WGCNA-ML-MR integration: uncovering immune-related genes in prostate cancer by Jing Lv, Jing Lv, Yuhua Zhou, Yuhua Zhou, Shengkai Jin, Shengkai Jin, Chaowei Fu, Chaowei Fu, Yang Shen, Yang Shen, Bo Liu, Bo Liu, Menglu Li, Yuwei Zhang, Yuwei Zhang, Ninghan Feng, Ninghan Feng, Ninghan Feng

“…Furthermore, a machine learning algorithm was used to screen for core genes and construct a diagnostic model, which was then validated in an external validation dataset. …”
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The Role of Three Plasma Proteins in the Diagnosis of Ovarian Tumors by Valeria Racheva, Adelaida Ruseva, Svetlana Mateva, Ivan Malkodanski

“…Ovarian cancer is not common, but it is still the fifth leading cause of death from malignant diseases among women worldwide. More than 200,000 women are diagnosed with ovarian cancer each year globally. …”
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Progress and challenges of artificial intelligence in lung cancer clinical translation by Erjia Zhu, Amgad Muneer, Jianjun Zhang, Yang Xia, Xiaomeng Li, Caicun Zhou, John V. Heymach, Jia Wu, Xiuning Le

“…Abstract Artificial intelligence (AI) algorithms, such as convolutional neural networks and transformers, have significantly impacted cancer care. …”
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AI-based classification of anticancer drugs reveals nucleolar condensation as a predictor of immunogenicity by Giulia Cerrato, Peng Liu, Liwei Zhao, Adriana Petrazzuolo, Juliette Humeau, Sophie Theresa Schmid, Mahmoud Abdellatif, Allan Sauvat, Guido Kroemer

“…Conclusions We developed AI-based algorithms for predicting CON-inducing drugs based on molecular descriptors and their validation using automated micrographs analysis, offering a new approach for screening ICD inducers with minimized adverse effects in cancer therapy.…”
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Data-Driven Battery Remaining Life Prediction Based on ResNet with GA Optimization by Jixiang Zhou, Weijian Huang, Haiyan Dai, Chuang Wang, Yuhua Zhong

“…To this end, this paper proposes a data-driven lithium-ion battery life prediction method based on residual network (ResNet) and genetic algorithm (GA) optimization, which is designed to screen the features of the lithium-ion battery training data in order to effectively reduce the redundant features and improve the prediction performance of the model. …”
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In silico assessment of biocompatibility and toxicity: molecular docking and dynamics simulation of PMMA-based dental materials for interim prosthetic restorations by Ravinder S. Saini, Rayan Ibrahim H. Binduhayyim, Vishwanath Gurumurthy, Abdulkhaliq Ali F. Alshadidi, Shashit Shetty Bavabeedu, Rajesh Vyas, Doni Dermawan, Punnoth Poonkuzhi Naseef, Seyed Ali Mosaddad, Artak Heboyan

“…The LigandScout was utilized for pharmacophore modeling, which employs a shape-based screening approach to identify potential ligand binding sites on protein targets. …”
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Noninvasive prediction of meningioma brain invasion via multiparametric MRI⁃based brain⁃tumor interface radiomics by CHENG Xing, WANG Zhi⁃chao, LI Hua⁃ning, WANG Xie⁃feng, YOU Yong⁃ping

“…Through five⁃fold cross⁃validation in the training set and evaluation in the testing set, comparative analysis of the predictive performance of 18 model⁃thickness combinations (6 ML algorithms × 3 BTI thicknesses) showed that the XGBoost model constructed with a 1.00 cm BTI thickness demonstrated exceptional performance. …”
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Identifying Safeguards Disabled by Epstein-Barr Virus Infections in Genomes From Patients With Breast Cancer: Chromosomal Bioinformatics Analysis by Bernard Friedenson

“…EBV-transformed human mammary cells accelerate breast cancer when transplanted into immunosuppressed mice, but the virus can disappear as malignant cells reproduce. If this model applies to human breast cancers, then they should have genome damage characteristic of EBV infection. …”
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Dielectric tensor prediction for inorganic materials using latent information from preferred potential by Zetian Mao, WenWen Li, Jethro Tan

“…We develop an equivariant readout decoder to predict total, electronic, and ionic dielectric tensors while preserving O(3) equivariance, and benchmark its performance against state-of-the-art algorithms. Virtual screening of thermodynamically stable materials from Materials Project for two discovery tasks, high-dielectric and highly anisotropic materials, identifies promising candidates including Cs2Ti(WO4)3 (band gap E g = 2.93eV, dielectric constant ε = 180.90) and CsZrCuSe3 (anisotropic ratio α r = 121.89). …”
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The use of patient-reported outcome measures to improve patient-related outcomes – a systematic review by Joshua M. Bonsel, Ademola J. Itiola, Anouk S. Huberts, Gouke J. Bonsel, Hannah Penton

“…Conclusions The use of PROMs at the individual level has matured considerably. Monitoring/screening applications seem promising particularly for diseases for which treatment algorithms rely on the experienced symptom burden by patients. …”
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Real-World Parkinson’s Hand Tremor Detection Using Ensemble Learning Techniques by Sungwook Hur, Jieming Zhang, Moon-Hyun Kim, Tai-Myoung Chung

“…Our method enables more accurate detection of subtle tremor patterns in real-world conditions compared to conventional methods. …”
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To accurately predict lymph node metastasis in patients with mass-forming intrahepatic cholangiocarcinoma by using CT radiomics features of tumor habitat subregions by Pengyu Chen, Zhenwei Yang, Peigang Ning, Hao Yuan, Zuochao Qi, Qingshan Li, Bo Meng, Xianzhou Zhang, Haibo Yu

