Predicting PbS Colloidal Quantum Dot Solar Cell Parameters Using Neural Networks Trained on Experimental Data by Hoon Jeong Lee, Arlene Chiu, Yida Lin, Sreyas Chintapalli, Serene Kamal, Eric Ji, Susanna M. Thon

“…The measurement system considers the spatial distribution of the materials parameters to gather and predict large amounts of data by treating an inhomogeneous device as a series of thousands of micro‐devices, a novel feature compared to existing solutions. This model can be extended to other materials and devices, accelerating development times for new optoelectronic technologies.…”
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Predicting the Pathway Involvement of Compounds Annotated in the Reactome Knowledgebase by Erik D. Huckvale, Hunter N. B. Moseley

“…<b>Results</b>: While models trained on a prior corresponding KEGG dataset with 502 pathways scored a mean Matthew’s correlation coefficient (MCC) of 0.847 and a 0.0098 standard deviation, the models trained on the Reactome dataset with 3985 pathways demonstrated improved performance with a mean MCC of 0.916, but with a higher standard deviation of 0.0149. …”
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Breaking barriers in ICD classification with a robust graph neural network for hierarchical coding by Suyang Xi, Jiesen Shi, Jiachen Yan, MingJing Lin, Xinyi Zhou, Yuan Cheng, Hong Ding, Chia Chao Kang

“…Extensive evaluations on benchmark datasets indicate that LGG-NRGrasp markedly surpasses leading models, exhibiting enhanced performance and dependability in automated ICD coding.…”
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Association of food environment with diet quality and Body Mass Index (BMI) of school-going adolescents in Nepal. by Pragya Sharma, Neha Limaye, Rajeeb Kumar Sah, Archana Shrestha

“…We used multiple regression models to analyze data, all statistical analyses were performed using STATA-14.…”
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Analysing the effectiveness of unsignalized crossing infrastructure in improving pedestrian safety using multiple data-driven approaches by Shengqi Liu, Harry Evdorides

“…The research highlights the superior predictive performance of Random Forest and MLP models, with accuracies of 84 % and 86 %, respectively, underscoring their capability to handle complex, nonlinear relationships in crash data. …”
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VenusMutHub: A systematic evaluation of protein mutation effect predictors on small-scale experimental data by Liang Zhang, Hua Pang, Chenghao Zhang, Song Li, Yang Tan, Fan Jiang, Mingchen Li, Yuanxi Yu, Ziyi Zhou, Banghao Wu, Bingxin Zhou, Hao Liu, Pan Tan, Liang Hong

“…Here, we present VenusMutHub, a comprehensive benchmark study using 905 small-scale experimental datasets curated from published literature and public databases, spanning 527 proteins across diverse functional properties including stability, activity, binding affinity, and selectivity. These datasets feature direct biochemical measurements rather than surrogate readouts, providing a more rigorous assessment of model performance in predicting mutations that affect specific molecular functions. …”
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LungDxNet: AI-Powered Low-Dose CT Analysis for Early Lung Cancer Detection by Jyoti Parashar, Rituraj Jain, Mahesh K. Singh, Ashwani Kumar, Premananda Sahu, Kamal Upreti

“…Very rigorous evaluations were performed on the model against both conventional machine learning and state of the art deep learning architectures. …”
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Broad coverage of neutralization-resistant SIV strains by second-generation SIV-specific antibodies targeting the region involved in binding CD4. by Hugh C Welles, Hannah A D King, Leonard Nettey, Nicole Cavett, Jason Gorman, Tongqing Zhou, Yaroslav Tsybovsky, Renguang Du, Kaimei Song, Richard Nguyen, David Ambrozak, Amy Ransier, Chaim A Schramm, Nicole A Doria-Rose, Adrienne E Swanstrom, James A Hoxie, Celia LaBranche, David C Montefiori, Daniel C Douek, Peter D Kwong, John R Mascola, Mario Roederer, Rosemarie D Mason

“…However, owing to a lack of rhesus-derived SIV broadly neutralizing antibodies (bnAbs), testing of bnAbs for HIV-1 prevention or treatment has thus far been performed exclusively in the SHIV NHP model using bnAbs from HIV-1-infected individuals. …”
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Transport and Mixing in Planetary Polar Vortices with Annular and Monopolar Potential Vorticity Structures by Jacob Shultis, William Seviour, Darryn Waugh, Anthony Toigo

“…The reduced stability of an annular vortex compared to a monopolar vortex may lead to increased horizontal transport between the midlatitudes and the poles, but this has yet to be thoroughly investigated. Here we perform such an investigation by quantifying the horizontal mixing in polar vortices of varying PV structure, for both a two-dimensional shallow-water model and a three-dimensional general circulation model (GCM). …”
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APBIO: bioactive profiling of air pollutants through inferred bioactivity signatures and prediction of novel target interactions by Eva Viesi, Ugo Perricone, Patrick Aloy, Rosalba Giugno

