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Identification and Validation of the Diagnostic Markers for Inflammatory Bowel Disease by Bioinformatics Analysis and Machine Learning.

Inflammatory bowel disease (IBD) is a chronic inflammatory disorder of the gastrointestinal tract which is mediated by the inappropriate imm … way enrichment analysis of DEGs were performed by clusterProfiler package. The LASSO regression and support vector machine recursive feature elimination (SVM-RFE) algorithms were conducted to identify novel diagnostic biomarkers. The receiver operating characteristic (ROC) curve was applied to evaluate the diagnostic value of the candidate biomarkers. The relationship o …

Classification of Acoustic Hearing Preservation After Cochlear Implantation Using Electrocochleography.

The objective to preserve residual hearing during cochlear implantation has recently led to the use of intracochlear electrocochleography (E … intracochlear ECochG recordings relating to hearing preservation or hearing loss, and (3) to design support vector machine (SVM) and random forest (RF) classifiers of acoustic hearing preservation that treat each subject as a sample and use all intracochlear ECochG recordings made during electrode array insertion for classification. Forty subjects undergoing cochlear im …

Derivation, validation, and transcriptomic assessment of pediatric septic shock phenotypes identified through latent profile analyses: Results from a prospective multi-center observational cohort.

BACKGROUND: Sepsis poses a grave threat, especially among children, but treatments are limited due to clinical and biological heterogeneity among patients. Thus, there is an urgent need for precise subclassification of patients to guide therapeutic interventions. METHODS: We used clinical, laboratory, and biomarker data from a prospective multi-center pediatric septic shock cohort to derive phenotypes using latent profile analyses. Thereafter, we trained a support vector machine model to assign phenotypes in a hold-out validation set. We tested interactions between phenotypes and common sepsi…

Deep Learning Model Based on Dual-Modal Ultrasound and Molecular Data for Predicting Response to Neoadjuvant Chemotherapy in Breast Cancer.

RATIONALE AND OBJECTIVES: To carry out radiomics analysis/deep convolutional neural network (CNN) based on B-mode ultrasound (BUS) and shear wave elastography (SWE) to predict response to neoadjuvant chemotherapy (NAC) in breast cancer patients. MATERIALS AND METHODS: In this prospective study, 255 breast cancer patients who received NAC between September 2016 and December 2021 were included. Radiomics models were designed using a support vector machine classifier based on US images obtained before treatment, including BUS and SWE. And CNN models also were developed using ResNet architecture.…

Identification of Inflammatory Gene in the Congenital Heart Surgery Patients following Cardiopulmonary Bypass via the Way of WGCNA and Machine Learning Algorithms.

Performing cardiopulmonary bypass (CPB) to reduce ischemic injury during surgery is a common approach to cardiac surgery. However, this proc … DLIM5) were further confirmed after the least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) screening and were used as hub genes for CPB-induced inflammatory cytokine release in patients with congenital heart defects. The enrichment analysis revealed 21 key module genes mainly related to the function …

Sarcopenia classification model for musculoskeletal patients using smart insole and artificial intelligence gait analysis.

BACKGROUND: The relationship between physical function, musculoskeletal disorders and sarcopenia is intricate. Current physical function tests, such as the gait speed test and the chair stand test, have limitations in eliminating subjective influences. To overcome this, smart devices utilizing inertial measurement unit sensors and artificial intelligence (AI)-based methods are being developed. METHODS: We employed cutting-edge technologies, including the smart insole device and pose estimation based on AI, along with three classification models: random forest (RF), support vector machine and …

Development and validation of a machine learning-based nomogram for predicting HLA-B27 expression.

BACKGROUND: HLA-B27 positivity is normal in patients undergoing rheumatic diseases. The diagnosis of many diseases requires an HLA-B27 exami … -B27 positivity whereas 236 with HLA-B27 negativity. In this study, 3 ML approaches, namely, LASSO, support vector machine (SVM) recursive feature elimination and random forest, were adopted for screening feature variables. Subsequently, to acquire the prediction model, the intersection was selected. Finally, differences among 148 cases with HLA-B27 positivity and negat …

Risk prediction for dermatomyositis-associated hepatocellular carcinoma.

