Integrating electronic health records and GWAS summary statistics to predict the progression of autoimmune diseases from preclinical stages by Chen Wang, Havell Markus, Avantika R. Diwadkar, Chachrit Khunsriraksakul, Laura Carrel, Bingshan Li, Xue Zhong, Xingyan Wang, Xiaowei Zhan, Galen T. Foulke, Nancy J. Olsen, Dajiang J. Liu, Bibo Jiang

“…Importantly, progression and case-control phenotypes may have shared genetic basis, which we can exploit to improve prediction accuracy. …”
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Machine vision-based detection of key traits in shiitake mushroom caps by Jiuxiao Zhao, Jiuxiao Zhao, Wengang Zheng, Wengang Zheng, Yibo Wei, Yibo Wei, Qian Zhao, Qian Zhao, Jing Dong, Jing Dong, Xin Zhang, Xin Zhang, Mingfei Wang, Mingfei Wang

“…It enables precise phenotyping through accurate image acquisition and analysis. …”
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Current Status and Applications of Genome‐Scale Metabolic Models of Oleaginous Microorganisms by Zijian Hu, Jinyi Qian, Yuzhou Wang, Chao Ye

“…Despite their potential for efficient lipid production, the metabolic pathways involved are not yet fully understood, largely due to the complexity of intracellular processes and the challenges in phenotypic prediction. This review synthesizes the latest research on the application of Genome‐scale Metabolic Network Models (GSMMs) to study oleaginous microorganisms, including bacteria, cyanobacteria, yeast, microalgae, and fungi, and provides a comprehensive analysis of how GSMMs have been utilized to decipher the metabolic mechanisms behind lipid accumulation and to identify key genes involved in lipid synthesis. …”
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Unbiased identification of cell identity in dense mixed neural cultures by Sarah De Beuckeleer, Tim Van De Looverbosch, Johanna Van Den Daele, Peter Ponsaerts, Winnok H De Vos

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ANO5 p.I616F variant drives florid cemento-osseous dysplasia by disrupting transmembrane domain structure by Lanlan Zhang, Wenxi Jin, Yunfa Qin, Yonghui Zhang, Yemei Qian, Jingyi Li, Yu Liu, Yan’an Shi, Rongju Xie, Weihong Wang

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A call to integrate menstrual cycle influences into just-in-time adaptive interventions for suicide prevention by Hafsah A. Tauseef, Daniel D. L. Coppersmith, Azure J. Reid-Russell, Anisha Nagpal, Jaclyn Ross, Matthew K. Nock, Tory Eisenlohr-Moul

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Food selectivity in Autism Spectrum Disorder: implications of eating, sensory and behavioural profile by Maria Pia Riccio, Maria Marino, Raffaele Garotti, Annalisa Tassiello, Valeria Maffettone, Mariangela Pezone, Carmela Bravaccio

“…These modalities were also found to predict greater food selectivity on regression analysis. …”
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Type 2 diabetes clusters in the Novosibirsk region by I. A. Bondar, O. Y. Shabelnikova

“…Determination of different T2DM phenotypes will improve the prediction of metabolic disorders, the risk of complications and individual diabetes therapy.AIM: To identify clusters of T2DM in patients with different duration of diabetes with a study of the frequency of diabetic complications and drug therapy in the Novosibirsk region.MATERIALS AND METHODS: The study was carried out at Diamodul in the period 2013–2017 in the Novosibirsk region. …”
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Characterization of Hazelnut Trees in Open Field Through High-Resolution UAV-Based Imagery and Vegetation Indices by Maurizio Morisio, Emanuela Noris, Chiara Pagliarani, Stefano Pavone, Amedeo Moine, José Doumet, Luca Ardito

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FABP4 suppresses proliferation and invasion of hepatocellular carcinoma cells and predicts a poor prognosis for hepatocellular carcinoma by Cheng‐Qian Zhong, Xiu‐Ping Zhang, Ning Ma, Er‐Bin Zhang, Jing‐Jing Li, Ya‐Bo Jiang, Yu‐Zhen Gao, Yan‐Mei Yuan, Shi‐Qian Lan, Dong Xie, Shu‐Qun Cheng

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Machine learning-assisted radiogenomic analysis for miR-15a expression prediction in renal cell carcinoma by Yulian Mytsyk, Paweł Kowal, Yuriy Kobilnyk, Mateusz Lesny, Michał Skrzypczyk, Dmytro Stroj, Victor Dosenko, Olena Kucheruk

“…Polynomial regression and Random Forest models were employed for prediction, and hierarchical clustering with K-means analysis was used for phenotypic stratification. …”
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Genetic analyses and molecular associations of FSHR and GH genes for semen traits in Egyptian buffalo by Abdelfatah R. Zaghloul, Maher H. Khalil, Mahmoud M. Iraqi, Amin M. S. Amin, Ibrahim Abousoliman, Ayman G. EL Nagar