“…Moreover, the interactivity between biological entities can be represented through combined feature vectors that can be given as input to a machine learning (ML) model to capture the underlying interaction. …”
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A Multi-Step Topological Optimization Approach for Spacer Shape Design in Double-Sided SiC MOSFET Power Modules Considering Thermo-Mechanical Effects by Yuhang Guo, Ke Chen, Wentao Jiang, Longnv Li, Gaojia Zhu

“…Subsequently, to identify the appropriate optimization target, different objectives are employed in the TOs of a 2D simplified model. Following this, TO with the selected target function is performed on 3D simplified models featuring diverse spacer combination architectures, with the preferable one chosen based on the outcomes. …”
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Quantification and clustering of immune states in hepatitis B Cirrhosis by Wei Hou, Tengxiao Liang, Fangliang Xing, Zhongjie Hu

“…Sort visualization analysis and three-dimensional numerical fitting were performed to establish a mathematical model describing the relationships among lymphocyte subsets. …”
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Does the FARNet neural network algorithm accurately identify Posteroanterior cephalometric landmarks? by Merve Gonca, İbrahim Şevki Bayrakdar, Özer Çelik

“…Abstract Background We explored whether the feature aggregation and refinement network (FARNet) algorithm accurately identified posteroanterior (PA) cephalometric landmarks. …”
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Plastic to apparel: an analysis of sustainable purchasing intention using a machine learning ensemble by Carmella Andrea L. Cabrera, Ardvin Kester S. Ong, John Francis T. Diaz, Maela Madel L. Cahigas, Ma. Janice J. Gumasing

“…Data preprocessing using feature selection and correlation analysis was conducted to validate the available data, performed data cleaning process, and data aggregation. …”
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Multimodal Fusion Multi-Task Learning Network Based on Federated Averaging for SDB Severity Diagnosis by Songlu Lin, Renzheng Tang, Yuzhe Wang, Zhihong Wang

“…However, real-world polysomnography (PSG) data often suffer from modality heterogeneity, label scarcity, and non-independent and identically distributed (non-IID) characteristics across institutions, posing significant challenges for model generalization and clinical deployment. To address these issues, we propose a federated multi-task learning (FMTL) framework that simultaneously performs sleep staging and SDB severity classification from seven multimodal physiological signals, including EEG, ECG, respiration, etc. …”
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UPN-GCN: Update Positive-Negative Graph Convolution Neural Network in Non-Euclidean Structured Data by Lijun Fan, Shichao Yi, Wenrui Guan, Pingxin Wang

“…When using the adjacency matrix obtained from positive and negative relationships to perform GCN operations on data, the feature representations obtained from each iteration are continuously updated to optimize the adjacency matrix of positive and negative relationships, making the representation of data differences and similarities more accurate. …”
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Predicting dementia from primary care records: A systematic review and meta-analysis. by Elizabeth Ford, Nicholas Greenslade, Priya Paudyal, Stephen Bremner, Helen E Smith, Sube Banerjee, Shanu Sadhwani, Philip Rooney, Seb Oliver, Jackie Cassell

“…Variables in the primary care record may indicate risk of developing dementia, and could be combined in a predictive model to help find patients who are missing a diagnosis. …”
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Intercellular Communication Network of CellChat Uncovers Mechanisms of Kidney Fibrosis Based on Single-Cell RNA Sequencing by Lei Lei, Yun-Xiu Xiang, Mao-Lin Luo, Ze-Yu Zhang, Hong-Wei Wu, Chun Tang, Tian-Jiao Cui, Xue-Mei Zhang, Xiao-Hua Wang, Denis Delic, Thomas Klein, Yvonne Liu, Bernhard K. Krämer, Zhi-Hua Zheng, Yong-Ping Lu, Berthold Hocher, Ting Zhu

“…This study aimed to investigate the effects of Empa on renal fibrosis in a nondiabetic CKD model and to elucidate the underlying mechanisms. …”
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Key risk factors of generalized anxiety disorder in adolescents: machine learning study by Yonghwan Moon, Hyekyung Woo, Hyekyung Woo

“…Using machine learning techniques such as Lasso Regression, SelectKBest, and XGBoost, we identified key variables, including health behaviors such as sleep, smoking, and fast-food intake, as significant factors associated with GAD. Predictive models using Random Forest and Artificial Neural Networks demonstrated that the XGBoost feature selection method effectively identified key factors and showed strong performance. …”
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Weighted Filter Bank and Regularization Common Spatial Pattern-Based Decoding Algorithm for Brain-Computer Interfaces by Jincai Ye, Jiajie Zhu, Shoulin Huang

“…In the field of brain–computer interfaces (BCI), the decoding of motor imagery EEG signals is significantly hindered by individual differences in EEG signals, which limits the generalization ability of decoding models. To address this challenge, this study proposes a mutual information weighted filter bank regularized common spatial pattern (WFBRCSP) algorithm. …”
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