Network pharmacology and AI in cancer research uncovering biomarkers and therapeutic targets for RALGDS mutations by S. Mohammed Zaidh, Hariharan Thirumalai Vengateswaran, Mohammad Habeeb, Kiran Balasaheb Aher, Girija Balasaheb Bhavar, N. Irfan, K N V Chenchu Lakshmi

“…To address this issue, we selected a target mutated Kirsten rat sarcoma virus oncogene homologue, which plays a significant role in various cancers. Our study aims to identify selective biomarkers and develop diagnostic and therapeutic strategies for KRAS-associated genes using artificial intelligence. …”
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Integrated transcriptome analysis and combinatorial machine learning to construct a homeostatic model of acetylation for ccRCC and validate the key gene GCNT4 by Baohua Zhu, Ziyang Mo, Yi Bao, Xinxin Gan, Linhui Wang

“…Protein acetylation plays a key role in regulating cellular processes and cancer signaling pathways. …”
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From tissue-specific to tissue-agnostic: HER2 overexpression and the rise of antibody-drug conjugates by Heidi C. Ko, Kyle C. Strickland, Kyle C. Strickland, Dana Jaggessarsingh, Alicia Dillard, Michelle Green, Erin Newburn, Tiffany Sellaro, Jonathan L. Klein, Brian Caveney, Marcia Eisenberg, Eric A. Severson, Shakti Ramkissoon, Shakti Ramkissoon, Rebecca A. Previs, Rebecca A. Previs, Anis Toumeh

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Leveraging AI to explore structural contexts of post-translational modifications in drug binding by Kirill E. Medvedev, R. Dustin Schaeffer, Nick V. Grishin

“…Abstract Post-translational modifications (PTMs) play a crucial role in allowing cells to expand the functionality of their proteins and adaptively regulate their signaling pathways. …”
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Quantum Technologies for Beyond 5G and 6G Networks: Applications, Opportunities, and Challenges by Engin Zeydan, Chamitha De Alwis, Rabia Khan, Yekta Turk, Abdullah Aydeger, Thippa Reddy Gadekallu, Madhusanka Liyanage

“…A particular focus is on the role of quantum technologies in enhancing 6G Radio Access Networks (RAN), 6G core and edge network optimization, and 6G security. …”
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A novel bone metastasis-related gene signature for predicting prognosis, anti-androgen resistance, and drug choice in prostate cancer by Yu Luo, Xiaoqi Deng, Chengcheng Wei, Zhangcheng Liu, Liangdong Song, Kun Han, Yunfan Li, Jindong Zhang, Shuai Su, Delin Wang

“…The immune landscape heterogeneity of subgroups was analyzed using CIBERSORT, ESTIMATE, and xCell algorithms. Drug sensitivity and molecular docking analysis were performed to identify drugs associated with BMRPS. …”
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The molecular characteristics of DNA damage and repair related to P53 mutation for predicting the recurrence and immunotherapy response in hepatocellular carcinoma by Jiayao Ma, Diya Tang, Guangzu Cui, Xiangyang Zhang, Xinwen Wang, Yin Li, Erya Hu, Xin Zhou, Haicong Liu, Qingping Peng, Changjing Cai, Xiangying Deng, Shan Zeng, Yihong Chen, Zemin Xiao

“…Abstract Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths globally, owing to its high recurrence rate of 50 to 70% within five years. Despite known associations of certain DNA damage and repair (DDR) genes with tumor recurrence and drug resistance, a comprehensive understanding of DDR pathways’ role in predicting HCC recurrence and therapeutic responses remains elusive. …”
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Diverse regulated cell death patterns and immune traits in kidney allograft with fibrosis: a prediction of renal allograft failure based on machine learning, single-nucleus RNA seq... by Yuqing Li, Jiandong Zhang, Xuemeng Qiu, Yifei Zhang, Jiyue Wu, Qing Bi, Zejia Sun, Wei Wang

“…Subsequently, weighted gene co-expression network analysis and seven machine learning algorithms were employed to identify RCD-related hub genes for renal fibrosis. …”
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Artificial intelligence in project management: A bibliometric analysis by Nurkhat Ibadildin, Zhaxat Kenzhin, Gaukhar Yeshenkulova, Rymkul Ismailova, Assel Nurguzhina, Samalgul Nassanbekova, Aiman Kadyrova

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Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning by Tiantian He, Jie Geng, Chuandong Hou, Hongyi Li, Hong Zhang, Peng Zhao, Peifeng He, Xuechun Lu

“…Programmed cell death (PCD) plays a critical role in the development and progression of various cancers. …”
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A machine-learning approach for predicting butyrate production by microbial consortia using metabolic network information by Claudia Silva-Andrade, Sergio Hernández, Pedro Saa, Ernesto Perez-Rueda, Daniel Garrido, Alberto J. Martin