OBJECTIVE: To explore dermatomyositis signature genes as potential biomarkers of hepatocellular carcinoma and their associated molecular regulatory mechanisms. METHODS: Based on the mRNA-Seq data of dermatomyositis and hepatocellular carcinoma in public databases, five dermatomyositis signature genes were screened by LASSO regression analysis and support vector machine (SVM) algorithm, and their biological functions in dermatomyositis with hepatocellular carcinoma were investigated, and a nomogram risk prediction model for hepatocellular carcinoma was constructed and its predictive efficiency…

Proteomic analysis to identification of hypoxia related markers in spinal tuberculosis: a study based on weighted gene co-expression network analysis and machine learning.

OBJECTIVE: This article aims at exploring the role of hypoxia-related genes and immune cells in spinal tuberculosis and tuberculosis involving other organs. METHODS: In this study, label-free quantitative proteomics analysis was performed on the intervertebral discs (fibrous cartilaginous tissues) obtained from five spinal tuberculosis (TB) patients. Key proteins associated with hypoxia were identified using molecular complex detection (MCODE), weighted gene co-expression network analysis(WGCNA), least absolute shrinkage and selection operator (LASSO), and support vector machine recursive fea…

Оставил Аксилу Аденокарциному неизвестного происхождения.

ЦЕЛЬ: Цель настоящего исследования заключалась в разработке метода классификации оптово-согласованных томографических (OCT) данных сетчатки в контексте автоматической диагностики множественных склерозов (MS) на ранней стадии с разъяснением решений. МЕТОДЫ: Использованная база данных содержит записи из 79 у пациентов с недавно диагностировавшимся повторным склерозом (RRMS) и отсутствием оптического неврата и 69 Здоровые подопытные по возрасту. Анализ проводился на толщине (среднее значение для правого и левого глаз и разница между глазами) слона макулярной сетчатки (mRNFL), слоя ячеек (mGCL), с

Совпадение в аннотации оригинала
PURPOSE: The purpose of this study was to provide the development of a method to classify optical coherence tomography (OCT)-assessed retina … ions (SHAP) are combined to rank relevant features and select the subset that maximizes classifier (support vector machine [SVM]) performance. RESULTS: Of the initial 48 features, 20 were identified as maximizing classifier accuracy (n = 0.9257). The SHAP values indicate that average thickness has greater relevance than inter-eye difference, that the mGCL and mRNFL are …

Treatment planning computed tomography radiomics for predicting treatment outcomes and haematological toxicities in locally advanced cervical cancer treated with radiotherapy: A retrospective cohort study.

OBJECTIVE: We evaluated whether radiomic features extracted from planning computed tomography (CT) scans predict clinical end points in pati … each of the gross target volume, pelvis and sacral vertebrae. The sequentially backward elimination support vector machine algorithm was used for feature selection and end point prediction. MAIN OUTCOMES AND MEASURES: Clinical end points include tumour complete response (CR), 5-year overall survival (OS), anaemia, and leucopenia. RESULTS: A combination of ten clinicop …

Spectral decomposition of EEG microstates in post-traumatic stress disorder.

Microstates offer a promising framework to study fast-scale brain dynamics in the resting-state electroencephalogram (EEG). However, microst … 5, pFWE = 0.02) repeating these group-level trends, only with larger effect sizes. Interestingly, a support vector machine classification analysis comparing broadband and frequency-specific measures revealed that models containing only alpha band features significantly out-perform broadband models. When classifying PTSD, the classification accuracy was 76 % and 65 % for …

Radiomics approach based on biphasic CT images well differentiate "early stage" of adrenal metastases from lipid-poor adenomas: A STARD compliant article.

