Exploration of the Prognostic Markers of Multiple Myeloma Based on Cuproptosis‐Related Genes by Xiao‐Han Gao, Jun Yuan, Xiao‐Xia Zhang, Rui‐Cang Wang, Jie Yang, Yan Li, Jie Li

“…Additionally, key module genes were identified through weighted gene co‐expression network analysis. A univariate Cox algorithm and multivariate Cox analysis were employed to obtain biomarkers of MM and build a prognostic model before conducting independent prognostic analysis. …”
Get full text

Identification of specific metabolic pathways as druggable targets regulating the sensitivity to cyanide poisoning. by Patrick Y Sips, Xu Shi, Gabriel Musso, Anjali K Nath, Yanbin Zhao, Jason Nielson, Jordan Morningstar, Amy E Kelly, Brittney Mikell, Eva Buys, Vikhyat Bebarta, Jared Rutter, V Jo Davisson, Sari Mahon, Matthew Brenner, Gerry R Boss, Randall T Peterson, Robert E Gerszten, Calum A MacRae

Get full text

Role of arachidonic acid metabolism in osteosarcoma prognosis by integrating WGCNA and bioinformatics analysis by Yaling Wang, Peichun HSU, Haiyan Hu, Feng Lin, Xiaokang Wei

“…An AA metabolism predictive model of the five AAMRGs were established by Cox regression and the LASSO algorithm. …”
Get full text

The Value of Subclinical Carotid Atherosclerosis for Primary Prevention of Cardiovascular Diseases. Review of the Main International Studies by E. K. Butina, E. V. Bochkareva

“…Measures for the prevention of cardiovascular diseases (CVD) are more effective if they are performed taking into account the risk factors of their development. …”
Get full text

ATP6AP1 drives pyroptosis-mediated immune evasion in hepatocellular carcinoma: a machine learning-guided therapeutic target by Lei Tang, Xiyue Wang, Zhengzheng Xia, Jiayu Yan, Shanshan Lin

“…Results Through a rigorous multi-algorithm screening process, ATP6AP1 was found to be a highly reliable biomarker with an area under the curve (AUC) of 0.979. …”
Get full text

Digital mapping of peat thickness and extent in Finland using remote sensing and machine learning by Jonne Pohjankukka, Timo A. Räsänen, Timo P. Pitkänen, Arttu Kivimäki, Ville Mäkinen, Tapio Väänänen, Jouni Lerssi, Aura Salmivaara, Maarit Middleton

“…We carefully split the reference data into training and test sets, allowing for independent and robust model validation. Feature selection included an initial screening for multicollinearity using correlation-based feature pruning, followed by final selection using a genetic algorithm. …”
Get full text

Postpartum depression in Northeastern China: a cross-sectional study 6 weeks after giving birth by XuDong Huang, LiFeng Zhang, ChenYang Zhang, Jing Li, ChenYang Li

“…Feature importance was ranked via a random forest model based on the change in ROC-AUC after predictor removal. …”
Get full text

Drug–target interaction prediction by integrating heterogeneous information with mutual attention network by Yuanyuan Zhang, Yingdong Wang, Chaoyong Wu, Lingmin Zhan, Aoyi Wang, Caiping Cheng, Jinzhong Zhao, Wuxia Zhang, Jianxin Chen, Peng Li

“…DrugMAN uses a graph attention network-based integration algorithm to learn network-specific low-dimensional features for drugs and target proteins by integrating four drug networks and seven gene/protein networks collected by a certain screening conditions, respectively. …”
Get full text

Machine Learning for Predicting Zearalenone Contamination Levels in Pet Food by Zhenlong Wang, Wei An, Jiaxue Wang, Hui Tao, Xiumin Wang, Bing Han, Jinquan Wang

“…Other algorithms showed moderate accuracy, ranging from 77.1% to 84.8%. …”
Get full text

Interpretable machine learning for depression recognition with spatiotemporal gait features among older adults: a cross-sectional study in Xiamen, China by Shaowu Lin, Sicheng Li, Ya Fang

“…The developed machine learning models with high predictive accuracy, suggest the potential of Kinect-based gait assessment as a real-time and cost-effective screening tool for older adults with depressive symptoms.…”
Get full text

Detection of Undiagnosed Liver Cirrhosis via Artificial Intelligence-Enabled Electrocardiogram (DULCE): Rationale and design of a pragmatic cluster randomized clinical trial by Amy Olofson, Ryan Lennon, Blake Kassmeyer, Kan Liu, Zacchi I. Attia, David Rushlow, Puru Rattan, Joseph C. Ahn, Paul A. Friedman, Alina Allen, Patrick S. Kamath, Vijay H. Shah, Peter A. Noseworthy, Douglas A. Simonetto

“…A novel electrocardiogram (ECG)-enabled deep learning model trained for detection of advanced chronic liver disease (CLD) has demonstrated promising results and it may be used for screening of advanced CLD in primary care. …”
Get full text

IMPROVEMENT OF ADAPTATION TO STRUCTURES REMOVABLE DENTURES IN PATIENTS WITH ISCHEMIC HEART DISEASE by N.A. Ryabushko, V.N. Dvornik, I.V. Pavlish, G.N. Balya

