Multi-view mixture-of-experts for predicting molecular properties using SMILES, SELFIES, and graph-based representations by Eduardo Soares, Victor Yukio Shirasuna, Emilio Vital Brazil, Indra Priyadarsini, Seiji Takeda

“…Chemical foundation models are generally based on a two-step approach-pre-training on unlabeled data followed by fine-tuning on specific tasks-to boost model capacity. …”
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Comparative and Interpretative Analysis of CNN and Transformer Models in Predicting Wildfire Spread Using Remote Sensing Data by Yihang Zhou, Ruige Kong, Zhengsen Xu, Linlin Xu, Sibo Cheng

“…Through detailed quantitative comparison analysis, we discovered that Transformer‐based Swin‐UNet and UNet generally outperform Autoencoder and ResNet, particularly due to the advanced attention mechanisms in Transformer‐based Swin‐UNet and the efficient use of skip connections in both UNet and Transformer‐based Swin‐UNet, which contribute to superior predictive accuracy and model interpretability. …”
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355 Validation of an artificial intelligence Algorithm for predicting diagnosis-related groups in a community health system by Angela Muhanga, King Jason, Xsolis Mueller, Jennifer L. Sanger, Julie M. Bartlett, Brian N. Manz, James W. Spiten, Matthew J. Overgaard, Shauna M. Wang Hanyin

“…The accuracy of these predictions was then compared against the final billed DRG codes from the hospital’s records. …”
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Neurons throughout the brain embed robust signatures of their anatomical location into spike trains by Gemechu Bekele Tolossa, Aidan M Schneider, Eva Dyer, Keith B Hengen

“…Crucially, anatomical signatures generalize across animals and even across different research laboratories, suggesting a fundamental principle of neural organization. …”
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Bayesian Neural Networks With Robust Feature Interpretation for Enhanced Compressive Strength Prediction of Ultra‐High‐Performance Concrete by Tao Zhang, Ji Hao, Jie Zhang, Lina Du, Yunhao Zhang, Wenbin Jiao

“…The final model, integrated into a user interface, achieved prediction errors within ±3 MPa. The complete implementation and source code are openly available at https://github.com/sean‐jiao/UHPC‐prediction.…”
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Construction of a Novel Necroptosis-Related Signature in Rat DRG for Neuropathic Pain by Liu Y, Hao S, Hao H, Zheng G, Bing J, Kang L, Li J, Zhao H, Hao H

“…Significantly enriched KEGG and GO pathways, drug target prediction and non-coding RNAs related to the signature holded promise for advancing our understanding of potential avenues for treatment and the mechanisms underlying neuropathic pain. …”
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SERUM LncRNA SNHG16: A Biomarker for Diagnosing Childhood Obesity and Predicting Its Progression to Metabolic Syndrome by Hu J, Zheng Z, Liang D, Zhang Y, Chen J, Zhou C, You C, Liu Q

“…Junjie Hu,1,* Zufen Zheng,2,* Dakang Liang,1 Yongjin Zhang,2 Jiejing Chen,1 Cuiyi Zhou,1 Chuming You,3 Qiong Liu3 1Department of General Pediatrics, Sihui People’s Hospital, Sihui, 526200, People’s Republic of China; 2Department of Neonatal Pediatrics, Sihui People’s Hospital, Sihui, 526200, People’s Republic of China; 3Department of Neonatal Pediatrics, The Affiliated Guangdong Second Provincial General Hospital of Jinan University, Guangzhou, 510310, People’s Republic of China*These authors contributed equally to this workCorrespondence: Qiong Liu, Department of Neonatal Pediatrics, The Affiliated Guangdong Second Provincial General Hospital of Jinan University, Compound No. 466, Xingangzhong Road, Haizhu District, Guangzhou, 510310, People’s Republic of China, Tel +86 020-89168888, Email qiongliu154@163.com Chuming You, Department of Neonatal Pediatrics, The Affiliated Guangdong Second Provincial General Hospital of Jinan University, Compound No. 466, Xingangzhong Road, Haizhu District, Guangzhou, 510310, People’s Republic of China, Tel +86 020-89168888, Email youkuiqq7362@163.comPurpose: Obesity is a major risk factor for metabolic syndrome (MS) in children. …”
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Predictive Validity of Hospital-Associated Complications of Older People Identified Using Diagnosis Procedure Combination Data From an Acute Care Hospital in Japan: Observational S... by Seigo Mitsutake, Tatsuro Ishizaki, Shohei Yano, Takumi Hirata, Kae Ito, Ko Furuta, Yoshitomo Shimazaki, Hideki Ito, Alison Mudge, Kenji Toba