The aim of the study was to develop an optimal radiomics model based on abdominal contrast-enhanced computed tomography (CECT) for pre-opera … radiomics, combined radiomics, and clinical-radiological-radiomics models were established using a support vector machine (SVM). The DeLong test was used to compare the areas under the receiver operating characteristic curves (AUCs) of all models. The AUCs of the unenhanced (0.913), arterial (0.845), portal (0.803), and venous (0.905) radiomics models were all higher t …

Identification of potential blood biomarkers for early diagnosis of schizophrenia through RNA sequencing analysis.

Schizophrenia (SCZ) is a highly heritable, polygenic complex mental disorder with imprecise diagnostic boundaries. Finding sensitive and spe … iomarkers, we constructed the risk prediction model with three classifiers (RF: Random Forest; SVM: support vector machine; DT: decision tree) through repeated k-fold cross validation ensuring better generalizability. Finest result of Area under Receiver Operating Characteristic (AUROC) score of 0.91 was achieved by RF classifier and with a comparable good performance o …

Identification of sperm proteins as biomarkers of field fertility in Holstein-Friesian bulls used for artificial insemination.

Despite passing stringent quality control, bulls used in artificial insemination can vary significantly in their fertility, emphasizing the … eins and 34 biomarker proteins. The predictive ability of the 34 biomarkers was evaluated employing support vector machine as the classifier, using their abundance levels in the Irish bulls to train the model and in the Danish bulls for validation. The prediction accuracy was 94.4%, with only one HF bull misclassified, corresponding to the lowest fertility index bull in …

Development of quantitative structure-retention relationship models to improve the identification of leachables in food packaging using non-targeted analysis.

Quantitative structure-retention relationship (QSRR) models can be used to predict the chromatographic retention time of chemicals and facil … S). First, different data partitioning ratios and feature selection methods [random forest (RF) and support vector machine (SVM)] were tested to build models to predict chromatographic retention times based on 2D molecular descriptors. The internal and external performances of the non-linear (RF) and corresponding linear predictive models were systematically compared, a …

Large-scale brain functional network abnormalities in social anxiety disorder.

BACKGROUND: Although aberrant brain regional responses are reported in social anxiety disorder (SAD), little is known about resting-state fu … alities with clinical characteristics, and assessed their ability to discriminate SAD from HC using support vector machine analyses. RESULTS: SAD patients showed widespread intra-network FNC abnormalities in the default mode network, the subcortical network and the perceptual system (i.e. sensorimotor, auditory and visual networks), and large-scale inter-network FNC ab …

Prediction of COVID-19 patients in danger of death using radiomic features of portable chest radiographs.

INTRODUCTION: Computer-aided diagnostic systems have been developed for the detection and differential diagnosis of coronavirus disease 2019 … hnique. We distinguished death from recovery cases using a linear discriminant analysis (LDA) and a support vector machine (SVM). The leave-one-out method was used to train and test the classifiers, and the area under the receiver-operating characteristic curve (AUC) was used to evaluate discriminative performance. RESULTS: The AUCs for LDA and SVM were 0.756 and 0.959 …

Automated analysis of small intestinal lamina propria to distinguish normal, Celiac Disease, and Non-Celiac Duodenitis biopsy images.

BACKGROUND AND OBJECTIVE: Celiac Disease (CD) is characterized by gluten intolerance in genetically predisposed individuals. High disease pr … tandardize and enhance the acquired images. RESULTS: For the normal controls versus CD use case, a Support Vector Machine (SVM) achieved an Accuracy (ACC) of 98.53%. For a second use case, we investigated the ability of the classification algorithm to differentiate between normal controls and NCD. In this use case, the SVM algorithm with linear kernel outperformed all …

Professional chess expertise modulates whole brain functional connectivity pattern homogeneity and couplings.

Previous studies have revealed changed functional connectivity patterns between brain areas in chess players using resting-state functional … players showing decreased functional connections between V1 and precentral gyrus. Besides, a linear support vector machine (SVM) based classification achieved an accuracy of 85.45%, a sensitivity of 85.71% and a specificity of 85.19% to differentiate chess players from novices by leave-one-out cross-validation. Finally, correlation analyses revealed that the mean FcHo v …