Phylogenetic and functional assessment of orthologs inference projects and methods. by Adrian M Altenhoff, Christophe Dessimoz

“…We systematically compared their predictions with respect to both phylogeny and function, using six different tests. …”
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Chemoresistance factors in non-small cell lung cancer by А. I. Shevchenko, А. P. Коlesnik, А. V. Каdzhoian, V. А. Кuzmenko

“…Basing on current data we tried to analyze prognostic and predictive value of main chemoresistance factors in NSCLC. …”
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Visceral adiposity index as a predictor of metabolic dysfunction-associated steatotic liver disease: a cross-sectional study by Tuo Zhou, Xiang Ding, Linjie Chen, Qianxiong Huang, Linfang He

“…Weighted multivariable regression models, subgroup analyses, and machine learning algorithms were used to evaluate associations and predictive performance. …”
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Integrating Machine Learning, SHAP Interpretability, and Deep Learning Approaches in the Study of Environmental and Economic Factors: A Case Study of Residential Segregation in Las... by Jingyi Liu, Yuxuan Cai, Xiwei Shen

“…By integrating traditional econometric techniques with machine learning and deep learning models, the study investigates (1) the correlation between housing prices, environmental quality, and segregation; (2) the differentiated impacts on various ethnic groups; and (3) the comparative effectiveness of predictive models. Among the tested algorithms, LGBM (Light Gradient Boosting) delivered the highest predictive accuracy and robustness. …”
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Personalized medication recommendations for Parkinson’s disease patients using gated recurrent units and SHAP interpretability by Atiye Riasi, Mehdi Delrobaei, Mehri Salari

“…This study aims to demonstrate the potential of sequence-by-sequence algorithms in recommending personalized medication combinations for patients with PD based on their previous visits. …”
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Characterization of time-variant and time-invariant assessment of suicidality on Reddit using C-SSRS. by Manas Gaur, Vamsi Aribandi, Amanuel Alambo, Ugur Kursuncu, Krishnaprasad Thirunarayan, Jonathan Beich, Jyotishman Pathak, Amit Sheth

“…Suicide is the 10th leading cause of death in the U.S (1999-2019). However, predicting when someone will attempt suicide has been nearly impossible. …”
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An Immune-Related Prognostic Classifier Is Associated with Diffuse Large B Cell Lymphoma Microenvironment by Xiao-Jie Liang, Rui-ying Fu, He-nan Wang, Jing Yang, Na Yao, Xin-di Liu, Liang Wang

“…The multi-IRG classifier showed powerful predictive ability. Patients with a high-risk score had poor survival. …”
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Deep reinforcement learning agents for dynamic spectrum access in television whitespace cognitive radio networks by Udeme C. Ukpong, Olabode Idowu-Bismark, Emmanuel Adetiba, Jules R. Kala, Emmanuel Owolabi, Oluwadamilola Oshin, Abdultaofeek Abayomi, Oluwatobi E. Dare

“…Reinforcement Learning (RL) in CRs helps predict spectral changes and identify optimal transmission frequencies. …”
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STING is significantly increased in high-grade glioma with high risk of recurrence by Meishi Zhong, Manmei Long, Chenjie Han, Saiyan Ji, Qingyuan Yang

“…Integrated analysis showed that ATRX is positively correlated with STING in high-grade glioma (HGG) with wild-type ATRX and IDH1. Then, a relapse predictive risk-scoring model was established using the least absolute shrinkage and selection operator regression algorithms. …”
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Comparing the potential of tree-based and area-based forest height metrics for aboveground biomass estimation in complex forest landscapes by Weiyan Liu, Yuling Chen, Haitao Yang, Guangcai Xu, Shiyu Yan, Lingjun Li, Ling Chen, Qinghua Guo

“…Two modeling approaches—parametric mixed-effects models (MEM) and non-parametric machine learning (ML) algorithms—were applied to evaluate predictive accuracy. …”
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Structural insights into SOD1: from in silico and molecular dynamics to experimental analyses of ALS-associated E49K and R115G mutants by Seyed Mahdi Hosseini Faradonbeh, Bagher Seyedalipour, Nasrin Keivan Behjou, Kimiya Rezaei, Payam Baziyar, Saman Hosseinkhani