“…The proposed health care was a complex algorithm to use: softener oral "Corsodyl" - 3-5 times a day after meals; clean teeth and dentures 2 times a day (morning and bedtime) toothpaste "Parodontax" and the bath solution Rinhra 3-5 times a day, with dryness in the mouth For the functional assessment made dentures were designed by us and proposed to use two methods of assessing patients to adapt designs removable dentures: • Objective evaluation - "Method of determining the degree of adaptation to the designs of removable dentures," Ukraine patent for utility model №101852 of 12.10.15; • Subjective evaluation - "Method of accelerated determination adaptation of patients to removable dentures designs using screening test" certificate of registration of copyright Ukraine №59280 15.04.2015. …”
Get full text

Identification of 17 novel epigenetic biomarkers associated with anxiety disorders using differential methylation analysis followed by machine learning-based validation by Yoonsung Kwon, Asta Blazyte, Yeonsu Jeon, Yeo Jin Kim, Kyungwhan An, Sungwon Jeon, Hyojung Ryu, Dong-Hyun Shin, Jihye Ahn, Hyojin Um, Younghui Kang, Hyebin Bak, Byoung-Chul Kim, Semin Lee, Hyung-Tae Jung, Eun-Seok Shin, Jong Bhak

“…Subsequent validation of identified biomarkers employed an artificial intelligence-based risk prediction models: a linear calculation-based methylation risk score model and two tree-based machine learning models: Random Forest and XGBoost. …”
Get full text

Identification of core therapeutic targets for Monkeypox virus and repurposing potential of drugs: A WEB prediction approach. by Huaichuan Duan, Quanshan Shi, Xinru Yue, Zelan Zhang, Ling Liu, Yueteng Wang, Yujie Cao, Zuoxin Ou, Li Liang, Jianping Hu, Hubing Shi

“…Here, we first summarized and improved the open reading frame information of monkeypox, constructed the monkeypox inhibitor library and potential targets library by database research as well as literature search, combined with advanced protein modeling technologies (Sequence-based and AI algorithms-based homology modeling). …”
Get full text

Exploring biomarkers and molecular mechanisms of Type 2 diabetes mellitus promotes colorectal cancer progression based on transcriptomics by Simin Luo, Yuhong Zhu, Zhanli Guo, Chuan Zheng, Xi Fu, Fengming You, Xueke Li

“…The diagnostic performance was assessed by supplementing external datasets to draw ROC curves on the diagnostic model. The diagnostic model was further screened for key genes by prognostic analysis. …”
Get full text

Autism in the Faroe Islands: Diagnostic Stability from Childhood to Early Adult Life by Eva Kočovská, Eva Billstedt, Asa Ellefsen, Hanna Kampmann, I. Carina Gillberg, Rannvá Biskupstø, Guðrið Andorsdóttir, Tormóður Stóra, Helen Minnis, Christopher Gillberg

“…Stability of the DISCO algorithm subcategory diagnoses was more variable but still good for AD. …”
Get full text

Fragmenstein: predicting protein–ligand structures of compounds derived from known crystallographic fragment hits using a strict conserved-binding–based methodology by Matteo P. Ferla, Rubén Sánchez-García, Rachael E. Skyner, Stefan Gahbauer, Jenny C. Taylor, Frank von Delft, Brian D. Marsden, Charlotte M. Deane

“…We show that an algorithmic approach (Fragmenstein) that ‘stitches’ the ligand atoms from this structural information together can provide more accurate and reliable predictions for protein–ligand complex conformation than general methods such as pharmacophore-constrained docking. …”
Get full text

Prediction of EGFR mutations in non-small cell lung cancer: a nomogram based on 18F-FDG PET and thin-section CT radiomics with machine learning by Jianbo Li, Qin Shi, Yi Yang, Jikui Xie, Qiang Xie, Ming Ni, Xuemei Wang, Xuemei Wang

“…After selecting optimal radiomic features, four machine learning algorithms, including logistic regression (LR), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost), were used to develop and validate radiomics models. …”
Get full text

The value of habitat analysis based on 18F-PSMA-1007 PET/CT images for prostate cancer risk grading by Yang Wang, Hongyue Zhao, Zhehao Lyu, Linhan Zhang, Wei Han, Zeyu Wang, Jiafu Wang, Xinyue Zhang, Shibo Guo, Peng Fu, Changjiu Zhao

“…Independent risk factors were screened and a combined model was constructed to predict GS grade by univariate logistic regression followed by multivariate logistic regression of habitat (1–4) and clinical factors (SUVmax, tPSA, fPSA/tPSA, age). …”
Get full text

The signature based on interleukin family and receptors identified IL19 and IL20RA in promoting nephroblastoma progression through STAT3 pathway by Chen Ding, Hongjie Gao, Liting Zhang, Zhiyi Lu, Bowen Zhang, Ding Li, Fengyin Sun

“…A prognostic model was constructed based on five selected IL(R)s using the LASSO Cox regression algorithm. …”
Get full text