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Artificial Intelligence in the Management of Hereditary and Acquired Hemophilia: From Genomics to Treatment Optimization.

Hemophilia, an X-linked bleeding disorder, is characterized by a deficiency in coagulation factors. It manifests as spontaneous bleeding, leading to severe complications if not properly managed. In contrast, acquired hemophilia is an autoimmune condition marked by the development of inhibitory antibodies against coagulation factors. Both forms present significant diagnostic and therapeutic challenges, highlighting the need for advanced genetic, molecular, laboratory, and clinical assessments. Recent advances in artificial intelligence have opened new avenues for the management of hemophilia. M

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… For example, convolutional neural networks (CNNs) have demonstrated high performance in identifying leukemic blasts, while algorithms such as support vector machines (SVMs) and random forests are used to predict patient outcomes and treatment responses based on molecular data. …

Endothelial activation and stress index associated with in-hospital mortality risk in patients with end-stage renal disease: a retrospective analysis based on the MIMIC database and machine learning model development.

BACKGROUND: End-stage renal disease (ESRD) is a severe chronic renal disorder with high mortality, requiring dialysis treatment or kidney transplantation. The endothelial activation and stress index (EASIX), reflecting inflammatory status and endothelial dysfunction, has demonstrated predictive value for outcomes in multiple diseases. However, its association with in-hospital mortality (IHM) risk in ESRD patients remains unclear, and machine learning prediction models remain unestablished. METHODS: Clinical data for ESRD patients were extracted from the MIMIC-IV (3.1) database. The outcome wa

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… Finally, combining the EASIX indicators and ML methods, six ML models were established, including logistic regression (LR), random forest (RF), neural network (NN), gradient boosting machine (GBM), support vector machine (SVM), and eXtreme Gradient Boosting (XGboost). … vector machine White blood cell count eXtreme gradient boosting References 1. …

Research on RNA modification in disease diagnosis and prognostic biomarkers: current status and challenges.

RNA modification, as a crucial post-transcriptional regulatory mechanism, plays a pivotal role in normal physiological processes and is closely associated with the onset and progression of various human diseases. Recent studies have highlighted significant alterations in the level of RNA modifications, including m6A, m6Am, m1A, m5C, m7G, ac4C, Ψ, and A-to-I editing, across multiple diseases. These findings suggest the potential of RNA modifications and their regulatory factors as biomarkers for early disease diagnosis and prognosis. This review provides an overview of statistical methods, mach

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… Similarly, Wang et al. [ 61 ] used the intersection of the feature selection results of three machine learning methods, namely LASSO, RF, and support Vector Machine recursive feature elimination (SVM-RFE), as the diagnostic feature gene for pulmonary arterial hypertension (PAH). … Applications of support vector machine (SVM) learning in cancer genomics. Cancer Genomics Proteomics 2018;15:41–51. 10.21873/cgp.20063. 11. Kotsiantis SB. Decision trees: A recent overview. Artif Intell Rev 2013;39:261–83. 10.1007/s10462-011-9272-4. 12. Chen T, Guestrin C. …

Public Omics Explorer (POE): Создание возможностей для комплексного семантического поиска по наборам данных GEO omics на основе публикаций PubMed.

Экспоненциальный рост общедоступных массивов данных об омиках и биомедицинской литературы создает как возможности, так и проблемы для открытия данных в области биологических наук. В то время как " Gene Express Omnibus " (GEO) содержит миллионы высокопроизводительных экспериментальных наборов данных, Европейский архив нуклеотидов (ENA) хранит соответствующие исходные данные о последовательности, а " PubMed " содержит большой объем соответствующих научных публикаций, комплексное исследование этих ресурсов по-прежнему носит ограниченный характер. Мы представляем "Public Omics

Integrative multi-omics analysis reveals CXCL10-driven inflammation and TREM2 + macrophage-plasma cell survival niche as hallmarks of late-stage rheumatoid arthritis.

BACKGROUND: Rheumatoid arthritis (RA) is characterized by persistent synovial inflammation, yet the molecular mechanisms distinguishing early from late-stage disease remain incompletely elucidated. Identifying stage-specific biomarkers and pathogenic cellular interactions is crucial for precision medicine. OBJECTIVE: To comprehensively characterize the transcriptomic landscape and cellular composition of early versus late RA synovium, identify diagnostic biomarkers, and elucidate key pathogenic cell–cell interactions driving disease chronicity. METHODS: Synovial tissues from 51 RA patients (13

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… Support Vector Machine-Recursive Feature Elimination (SVM-RFE) SVM-RFE was performed using the e1071 (v1.7–12) and caret (v6.0–93) packages in R. … G Venn diagram of LASSO, random forest (RF), and support vector machine (SVM) screening results. …

Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.