“…Our analysis was performed through multiple algorithms on the structural characterization of the hSOD1 protein using computational predictions, molecular dynamics (MD) simulations, and experimental studies to understand the effects of amino acid substitutions. …”
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Understanding students’ sentiment from feedback with a new feature selection and semantics networks by Tran Anh Tuan, Dao Thi Thanh Loan, Nichnan Kittiphattanabawon

“… Sentiment analysis of students’ feedback using machine learning algorithms has emerged as a valuable tool for understanding students’ sentiments and improving educational outcomes. …”
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In Silico Analysis of Coding/Noncoding SNPs of Human RETN Gene and Characterization of Their Impact on Resistin Stability and Structure by Lamiae Elkhattabi, Imane Morjane, Hicham Charoute, Soumaya Amghar, Hind Bouafi, Zouhair Elkarhat, Rachid Saile, Hassan Rouba, Abdelhamid Barakat

“…Stability analysis predicted 9 nsSNPs (I32S, C51Y, G58E, G58R, C78S, G79C, W98C, C103G, and C104Y) which can decrease protein stability with at least three out of the four algorithms used in this study. …”
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ggClusterNet 2: An R package for microbial co‐occurrence networks and associated indicator correlation patterns by Tao Wen, Yong‐Xin Liu, Lanlan Liu, Guoqing Niu, Zhexu Ding, Xinyang Teng, Jie Ma, Ying Liu, Shengdie Yang, Penghao Xie, Tianjiao Zhang, Lei Wang, Zhanyuan Lu, Qirong Shen, Jun Yuan

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Comparative review of intelligent structural safety in building seismic risk mitigation utilizing an integrated artificial intelligence controller by Normaisharah Mamat, Rawad Abdulghafor, Sherzod Turaev, Fitri Yakub

“…The research highlights the influence of integrated AI controllers on control systems, examining several AI controllers, including machine learning algorithms, neural networks, and evolutionary algorithms concerning structural safety. …”
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A manganese metabolism-related gene signature stratifies prognosis and immunotherapy efficacy in kidney cancer by Yang Liu, Hao Ye, Ruoxuan Zhang, Xiaolong Liu, Ranlu Liu

“…Through integrated bioinformatics approaches, including differential expression analysis, univariate Cox regression, and three machine learning algorithms (Boruta, GBM, and RFS), we identified prognosis-related MMCG. …”
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Modeling residue formation from crude oil oxidation using tree-based machine learning approaches by Mohammad-Reza Mohammadi, Seyyed-Mohammad-Mehdi Hosseini, Behnam Amiri-Ramsheh, Saptarshi Kar, Ali Abedi, Abdolhossein Hemmati-Sarapardeh, Ahmad Mohaddespour

“…Four advanced tree-based machine learning algorithms comprising gradient boosting with categorical features support (CatBoost), light gradient boosting machine (LightGBM), random forest (RF), and extreme gradient boosting (XGBoost) were utilized to develop accurate predictive models. …”
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Combined Allocation of Renewable-Based Distributed Generators and Shunt Capacitor Banks in Distribution Networks with Electric Vehicle Load Penetration by Abhilasha Pawar, Rajkumar Viral, Divya Asija, Mohit Bansal

“…Compared to other optimization algorithms, the APO shows superior performance. …”
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Transitional Grid Maps: Joint Modeling of Static and Dynamic Occupancy by Jose Manuel Gaspar Sanchez, Leonard Bruns, Jana Tumova, Patric Jensfelt, Martin Torngren

“…Autonomous agents rely on sensor data to construct representations of their environments, essential for predicting future events and planning their actions. …”
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Artificial intelligence in personalized cardiology treatment by Abbas Mohammadi, Sheida Shokohyar

“…This study examines the advancements and challenges in personalized cardiovascular medicine, highlighting the need to address issues such as data privacy, algorithmic bias, and accessibility to promote the equitable application of personalized medicine.…”
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