Predicting noncoding RNA and disease associations using multigraph contrastive learning by Si-Lin Sun, Yue-Yi Jiang, Jun-Ping Yang, Yu-Han Xiu, Anas Bilal, Hai-Xia Long

“…Abstract MiRNAs and lncRNAs are two essential noncoding RNAs. Predicting associations between noncoding RNAs and diseases can significantly improve the accuracy of early diagnosis.With the continuous breakthroughs in artificial intelligence, researchers increasingly use deep learning methods to predict associations. …”
Get full text

Predicting patients’ sentiments about medications using artificial intelligence techniques by Amir Sorayaie Azar, Samin Babaei Rikan, Amin Naemi, Jamshid Bagherzadeh Mohasefi, Uffe Kock Wiil

“…Therefore, this study intends to develop Artificial Intelligence (AI) models to predict patients’ sentiments. This study used a large medication review dataset to perform a SA of medications. …”
Get full text

Lifestyle factors and colorectal cancer prediction: A nomogram-based model by Wooin Seo, Se Young Jung, Yeonhoon Jang, Kiheon Lee

“…Using the LASSO regression algorithm, we selected risk factors and fitted a Cox proportional hazards model to predict the 10-year CRC incidence. …”
Get full text

Neural network-based performance prediction of marine UHPC with coarse aggregates by Yunhao Luan, Dongbo Cai, Deming Wang, Changqing Luo, Anni Wang, Chao Wang, Degao Kong, Chaohui Xu, Sining Huang

“…In order to improve bearing capacity and service life of marine structure using marine UHPC with coarse aggregate (UHPC-CA), it is necessary to reasonably predict the performance of UHPC-CA. The performance of UHPC-CA was predicted in this paper based on five prediction models: multiple linear regression, multiple nonlinear regression, traditional neural network (T-BP), principal component approach neural network (PCA-BP), and improved neural network based on genetic algorithm (GA-BP). …”
Get full text

Predicting the risk of hematoma expansion in acute intracerebral hemorrhage: the GIVE score by Tian-Nan Yang, Xin-Ni Lv, Zi-Jie Wang, Xiao Hu, Li-Bo Zhao, Jing Cheng, Qi Li

“…Our objective was to develop and validate a score based on NCCT markers and clinical characteristics to predict risk of HE in acute intracerebral hemorrhage (ICH) patients. …”
Get full text

Predicting the presence of adjacent septic arthritis in children with acute hematogenous osteomyelitis by Shuting Lin, Donghao Gu, Peng Ning, Jingyu Wu, Zhixin Yang, Tianjing Liu

“…Four risk factors (age below 4 years, a history of preceding infection, platelet count > 390.5 × 10^9/L, and absolute neutrophil count < 5.45 × 10^3 cells/ml) were found to be predictive of concomitant infection and were included in the algorithm. …”
Get full text

Corticosteroid treatment prediction using chest X-ray and clinical data by Anzhelika Mezina, Samuel Genzor, Radim Burget, Vojtech Myska, Jan Mizera, Aleksandr Ometov

“…Conclusions: The introduced system for CS treatment prediction using our neural network and learning algorithm is unique in this field of research. …”
Get full text

Empirical Analysis of Honeybees Acoustics as Biosensors Signals for Swarm Prediction in Beehives by Kainat Iqbal, Bayan Alabdullah, Naif Al Mudawi, Asaad Algarni, Ahmad Jalal, Jeongmin Park

“…Monitoring of these beehives is therefore of paramount importance to keep an eye on their irregular behavior. Swarm prediction can be done by visually inspecting hives, monitoring temperature, or analyzing acoustic features with machine learning. …”
Get full text

Predicting breeding values in Murrah buffaloes using random regression models by ANAND JAIN, ASHISH RANJAN, I D GUPTA, H K SINGH

Get full text

Prediction of Human Papillomavirus-Host Oncoprotein Interactions Using Deep Learning by Sheila Santa, Samuel Kojo Kwofie, Kwasi Agyenkwa-Mawuli, Osbourne Quaye, Charles A Brown, Emmanuel A Tagoe

“…This study aimed to develop a deep learning model to predict interactions between HPV and host proteins. …”
Get full text

