-associated miRNAs test set had 100% accuracy while non-cancer miRNAs had 52.4% accuracy, indicating specificity to PDAC. Conclusions: Our results suggest an ML model developed using blood-based miRNA biomarkers' target gene, pathway, and sequence features could be potentially implicated in PDAC diagnostics.