“…In the context of the human gut, butyrate is a central metabolite produced by bacteria that plays a key role within the gut microbiome impacting human health. …”
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Identifying potential co-expressed genes and molecular mechanisms linking post-COVID-19 and Guillain-Barre syndrome through neutrophil extracellular trap-related genes by Jie-Hua Su, Dan-Yu Lin, Xiao-Huan Liu, Jie-Li Zhang, Zhong-Gui Li, En-Xiang Tao, Kai-Xun Huang

“…Bioinformatics functional analysis indicated that co-expressed DEGs were predominantly gathered in immune-related and inflammatory response pathways. Employing various algorithms, we identified MMP9, CAMP, and CASP1 as NETRGs, demonstrating good discriminatory capacity in COVID-19 and GBS. …”
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Analysis of human brain RNA-seq data reveals combined effects of 4 types of RNA modifications and 18 types of programmed cell death on Alzheimer’s disease by Ke Ye, Xinyu Han, Mengjie Tian, Lulu Liu, Xu Gao, Qing Xia, Dayong Wang

“…Finally, by combining unsupervised consensus clustering, gene co-expression networks, and machine learning algorithms, an RNA modification-related programmed cell death network was constructed, and the pivotal roles of programmed cell death genes in key modules were identified. …”
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Pan-Cancer Analysis of Disulfidptosis-Related Genes Affecting Prognosis and Tumor Microenvironment by Jingyang SUN, Rongxuan JIANG, Liren HOU, Huanhuan DONG, Yihan LIN, Niuniu DONG, Guangjian ZHANG, Yanpeng ZHANG

“…ObjectiveTo assess the potential role of disulfidptosis-related genes (DRGs) in pan-cancer on prognosis and immunity on the basis of bioinformatics approaches. …”
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Trading off Iodine and Radiation Dose in Coronary Computed Tomography by Guillaume Fahrni, Thomas Saliba, Damien Racine, Marianna Gulizia, Georgios Tzimas, Chiara Pozzessere, David C. Rotzinger

“…Similarly, CM volume optimization, including adjustments in strategies for calculating CM volume, iodine concentration, and flow protocols, plays a role in managing risks such as contrast-associated acute kidney injury, particularly in patients with renal impairment. …”
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Multi-omics reveals the polyethylene terephthalate carcinogenicity: Cancer progression and immune microenvironment by Kangping Yang, Xianhuan Zhou, Kai Wu, Jiaqiang Wu, Chunxia Huang, Liang Yang

“…This multidisciplinary study systematically investigates PET's carcinogenic mechanisms by integrating network toxicology, machine learning, molecular docking simulations, single-cell RNA sequencing, and spatial transcriptomics. We identified PET-associated oncogenic targets and conducted pan-cancer expression profiling with prognostic analyses of 22 core genes, subsequently employing machine learning algorithms to quantify their prognostic weights across malignancies. …”
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A real-world disproportionality analysis of FDA adverse event reporting system (FAERS) events for lecanemab by Linlin Yan, Linhai Zhang, Zucai Xu, Zhong Luo

“…Using the Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-item Gamma Poisson Shrinker (MGPS) algorithms, we conducted a comprehensive analysis of lecanemab-related AEs, restricting the analysis to AEs with the role code of primary suspect (PS).ResultsA total of 811 AEs reports related to lecanemab used in AD patients and 506 AEs in Non-AD patients were included. …”
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Next Generation Power System Planning and Operation With Quantum Computation by Priyanka Arkalgud Ganeshamurthy, Kumar Ghosh, Corey O'Meara, Giorgio Cortiana, Jan Schiefelbein-Lach, Antonello Monti

“…In this article, we lay out clear motivations for seeking quantum solutions for solving computational burden challenges associated with power system applications. Next, we present the fundamental principles of quantum computing, introduce key quantum algorithms, and offer a comparison of the computational load between classical and quantum approaches for few important mathematical problems, indicating their relevance to various power system applications. …”
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Early prediction of progression-free survival of patients with locally advanced nasopharyngeal carcinoma using multi-parametric MRI radiomics by Lian Jian, Cai Sheng, Huaping Liu, Handong Li, Pingsheng Hu, Zhaodong Ai, Xiaoping Yu, Huai Liu

“…Pearson correlation analysis and recursive feature elimination or Relief were used for identifying features associated with progression-free survival (PFS). Five machine learning algorithms with cross-validation were compared to develop the optimal single-layer and fusion radiomic models. …”
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Resectograms: Planning liver surgery with real-time occlusion-free visualization of virtual resections by Ruoyan Meng, Davit Aghayan, Egidijus Pelanis, Bjørn Edwin, Faouzi Alaya Cheikh, Rafael Palomar

“…Background and Objective:: Visualization of virtual resections plays a central role in computer-assisted liver surgery planning. …”
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