Genetic Risk Score Modelling for Disease Progression in New-Onset Type 1 Diabetes Patients: Increased Genetic Load of Islet-Expressed and Cytokine-Regulated Candidate Genes Predict... by Caroline A. Brorsson, Lotte B. Nielsen, Marie Louise Andersen, Simranjeet Kaur, Regine Bergholdt, Lars Hansen, Henrik B. Mortensen, Flemming Pociot, Joachim Størling, Hvidoere Study Group on Childhood Diabetes

“…We aimed at testing whether a genetic risk score could predict glycemic control and residual β-cell function in type 1 diabetes (T1D). …”
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Genomic selection in forest trees comes to life: unraveling its potential in an advanced four-generation Eucalyptus grandis population by Damián Duarte, Esteban J. Jurcic, Esteban J. Jurcic, Joaquín Dutour, Pamela V. Villalba, Carmelo Centurión, Dario Grattapaglia, Eduardo P. Cappa, Eduardo P. Cappa

“…Employing single-step genomic BLUP, we compared the genomic predictions of breeding values (GEBVs) for 1,153 fourth-generation full-sib seedlings in the greenhouse with their later-collected phenotypic estimated breeding values (EBVs) at age three years. …”
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Marker Haplotype Construction for the Hybrid Necrosis Gene <i>Ne2</i> and Its Distribution in Old and New Wheat Varieties by Volker Mohler, Adalbert Bund, Lorenz Hartl, Theresa Albrecht

“…We analyzed a set of wheat varieties which had partial SNPs and phenotypic data, i.e., hybrid necrosis and leaf rust reactions, using Kompetitive Allele-Specific PCR (KASP) markers previously available for <i>Ne2</i>. …”
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Plant photosynthesis in basil (C3) and maize (C4) under different light conditions as basis of an AI-based model for PAM fluorescence/gas-exchange correlation by Isabell Pappert, Stefan Klir, Luca Jokic, Celine Ühlein, Khanh Tran Quoc, Ralf Kaldenhoff

“…Accurate, non-invasive prediction of photosynthetic performance under varying conditions is highly relevant for phenotyping and stress diagnostics. …”
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Optimizing fully-efficient two-stage models for genomic selection using open-source software by Javier Fernández-González, Julio Isidro y Sánchez

“…Single-stage models predict GEBVs from phenotypic observations in one step, fully accounting for the entire variance-covariance structure among genotypes, but face computational challenges. …”
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Multifactor Analysis of a Genome-Wide Selection System in <i>Brassica napus</i> L. by Wanqing Tan, Zhiyuan Wang, Jia Wang, Sayedehsaba Bilgrami, Liezhao Liu

“…The results highlight the superior prediction accuracy (PA) under the RF model. Among the ten traits, the PA of glucosinolate was the highest, and that of linolenic acid was the lowest. …”
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Lower dietary folate intake increases the risk of autoimmune thyroiditis by Lijun Chen, Changjian Yan, Yingxuan Lin, Liqun Huang, Chunling Huang, Zhida Wei, Hongli Lin, Xiaohong Wu, Ruhai Lin, Zhengrong Jiang, Huibin Huang

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Substantial Heritability Underlies Fairness Norm Adaptation Capability and its Neural Basis by Yuening Jin, Dang Zheng, Ruolei Gu, Qingchen Fan, Martin Dietz, Changshuo Wang, Xinying Li, Jie Chen, Yuanyuan Hu, Yuan Zhou

“…The anterior insula has a significant phenotypic correlation, whereas the Supplementary Motor Area/Medial Frontal Gyrus (SMA/mSFG) shows both a significant phenotypic correlation and a shared genetic influence with the learning rate, an index for norm adaptation. …”
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Bind: large-scale biological interaction network discovery through knowledge graph-driven machine learning by Naafey Aamer, Muhammad Nabeel Asim, Aamer Iqbal Bhatti, Andreas Dengel

“…Optimal embedding-classifier combinations achieved F1-scores ranging from 0.85 to 0.99 across different biological domains. In a drug-phenotype interaction case study, BIND generated 1355 high confidence predictions, with novel interactions successfully validated through existing literature evidence. …”
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Identification of gray leaf spot–resistant donor lines in tropical maize germplasm and their agronomic performance under artificial inoculation by L. M. Suresh, Manje Gowda, Yoseph Beyene, Dan Makumbi, Kulai Amadu Manigben, Kulai Amadu Manigben, Kulai Amadu Manigben, Juan Burgueño, Robert Okayo, Vincent W. Woyengo, Boddupalli M. Prasanna