“…ResultsAmong 15,278 patients, 3610 (23.6%) patients had coding evidence of one or more HAC-OP-DPC (1: 18.8% and ≥2: 4.8%). …”
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Machine learning-based prediction of anxiety disorders using blood metabolite and social trait data from the UK Biobank by Annabel Smith, Jack J. Miller, Daniel C. Anthony, Daniel E. Radford-Smith

“…Overall, we demonstrate that the integration of biological and psychosocial risk factors is an effective tool to screen for and predict anxiety disorder onset in the general population.…”
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In-silico tool based on Boolean networks and meshless simulations for prediction of reaction and transport mechanisms in the systemic administration of chemotherapeutic drugs. by Fabián Mauricio Vélez Salazar, Iván David Patiño

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A Novel Similarity Score for Link Prediction Approach Using Financial Transaction Networks and Firms’ Attribute by Aparajita Bose, Byunghoon Kim

“…While several link prediction studies exist for general networks, limited research has specifically addressed the unique characteristics of financial transaction networks. …”
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Factors influencing student’s transition from student to workforce in intensive care units by Leonie Olivier, Charlene Downing

“…Qualitative data collected through purposive selection and semi-structured focus group discussions were transcribed, coded and analysed through domain analysis. Results: Four factors affecting nursing students’ perceived readiness for practice in the ICU were identified: (1) Support for new general nurses, (2) their need for professional socialisation and belonging, (3) orientation and skill development and (4) rotation and exposure to the ICU. …”
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MicroRNA-1912 regulates cholesterol homeostasis by targeting PCSK9 by Chan Joo Lee, Myeongjune Go, Sae-Bom Jeon, Sun-Ho Lee, Dongkook Min, So Hee Park, Seunghyun Lee, Sahng Wook Park

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iProtDNA-SMOTE: Enhancing protein-DNA binding sites prediction through imbalanced graph neural networks. by Ruiyan Huang, Wangren Qiu, Xuan Xiao, Weizhong Lin

“…These results indicate that iProtDNA-SMOTE outperforms existing methods in terms of accuracy and generalization for predicting DNA binding sites, offering reliable and effective predictions to minimize errors. …”
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The Development and Assessment of a Unique Disulfidptosis-Associated lncRNA Profile for Immune Microenvironment Prediction and Personalized Therapy in Gastric Adenocarcinoma by Jiyue Zhu, Xiang Zhu, Tingting Su, Huiqing Zhou, Shouhua Wang, Weibin Shi

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Physicians’ opinion of patients’ CPR decision: Secondary analysis from a pilot RCT by Stephanie Ji, Alexa Pisciotti, Mitsu Patel, Catherine Chen, Michael B. Steinberg, Karthik J. Kota

“…Background: While patients in the United States generally have final say in their code status, discussion with their physician plays an important role in decision-making. …”
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LightRoseTTA: High‐Efficient and Accurate Protein Structure Prediction Using a Light‐Weight Deep Graph Model by Xudong Wang, Tong Zhang, Guangbu Liu, Zhen Cui, Zhiyong Zeng, Cheng Long, Wenming Zheng, Jian Yang

“…Abstract Accurately predicting protein structure, from sequences to 3D structures, is of great significance in biological research. …”
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Electrocardiogram-Based Artificial Intelligence to Identify Coronary Artery Disease by Shinwan Kany, MD, MSc, Samuel F. Friedman, PhD, Mostafa Al-Alusi, MD, Shaan Khurshid, MD, MPH, Joel T. Rämö, MD, PhD, Daniel Pipilas, MD, James P. Pirruccello, MD, Christopher Reeder, PhD, Anthony A. Philippakis, MD, PhD, Jennifer E. Ho, MD, Mahnaz Maddah, PhD, Patrick T. Ellinor, MD, PhD, Akl C. Fahed, MD, MPH

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Context-Aware Gene Embedding Pipeline (CGEP): An Accessible, Regulatory-Region-Inclusive Embedding Framework for Interpretable Disease-Agnostic Prediction by Twaha Ahmed Minai, Zubair Ahmed Shaikh, Asim Imdad Wagan, M. Kamran Azim, Syed Muhammad Muaz, Muhammad Shoaib Siddiqui

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RMDNet: RNA-aware dung beetle optimization-based multi-branch integration network for RNA–protein binding sites prediction by Jiangbo Zhang, Yunhui Peng, Feifei Cui, Zilong Zhang, Shankai Yan, Qingchen Zhang

“…The source code is available https://github.com/cskyan/RMDNet .…”
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