Results 51 to 60 of about 139,430 (360)

RaMBat: Accurate identification of medulloblastoma subtypes from diverse data sources with severe batch effects

Molecular Oncology, EarlyView.
To integrate multiple transcriptomics data with severe batch effects for identifying MB subtypes, we developed a novel and accurate computational method named RaMBat, which leveraged subtype‐specific gene expression ranking information instead of absolute gene expression levels to address batch effects of diverse data sources.
Mengtao Sun, Jieqiong Wang, Shibiao Wan
wiley   +1 more source

Innovative Machine Learning Approach for Distinguishing Rheumatoid Arthritis and Osteoarthritis: Integrating Shapely Additive Explanations and Dendrograms

Journal of Scientific Innovation in Medicine
Background: Arthritis is a major healthcare issue and accurate diagnosis is important to treatment. Objective: The study aimed to identify and intuitively visualize feature importance of factors associated with osteoarthritis versus rheumatoid arthritis ...
Alexander A. Huang, Samuel Y. Huang
doaj   +1 more source

XGBoost-SHAP-based interpretable diagnostic framework for alzheimer’s disease

BMC Medical Informatics and Decision Making, 2023
Background Due to the class imbalance issue faced when Alzheimer’s disease (AD) develops from normal cognition (NC) to mild cognitive impairment (MCI), present clinical practice is met with challenges regarding the auxiliary diagnosis of AD using machine
Fuliang Yi, Hui Yang, Durong Chen, Yao Qin, Hongjuan Han, Jing Cui, Wenlin Bai, Yifei Ma, Rong Zhang, Hongmei Yu   +9 more
semanticscholar   +1 more source

Systemic T Cell Receptor Profiling Reveals Adaptive Immune Activation and Potential Immune Signatures of Diagnosis and Brain Atrophy in Epilepsy

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Epilepsy is increasingly associated with immune dysregulation and inflammation. The T cell receptor (TCR), a key mediator of adaptive immunity, shows repertoire alterations in various immune‐mediated diseases. The unique TCR sequence serves as a molecular barcode for T cells, and clonal expansion accompanied by reduced overall TCR ...
Yong‐Won Shin, Sang Bin Hong, Yong Woo Shin, Inpyeong Hwang, Jaeseong Oh, Jihyeon Choi, Narae Kim, Jangsup Moon, Keun‐Hwa Jung, Kyung‐Il Park, Ki‐Young Jung, Kon Chu, Sang Kun Lee   +12 more
wiley   +1 more source

A comparative analysis of Deep Neural Networks and Gradient Boosting Algorithms in long-term wind power forecasting [PDF]

Zbornik Radova: Elektrotehnički Institut "Nikola Tesla"
A vital step toward a sustainable future is the power grid's incorporation of renewable energy sources. Wind energy is significant because of its broad availability and minimal environmental impact.
Ivanović Luka, Milić Saša D., Sokolović Živko, Rakić Aleksandar   +3 more
doaj   +1 more source

Multi-Person Brain Activity Recognition via Comprehensive EEG Signal Analysis

, 2017
An electroencephalography (EEG) based brain activity recognition is a fundamental field of study for a number of significant applications such as intention prediction, appliance control, and neurological disease diagnosis in smart home and smart ...
Gu, Tao, Sheng, Quan Z., Wang, Xianzhi, Yao, Lina, Zhang, Dalin, Zhang, Xiang   +5 more
core   +1 more source

Unveiling diabetes onset: Optimized XGBoost with Bayesian optimization for enhanced prediction

PLoS ONE
Diabetes, a chronic condition affecting millions worldwide, necessitates early intervention to prevent severe complications. While accurately predicting diabetes onset or progression remains challenging due to complex and imbalanced datasets, recent ...
Muhammad Rizwan Khurshid, Sadaf Manzoor, Touseef Sadiq, Lal Hussain, Mohammed Shahbaz Khan, A. Dutta   +5 more
semanticscholar   +1 more source

Flux‐Regulated Crystallization of Perovskites Using Machine Learning‐Predicted Solvent Evaporation Rates for X‐Ray Detectors

Advanced Functional Materials, EarlyView.
By integrating machine learning into flux‐regulated crystallization (FRC), accurate prediction of solvent evaporation rates in real time, improving crystallization control and reducing crystal growth variability by over threefold, is achieved. This enhances the reproducibility and quality of perovskite single crystals, leading to reproducible ...
Tatiane Pretto, Sergey Dayneko, Antoine Pavesic Junior, Nicholas Sandor, Alexander Hart, Yuki Haruta, Magdalena Bazalova‐Carter, Marcos J. L. Santos, Makhsud I. Saidaminov   +8 more
wiley   +1 more source

Relative Humidity Prediction using XGBoost Machine Learning Model, Case Study: Bajgah Climatological Station, Iran [PDF]

مهندسی منابع آب
given the prevalence of available data for only these two parameters in many parts of the country, various scenarios involving these parameters were studied. The best scenario for predicting relative humidity was obtained using the XGBoost model.
Reza Piraei, Ali Mohammadi, Seied Hosein Afzali   +2 more
doaj   +1 more source

Comparison of Random Forest and XGBoost Classifiers Using Integrated Optical and SAR Features for Mapping Urban Impervious Surface

Remote Sensing
The integration of optical and SAR datasets through ensemble machine learning models shows promising results in urban remote sensing applications. The integration of multi-sensor datasets enhances the accuracy of information extraction.
Zhenfeng Shao, Muhammad Nasar Ahmad, Akib Javed   +2 more
semanticscholar   +1 more source