“…However, SNPs on chromosomes 9 and 10 were unique to the present study. Genomic prediction on GLS traits revealed moderate to high prediction correlations, suggesting its usefulness in the selection of desirable candidates with favorable alleles for GLS resistance. …”
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Overcoming challenges in metagenomic AMR surveillance with nanopore sequencing: a case study on fluoroquinolone resistance by Bram Bloemen, Bram Bloemen, Mathieu Gand, Moniek Ringenier, Bert Bogaerts, Kevin Vanneste, Kathleen Marchal, Kathleen Marchal, Nancy H. C. Roosens, Jeroen Dewulf, Filip Boyen, Sigrid C. J. De Keersmaecker

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Association analysis of intragenic molecular markers related to fiber quality and tensile strength of abaca (Musa textilis Nee) by Mariecris Rizalyn Mendoza, Antonio Laurena, Maria Genaleen Diaz, Eureka Teresa Ocampo, Tonette Laude, Antonio Lalusin

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Biodegradation of plasticizers by novel strains of bacteria isolated from plastic waste near Juhu Beach, Mumbai, India by Khushboo Sharma, Anuraj Nayarisseri, Sanjeev Kumar Singh

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NLP for computational insights into nutritional impacts on colorectal cancer care by Shengnan Gong, Xiaohong Jin, Yujie Guo, Jie Yu

“…ATSO-LLMs are employed to analyze the processed dietary data, identifying key nutritional factors and forecasting CRC and non-CRC phenotypes based on dietary patterns. The results show that combining NLP-derived features with ATSO-LLMs significantly enhances prediction accuracy (98.4 %), sensitivity (97.6 %) specificity (96.9 %) and F1-Score (96.2 %), with minimal misclassification rates. …”
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Feasibility analysis of the SICKLECHECK™ test kit for rapid screening of sickle cell disease at a County Referral Hospital in Kenya by Antony S. Katayi, Phidelis M. Marabi, Stanslaus K. Musyoki

“…Sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy were calculated using MedCalc™ statistical software. …”
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Characterization of Macular Fundus Autofluorescence Changes in Patients with Retinitis Pigmentosa by Muhammad Jehanzeb Khan, Zainab Rustam, Faiqa Binte Aamir, Maria Chairez Miranda, Imad Shaikh, Anam Akhlaq, Jiawen Liu, Mandeep Singh, Xiangrong Kong, Peter A. Campochiaro

“…Longitudinal studies are needed to test whether presumed early AF phenotypes evolve into later phenotypes. Use of the grading scheme for patient populations in interventional trials could help determine if any of the defined AF features provide predictive value for therapeutic responses. …”
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Circadian phase resetting via single and multiple control targets. by Neda Bagheri, Jörg Stelling, Francis J Doyle

“…These studies prove the efficacy and immediate application of model predictive control in experimental studies and medicine. …”
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Multi-omics data integration reveals metabolome as the top predictor of the cervicovaginal microenvironment. by Nicholas A Bokulich, Paweł Łaniewski, Anja Adamov, Dana M Chase, J Gregory Caporaso, Melissa M Herbst-Kralovetz

“…Different feature classes were important for prediction of different phenotypes. Lipids (e.g. sphingolipids and long-chain unsaturated fatty acids) were strong predictors of genital inflammation, whereas predictions of vaginal microbiota and vaginal pH relied mostly on alterations in amino acid metabolism. …”
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Sparse testing designs for optimizing resource allocation in multi‐environment cassava breeding trials by Nelson Lubanga, Beatrice E. Ifie, Reyna Persa, Ibnou Dieng, Ismail Yusuf Rabbi, Diego Jarquin

“…Sparse testing using a model incorporating G × E could be implemented to reduce cost of phenotyping in cassava METs. If data were available, integrating crop growth models (CGMs) with genomic prediction holds the potential to improve predictive ability. …”
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Enhanced genetic fine mapping accuracy with Bayesian Linear Regression models in diverse genetic architectures. by Merina Shrestha, Zhonghao Bai, Tahereh Gholipourshahraki, Astrid J Hjelholt, Sile Hu, Mads Kjolby, Palle Duun Rohde, Peter Sørensen

“…Through extensive simulations and analyses of UK Biobank (UKB) phenotypes, we assessed F1 classification scores and predictive accuracy across models. …”
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