“…Conclusion Enhancing management and feeding practices, the implementation and widespread use of artificial insemination as well as employing precise estimations of predicted breeding values in genetic improvement programs, should effectively enhance the semen traits of Egyptian buffalo bulls. …”
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Effects of marker density and minor allele frequency on genomic prediction for growth traits in Chinese Simmental beef cattle by Bo ZHU, Jing-jing ZHANG, Hong NIU, Long GUAN, Peng GUO, Ling-yang XU, Yan CHEN, Lu-pei ZHANG, Hui-jiang GAO, Xue GAO, Jun-ya LI

“…Two strategies were proposed for SNP selection to construct different marker densities: 1) select evenly-spaced SNPs (Strategy 1), and 2) select SNPs with large effects estimated from BayesB (Strategy 2). Furthermore, predictive ability was assessed in terms of the correlation between predicted genomic values and corrected phenotypes from 10-fold cross-validation. …”
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InertDB as a generative AI-expanded resource of biologically inactive small molecules from PubChem by Seungchan An, Yeonjin Lee, Junpyo Gong, Seokyoung Hwang, In Guk Park, Jayhyun Cho, Min Ju Lee, Minkyu Kim, Yun Pyo Kang, Minsoo Noh

“…Compared to conventional approaches such as random sampling or property-matched decoys, InertDB significantly improves predictive AI performance, particularly for phenotypic activity prediction by providing reliable inactive compound sets. …”
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Urinary Dickkopf-3 Reflects Disease Severity and Predicts Short-Term Kidney Function Decline in Renal Ciliopathies by Mareike Dahmer-Heath, Joachim Gerß, Danilo Fliser, Max Christoph Liebau, Thimoteus Speer, Anna-Katharina Telgmann, Kathrin Burgmaier, Petra Pennekamp, Lars Pape, Franz Schaefer, Martin Konrad, Jens Christian König

“…Introduction: Phenotypic heterogeneity and unpredictability of individual disease progression present enormous challenges in ultrarare renal ciliopathies. …”
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Prognostic value of tertiary lymphoid structures in triple-negative breast cancer: integrated analysis with the tumor microenvironment and clinicopathological features by Florence Boissière-Michot, Marie-Christine Chateau, Simon Thézenas, Virginie Lafont, Evelyne Crapez, Evelyne Crapez, Priyanka Sharma, Angélique Bobrie, Angélique Bobrie, Pascal Roger, Séverine Guiu, Séverine Guiu, William Jacot, William Jacot, William Jacot, William Jacot

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Genomic selection with GWAS-identified QTL markers enhances prediction accuracy for quantitative traits in poplar (Populus deltoides) by Chenchen Guo, Tongming Yin, Huaitong Wu, Xiaogang Dai, Yingnan Chen, Suyun Wei

“…Furthermore, the effects of QTL alleles were significantly correlated with phenotypic values. The integration of multi-trait QTL as random effects into genomic selection (GS) models significantly enhanced prediction accuracy, with an increase ranging from 0.06 to 0.48. …”
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A gene‐centered C. elegans protein–DNA interaction network provides a framework for functional predictions by Juan I Fuxman Bass, Carles Pons, Lucie Kozlowski, John S Reece‐Hoyes, Shaleen Shrestha, Amy D Holdorf, Akihiro Mori, Chad L Myers, Albertha JM Walhout

“…We used this network as a backbone to predict TF binding sites for 77 TFs, two‐thirds of which are novel, as well as integrate gene expression, protein–protein interaction, and phenotypic data to predict regulatory and biological functions for multiple genes and TFs.…”
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Cross-generational genomic prediction of Norway spruce (Picea abies) wood properties: an evaluation using independent validation by Haleh Hayatgheibi, Henrik R. Hallingbäck, Salvador A. Gezan, Sven-Olof Lundqvist, Thomas Grahn, Gerhard Scheepers, Sonali Sachin Ranade, Katri Kärkkäinen, M. Rosario García Gil

“…Notably, SAD-GBLUP provided comparable prediction accuracies to AWE-GBLUP, supporting the use of more practical and cost-effective phenotyping methods in operational breeding programs. …”
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Genomic prediction and validation strategies for reproductive traits in Holstein cattle across different Chinese regions and climatic conditions by Rui Shi, Luiz F. Brito, Shanshan Li, Liyun Han, Gang Guo, Wan Wen, Qingxia Yan, Shaohu Chen, Yachun Wang

“…Across-regional genomic prediction by RNM can account for genotype-by-environment interactions, potentially increase the accuracy of genomic prediction, and predict the performances of individuals in the environments with limited phenotypic data available.…”
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