Construction of a prediction model for sarcopenic obesity based on machine learning by Mengru Xu, Mengru Xu, Jia Liu, Jia Liu, Song Hu, Song Hu, Tongxiao Luan, Tongxiao Luan, Yuting Duan, Yuting Duan, Aohua Wang, Aohua Wang, Ziwei Cui, Ziwei Cui, Jing Zhou, Yongjun Mao, Yongjun Mao

“…We identified four independent predictive factors, namely BMI, Barthel Index score, grip strength, and calf circumference. …”
Get full text

Establishment of Hyperspectral Prediction Model of Water Content in Anshan-Type Magnetite by Xiaoxiao XIE, Yang BAI, Jiuling ZHANG, Yuna JIA

“…Compared with previous studies, a more stable water content prediction model of Anshan magnetite was constructed by combining data preprocessing, CARS feature screening and nonlinear regression algorithm, which provides higher precision support for water content detection in mining production.…”
Get full text

A novel model for predicting immunotherapy response and prognosis in NSCLC patients by Ting Zang, Xiaorong Luo, Yangyu Mo, Jietao Lin, Weiguo Lu, Zhiling Li, Yingchun Zhou, Shulin Chen

“…Methods Patients were randomly divided into training cohort and validation cohort at a ratio of 2:1. The random forest algorithm was applied to select important variables based on routine blood tests, and a random forest (RF) model was constructed to predict the efficacy and prognosis of ICIs treatment. …”
Get full text

Construction of enhanced MRI-based radiomics models using machine learning algorithms for non-invasive prediction of IL7R expression in high-grade gliomas and its prognostic value in clinical practice by Jie Zhou

“…For selecting the most relevant features, we utilized the Minimum Redundancy Maximum Relevance (mRMR) and Recursive Feature Elimination (RFE) algorithms. Following this, we developed and assessed Support Vector Machine (SVM) and Logistic Regression (LR) models, measuring their performance through various metrics such as accuracy, specificity, sensitivity, positive predictive value, calibration curves, the Hosmer–Lemeshow goodness-of-fit test, decision curve analysis (DCA), and Kaplan–Meier survival analysis. …”
Get full text

Prediction of risk for acute kidney injury and its progression to mortality in obese patients admitted to ICU postoperatively by LI Qiang, LI Qiang, MU Guo, MU Guo, WANG Wenzhang

“…After data cleaning and preprocessing, Boruta feature selection was applied, followed by the construction of prediction models using 7 machine learning algorithms, that is, Gradient Boosting Machine (GBM), Generalized Linear Model (GLM), k-Nearest Neighbors (KNN), Naïve Bayes (NB), Neural Network (NNET), Support Vector Machine (SVM), and XGBoost. …”
Get full text

Long Short-Term Memory–Model Predictive Control Speed Prediction-Based Double Deep Q-Network Energy Management for Hybrid Electric Vehicle to Enhanced Fuel Economy by Haichao Liu, Hongliang Wang, Miao Yu, Yaolin Wang, Yang Luo

“…The initial learning rate and dropout probability of the LSTM speed prediction model are optimized using a Double Deep Q-Network (DDQN) algorithm. …”
Get full text

Intracranial stenosis prediction using a small set of risk factors in the Tromsø Study by Luca Bernecker, Liv-Hege Johnsen, Torgil Riise Vangberg

“…Furthermore, the results demonstrate the predictive potential of limited risk factors, highlighting its potential contribution to a multi-modular classification algorithm based on MRAs.…”
Get full text

Optimization of distribution networks using quantum annealing for loss reduction and voltage improvement in electrical vehicle parking management by Naser Rashnu, Babak Mozafari, Reza Sharifi

Subjects: Get full text

A Novel Long Short-Term Memory Seq2Seq Model with Chaos-Based Optimization and Attention Mechanism for Enhanced Dam Deformation Prediction by Lei Wang, Jiajun Wang, Dawei Tong, Xiaoling Wang

Subjects: “…dam deformation prediction…”
Get full text

Comparative analysis of machine learning techniques in metabolomic-based preterm birth prediction by Ying-Chieh Han, Jane Shearer, Chunlong Mu, Donna M. Slater, Suzanne C. Tough, Gavin E. Duggan

“…This study investigated several ML models' efficacy in predicting preterm birth using untargeted metabolomics from serum collected during the third trimester of gestation. …”
Get full text