Artificial intelligence (AI) is increasingly advancing precision immunotherapy by integrating high-dimensional biomedical data to support diagnosis, treatment selection, and longitudinal monitoring in both cancer and autoimmune diseases. This review summarizes AI applications in biomarker discovery, prediction of immune checkpoint inhibitor (ICI) response and toxicity, neoantigen prioritization, CAR-T cell optimization, and therapeutic antibody engineering. In oncology, multimodal models combining multi-omics, medical imaging, and clinical variables improve patient stratification and non-invas

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… Accordingly, we cover representative AI approaches ranging from classical machine learning (e.g., regularized regression, random forests, support vector machines) to deep learning architectures—including convolutional neural networks (CNNs) for radiology and whole-slide pathology, and transformer-based … Various AI algorithms, such as support vector machines, logistic regression, neural networks, and decision trees, are utilized in diabetes prediction( 258 ). These algorithms use diverse mathematical frameworks to recognize patterns and generate predictions. …

Established risk prediction models for the incidence of a low lean tissue index in patients with peritoneal dialysis.

ObjectiveThe objective of this study is to investigate the incidence of low lean tissue index (LTI) and the risk factors for low LTI in peritoneal dialysis (PD) patients, including to establish risk prediction models.MethodsA total of 104 PD patients were enrolled from October 2019 to 2021. LTI was measured by bioimpedance spectroscopy. Multivariate logistic regression and machine learning were used to analyze the risk factors for low LTI in PD patients. Kaplan-Meier analysis was used to analyze the survival rate of patients with low LTI.ResultsThe interleukin-6 (IL-6) level, red cell distribu

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… Five classifiers, random forest (RF), gradient boosted decision tree (GBDT), decision tree (DT), gradient lifting (GBM), and support vector machine (SVM), were used to construct prediction models based on training data. … DT: decision tree; SVM: support vector machine; RF: random forest; GBDT: gradient boosted decision tree; GBM: gradient boosted machine; AUC: area under the curve. The boldface fonts mean that DT, GBDT and GBM show the best performance in terms of AUC values. …

Gene therapy of rare diseases as a milestone in medicine - overview of the field and report on initial experiences in Slovenia.

Gene therapy has transitioned from a long-awaited promise to a clinical reality, offering transformative treatments for rare congenital diseases and certain cancers, which have a significant impact on patients' lives. Current approaches focus on gene replacement therapy, either in vivo or ex vivo, mostly utilizing viral vectors to deliver therapeutic genes into target cells. However, refining these techniques is essential to overcome challenges and complications associated with gene therapy to ensure long-term safety and efficacy. Slovenia has witnessed significant advancements in this field s

Artificial Intelligence in Bone Metastases: An MRI and CT Imaging Review.

(1) Background: The purpose of this review is to study the role of radiomics as a supporting tool in predicting bone disease status, differentiating benign from malignant bone lesions, and characterizing malignant bone lesions. (2) Methods: Two reviewers conducted the literature search independently. Thirteen articles on radiomics as a decision support tool for bone lesions were selected. The quality of the methodology was evaluated according to the radiomics quality score (RQS). (3) Results: All studies were published between 2018 and 2021 and were retrospective in design. Eleven (85%) studie

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… vector machine, k-nearest neighbor, random forest, artificial neural networks (ANNs), and naïve Bayes | Histogram features, GLCM, GRLM, and an autoregressive model (282) | Differentiating myeloma and metastasis, ANN T2W-FS: AUC = 0.815, SEN = 0.879, SPE = 0.790; differentiating myeloma and metastasis … Xiong et al. [ 20 ] evaluated the discrimination ability in T1W and T2W-FS MRI sequences between bone lesions from multiple myeloma and metastasis through several machine learning models: support vector machine (SVM), k-nearest neighbor (KNN), random forest (RF), artificial neural networks (ANNs), and …

Radiomics in spinal research: a narrative review.