“…Using information from the arterial and venous phases of multisequence CT images, tumor habitat subregions were delineated through the K-means clustering algorithm. Radiomic features were extracted and screened, and prediction models based on different subregions were constructed and compared with traditional intratumoral models. …”
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A machine learning framework for predicting cognitive impairment in aging populations using urinary metal and demographic data by Fengchun Ren, Xiao Zhao, Qin Yang, Huaqiang Liao, Yudong Zhang, Xuemei Liu

“…Ultimately, a user-friendly webserver was deployed for the predictive model and is freely accessed at http://bio-medical.online/admxp/.DiscussionThe associated webserver enables accessible risk screening and underpins precision prevention strategies in aging populations.…”
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Empowering Healthcare: TinyML for Precise Lung Disease Classification by Youssef Abadade, Nabil Benamar, Miloud Bagaa, Habiba Chaoui

“…These findings highlight the potential of TinyML to provide accessible, reliable, and real-time diagnostic tools, particularly in remote and underserved areas, demonstrating the transformative impact of integrating advanced AI algorithms into portable medical devices. This advancement facilitates the prospect of automated respiratory health screening using lung sounds.…”
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Investigating the Complexity of Multidimensional Symptom Experiences in Patients With Cancer: Systematic Review of the Network Analysis Approach by Vincent Richard, Allison Gilbert, Emanuela Pizzolla, Giovanni Briganti

“…Several NA approaches were used: network visualization (n=1), Bayesian network (n=1), pairwise Markov random field and IsingFit method (n=1), unregularized Gaussian graphical model (n=2), regularized partial correlation network (n=6), network visualization and community NA (n=1), network visualization and Walktrap algorithm (n=1), undirected network model with the Fruchterman-Reingold and edge-betweenness approaches (n=4), biased correlation and concise pattern diagram (n=1), extended Bayesian information criterion graphical LASSO method (n=3), cross-lagged panel network (n=1), and unspecified NA (n=3). …”
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Design and optimization of planetary gear train pendulum type sugarcane seeding mechanism based on spatial offset trajectory by Jiaodi Liu, Chaoyuan Luo, Qingli Chen, Jianhao Chen, Jianlong Chen, Yihao Xing

“…Based on the speed requirements of the sugarcane seeds at the critical motion points, a forward kinematics model of this seeding mechanism is established. A multi-objective genetic algorithm combined with the entropy-weight TOPSIS method is used to optimize and screen the installation dimensions of the components of the mechanism so as to keep the motion of the sugarcane seeds stable at the critical positions. …”
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Multidimensional Evaluation Framework and Classification Strategy for Low-Carbon Technologies in Office Buildings by Hongjiang Liu, Yuan Song, Yawei Du, Tao Feng, Zhihou Yang

“…The method includes four core components: (1) establishing three archetypal models—low-rise (H ≤ 24 m), mid-rise (24 m < H ≤ 50 m), and high-rise (50 m < H ≤ 100 m) office buildings—based on 99 office buildings in Beijing; (2) classifying 19 key technologies into three clusters—Envelope Structure Optimization, Equipment Efficiency Enhancement, and Renewable Energy Utilization—using bibliometric analysis and policy norm screening; (3) developing a four-dimensional evaluation framework encompassing Carbon Reduction Degree (CRD), Economic Viability Degree (EVD), Technical Applicability Degree (TAD), and Carbon Intensity Degree (CID); and (4) conducting a comprehensive quantitative evaluation using the AHP-entropy-TOPSIS algorithm. …”
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Community-based COVID-19 active case finding and rapid response in the Democratic Republic of the Congo: Improving case detection and response. by John Otokoye Otshudiema, Gervais Léon Tengomo Folefack, Justus M Nsio, Cathy H Kakema, Luigino Minikulu, Aimé Bafuana, Joel B Kosianza, Antoine K Mfumu, Edith Nkwembe, Yannick Munyeku-Bazitama, Sheila Makiala-Mandanda, Noé Guinko, Gisèle Mbuyi, Jean-Marie K Tshilumbu, Guy N Saidi, Moreau-Serge Umba-di-Masiala, Amos K Ebondo, Jean-Jacques Mutonj, Serge Kalombo, Jad Kabeya, Taty K Mawanda, Faustin N Bile, Gaby K Kasereka, Placide Mbala-Kingebeni, Steve Ahuka-Mundeke, Humphrey Cyprian Karamagi, Karl Njuwa Fai, Amédée Prosper Djiguimde

“…This pilot study was modeled on the DRC's National COVID-19 Response Plan and the COVID-19 Ag-RDT screening algorithm defined by the World Health Organization (WHO), with case findings implemented in 259 health areas, 39 health zones, and 9 provinces. …”
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Detection of Autism Spectrum Disorder Using A 1-Dimensional Convolutional Neural Network by Aythem Khairi Kareem, Mohammed M. AL-Ani, Ahmed Adil Nafea

“…Results strongly suggest that 1D CNNs have shown improved accuracy in the classification of ASD compared to traditional machine learning algorithms, on all these datasets with higher accuracy of 99.45%, 98.66%, and 90% for Autistic Spectrum Disorder Screening in Data for Adults, Children, and Adolescents respectively as they are better suited for the analysis of time series data commonly used in the diagnosis of this disorder …”
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An interpretable disruption predictor on EAST using improved XGBoost and SHAP by D.M. Liu, X.L. Zhu, Y.S. Jiang, S. Wang, S.B. Shu, B. Shen, B.H. Guo, L.C. Liu

“…Based on the physical characteristics of the disruption, 2094 disruption shots and 4858 non-disruption shots from 2022 to 2024 were screened as training shots, and then the disruption prediction model was trained using the eXtreme Gradient Boosting (XGBoost) algorithm from training samples consisting of 16 diagnostic signals, such as plasma current, density, and radiation. …”
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