Radiomics has emerged as a transformative tool in medical imaging, offering the ability to extract complex quantitative features that are generally indiscernible to the human eye. These radiomic features can be interrogated non-invasively on medical imaging. Importantly, in contrast to traditional diagnostic methods reliant on a single, often scalar, measure, radiomics features can form a high-dimensional data space from imaging data suitable for machine learning. Within the framework of artificial intelligence, radiomic features can be harnessed for differentiating healthy from pathological t

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… Support vector machine (SVM) computes the separating hyperplane between data points, using kernel transformations allowing non-linear classification ( Wang and Hu, 2005 ). TABLE 1 Machine learning algorithms in medical imaging. … Xue et al. (2022) extracted 1,197 handcrafted radiomic features from lumbar spine CT images of 133 patients, applied a machine learning approach for model selection (Support vector machine (SVM), random forest (RF), and K-nearest neighbor (KNN)). …

The Applications of Machine Learning in the Management of Patients Undergoing Stem Cell Transplantation: Are We Ready?

Hematopoietic stem cell transplantation (HSCT) is a life-saving therapy for hematologic malignancies, such as leukemia and lymphoma and other severe conditions but is associated with significant risks, including graft versus host disease (GVHD), relapse, and treatment-related mortality. The increasing complexity of clinical, genomic, and biomarker data has spurred interest in machine learning (ML), which has emerged as a transformative tool to enhance decision-making and optimize outcomes in HSCT. This review examines the applications of ML in HSCT, focusing on donor selection, conditioning re

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… Similar work has been conducted by Buturovic et al. who created a multivariate Support Vector Machine (SVM) classifier with the intention to identify preferred unrelated donors for a given recipient with AML and ALL. … The time frame considered was 2014–2024. kNN = k-Nearest Neighbors, VM = Support Vector Machine, ANN = Artificial Neural Network, RF-DT = random forest (RF) and decision trees (DT). …

Artificial intelligence in cancer immunotherapy: current trends in predicting response and personalizing treatment.

Artificial intelligence (AI) can transform cancer immunotherapy by enabling more accurate prediction of treatment responses, the discovery of specific biomarkers, and the development of personalised treatment plans. Traditional single-marker biomarkers (PD-L1, TMB, MSI) lack consistency across tumour types and cannot be used to assess tumour heterogeneity or the dynamic tumour microenvironment (TME). This review synthesises developments in multimodal AI models that combine genomics, transcriptomics, radiomics, digital pathology (pathomics), circulating biomarkers, and clinical evidence to crea

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… Multimodal AI is employed to predict MSI status and immune response, as well as survival, in many studies that employ deep learning, support vector machines, and ensemble methods to obtain robust predictions. … CatBoost, logistic regression, neural networks, and support vector machines are among the models tested to assess their predictive capabilities for outcomes such as disease control rate, overall survival, and time to treatment failure [ 16 ]. …

KIF11 promotes rheumatoid arthritis pathogenesis by activating M1 macrophage polarization and promoting inflammatory cytokine secretion.

BACKGROUND AND OBJECTIVE: KIF11, a mitotic kinesin, has been implicated in oncogenesis, but its specific role and mechanism in RA pathogenesis remain largely unexplored. We aim to explore functional role and molecular mechanism of KIF11 to promote RA progression. METHODS: Bioinformatics analysis was first performed on public RA datasets (GSE55457, GSE55235, GSE2053, GSE12021, and GSE1919) to identify KIF11-associated DEGs and their enriched pathways using GO/KEGG and GSEA analysis. Lentivirus-mediated shRNA was employed to knock down KIF11 expression in MH7A cells and M1-polarized macrophages

Nanoparticle-based strategy in CAR-T cell immunotherapy: challenges, implications, and perspectives.

Chimeric antigen receptor (CAR)-T cell therapy has achieved remarkable progress in treating hematologic malignancies, yet its broader application faces challenges such as manufacturing complexity, solid tumor microenvironment barriers, and immune toxicity. Nanoparticles (NPs), leveraging their precise delivery, immunomodulation, and multifunctional integration capabilities, offer innovative strategies to optimize CAR-T cell therapy. This review provides a comprehensive elucidation of the fundamental framework of CAR-T cell therapy and the challenges in oncological applications. Subsequently, w

AI predictive modeling of survival outcomes for renal cancer patients undergoing targeted therapy.

Renal clear cell cancer (RCC) is a complex disease that is challenging to predict patient outcomes. Despite improvements with targeted therapy, personalized treatment planning is still needed. Artificial intelligence (AI) can help address this challenge by developing predictive models that accurately forecast patient survival periods. With AI-powered decision support, clinicians can provide patients with tailored treatment plans, enhancing treatment efficacy and quality of life. The study analyzed 267 patients with renal clear cell carcinoma, focusing on 26 who received targeted drug therapy.

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… The OpenCV library in Python was utilized to carry out Otsu threshold segmentation, followed by classification of the segmented images using a Support Vector Machine (SVM). Two metrics was to evaluate the performance of our model: accuracy and Intersection over Union (IoU) 30 , 31 . … Results In the comparative experiment, it was observed that the classification accuracy (ACC) of the model utilizing 3D Slicer for information extraction and applying it to the support vector machine (SVM) was only 0.33. …

Изучение общих генных сигнатур и механизмов между тремя аутоиммунными заболеваниями по массовому секвенированию РНК в сочетании с последовательности одноклеточного РНК.

ВАЖНЕЙШАЯ ИНФОРМАЦИЯ: Появляющиеся данные подчеркивают наличие механизмов коморбидности среди аутоиммунных заболеваний (АИД), при этом инновационные технологии, такие, как секвенирование РНК в одной клетке (scRNA-seq), значительно улучшают разведку в этой области. Цель этого исследования заключалась в изучении общих генов трех AID-Multiple Sclerosis (MS), Systemic Lupus Erythematosus (SLE) и Rheumatoid Artritis (RA) с использованием баз данных биоинформатики, а также в выявлении потенциальных биомаркеров для ранней диагностики. Мы извлекли стеноскопические данные о пациентах MS, SLE и RA из об

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… Machine learning algorithms To further identify candidate biomarkers, three machine learning algorithms were applied: least absolute shrinkage and selection operator (LASSO) regression, random forest, and support vector machine-recursive feature elimination (SVM-RFE). … LASSO, least absolute shrinkage and selection operator; SVM-RFE, support vector machine recursive feature elimination; AMI, acute myocardial infarction. …

Development of a Multimodal Machine Learning Model for Predicting Second Osteoporotic Vertebral Compression Fractures.

PURPOSE: Osteoporosis increases the probability of osteoporotic vertebral compression fractures (OVCF), and the resultant pain severely limits physical activity and increases mortality and morbidity. This study aimed to develop a high-precision multimodal prediction model to forecast the risk of a second OVCF. MATERIALS AND METHODS: This retrospective study included 178 patients from a single institution with a first OVCF between January 1, 2000, and December 31, 2019. The study dataset included 18 preoperative clinical variables, including demographics, medications, comorbidities, bone miner

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… Random forest, 18 CatBoost, 19 linear regression (LR), and support vector machine (SVM) 20 were tested. The number of PCA components was systematically varied from 10 to 100 to identify the optimal configuration for ensuring predictive accuracy. … vector machines arXiv [Preprint] 2013 accessed on 2025 February 3 10.48550/arXiv.1306.0239 21 Lundberg SM Lee SI A unified approach to interpreting model predictions Guyon I von Luxburg U Bengio S Wallach H Fergus R Vishwanathan S Advances in Neural Information Processing Systems 30 (NeurIPS 2017) …

Astatine-211 based radionuclide therapy: Current clinical trial landscape.

Astatine-211 (211At) has physical properties that make it one of the top candidates for use as a radiation source for alpha particle-based radionuclide therapy, also referred to as targeted alpha therapy (TAT). Here, we summarize the main results of the completed clinical trials, further describe ongoing trials, and discuss future prospects.

Blood cancer prediction model based on deep learning technique.

Blood cancer is among the critical health concerns among people around the world and normally emanates from genetic and environmental issues. Early detection becomes essential, as the rate of death associated with it is high, to ensure that the rate of treatment success is up, and mortality reduced. This paper focuses on improving blood cancer diagnosis using advanced deep learning techniques like ResNetRS50, RegNetX016, AlexNet, Convnext, EfficientNet, Inception_V3, Xception, and VGG19. Among the models assessed, ResNetRS50 had better accuracy and speed with minimal error rates compared with

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… During the classification study, the traditional machine learning classifiers involved were the artificial neural network and the support vector machine. … They both shared a specificity level of 95.31%, while the support vector machine had levels like that of the sensitivity for the artificial neural network. …

Logic programming reveals alteration of key transcription factors in multiple myeloma.

Innovative approaches combining regulatory networks (RN) and genomic data are needed to extract biological information for a better understanding of diseases, such as cancer, by improving the identification of entities and thereby leading to potential new therapeutic avenues. In this study, we confronted an automatically generated RN with gene expression profiles (GEP) from a cohort of multiple myeloma (MM) patients and normal individuals using global reasoning on the RN causality to identify key-nodes. We modeled each patient by his or her GEP, the RN and the possible automatically detected r