Covariate adjustment of spirometric and smoking phenotypes: The potential of neural network models. by Kirsten Voorhies, Ruofan Bie, John E Hokanson, Scott T Weiss, Ann Chen Wu, Julian Hecker, Georg Hahn, Dawn L Demeo, Edwin Silverman, Michael H Cho, Christoph Lange, Sharon M Lutz

“…As an alternative, we used neural networks for the modeling of complex phenotypes and covariate adjustments. We compared the prediction accuracy of the neural network models to that of classical approaches based on linear regression. …”
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Phenotypic and in silico characterization of carbapenem-resistant Serratia marcescens clinical strains by Anelise Stella Ballaben, Otávio G.G. de Almeida, Joseane Cristina Ferreira, Doroti de Oliveira Garcia, Yohei Doi, Robert K. Ernst, Marcia R. von Zeska Kress, Ana Lúcia da Costa Darini

“…Whole genome sequencing was performed using Illumina NextSeq 250-bp paired-end sequencing for two isolates, Sm424 and Sm613, which presented representative phenotypes. Results: The pathogenicity of both sequenced strains was predicted using the Pathogen Finder tool. …”
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Clinical Phenotype Identification and Validation of Patients with Sepsis in the Intensive Care Unit by GONG Chao, YU Na, CHEN Haoran

“…Then, supervised machine learning algorithms (lightweight gradient boosting machine) were used for the prediction of the patient's phenotypes, and were further combined with SHAP (Shapely Additive eXplanations) for the identification of important features. …”
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Early markers of phenotypic heterogeneity in the induced model of systemic lupus erythematosus by O. P. Kolesnikova, E. V. Goiman, E. D. Gavrilova, E. N. Demchenko, V. A. Kozlov

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An explainable dataset linking facial phenotypes and genes to rare genetic diseases by Jie Song, Mengqiao He, Shumin Ren, Bairong Shen

“…Although AI-driven image recognition achieves high diagnostic accuracy, it often fails to explain its predictions. In this study, we present the Facial phenotype-Gene-Disease Dataset (FGDD), an explainable dataset collected from 509 research publications. …”
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Label efficient phenotyping for Long COVID using electronic health records by Chuan Hong, Jun Wen, Harrison G. Zhang, Vidul Ayakulangara Panickan, Doris Y. Yang, Alicia W. Chen, Xin Xiong, Xuan Wang, Michele Morris, Sara Morini, Rahul Sangar, Andrew Dey, Malarkodi J. Samayamuthu, Katherine Liao, Clara-Lea Bonzel, Vidisha Tanukonda, Monika Maripuri, Jacqueline Honerlaw, Yuk-Lam Ho, Shyam Visweswaran, Isaac S. Kohane, Kelly Cho, Gabriel Brat, Zongqi Xia, Tianxi Cai

“…Developing efficient phenotyping algorithms is crucial to enabling risk prediction and effective management of Long COVID. …”
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Tunable phenotypic variability through an autoregulatory alternative sigma factor circuit by Christian P Schwall, Torkel E Loman, Bruno M C Martins, Sandra Cortijo, Casandra Villava, Vassili Kusmartsev, Toby Livesey, Teresa Saez, James C W Locke

“…However, how bacterial populations modulate their level of phenotypic variability remains unclear. Here we show that the alternative sigma factor σV circuit in Bacillus subtilis generates functional phenotypic variability that can be tuned by stress level, environmental history and genetic perturbations. …”
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Phenotypic, Functional, and Safety Control at Preimplantation Phase of MSC-Based Therapy by Wioletta Lech, Anna Figiel-Dabrowska, Anna Sarnowska, Katarzyna Drela, Patrycja Obtulowicz, Bartlomiej Henryk Noszczyk, Leonora Buzanska, Krystyna Domanska-Janik

“…In addition the high phenotypic plasticity of MSC population makes it enormously sensitive to any changes in environmental properties including fluctuation in oxygen concentration. …”
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Therapeutic potential of tumor-associated neutrophils: dual role and phenotypic plasticity by Yanting Zhou, Guobo Shen, Xikun Zhou, Jing Li

“…Finally, we discuss the potential of TANs and their related markers as emerging biomarkers for predicting the prognosis of cancer patients.…”
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ALDH1A3 correlates with luminal phenotype in prostate cancer by Shangqian Wang, Chao Liang, Meiling Bao, Xiao Li, Lei Zhang, Shuang Li, Chao Qin, Pengfei Shao, Jie Li, Lixin Hua, Zengjun Wang

“…Furthermore, we looked up our single center primary prostate cancer post-operative follow-up data and suggested that the high level ALDH1A3 expression could predict the poor progression-free survival in a 158-patient cohort. …”
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A model-based analysis of culture-dependent phenotypes of mESCs. by Maria Herberg, Tüzer Kalkan, Ingmar Glauche, Austin Smith, Ingo Roeder

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Association Between Metabolic and Obesity Phenotypes and Diabetes Risk in Children and Adolescents by Hao H, Su Y, Feng M

“…The correlation between metabolic/obesity phenotypes and the development of pre-diabetes in children and adolescents remains unclear.Methods: This study aimed to explore this association within a cohort of 1,524 subjects aged 7 to 18 years. …”
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Light-driven phenotypic plasticity in the depth-generalist coral, Pavona varians. by Claire J Lewis, Shayle B Matsuda, Tayler L Sale, Caitlyn Genovese, Chelsea S Wolke, Norton Chan, Stephen Ranson, Jake M Ferguson, Amy L Moran, David A Gulko, Peter B Marko

“…In reduced light conditions, this species may mitigate some of the negative effects of bleaching temperatures on growth. We predict that P. varians is likely one of a minority of species that may benefit from deep reef refugia.…”
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Phenotyping of outpatients with heart failure with preserved ejection fraction and poor prognosis by V. N. Larina, V. I. Lunev

“…The threshold value for predicting death for LVGFI was ≤21,4%, for derivative index of LVGFI — ≥303,6 ml. …”
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Phenotypic and genetic analysis of auction selling performances of young Simmental calves by Alberto Cesarani, Lorenzo Degano, Salvatore Mastrangelo, Matthias Wenter, Martin Tröger, Daniele Vicario, Roberto Steri, Giuseppe Pulina, Nicolò Pietro Paolo Macciotta

“…The efficacy of these traits for predicting future performances of calves at the end of the cycle of fattening should be further investigated.…”
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Heterogeneity of phenotypic manifestations of cystic fibrosis in children and predictors of the disease severity by K. V. Skriabina, S. I. Ilchenko, A. O. Fialkovska

“…The aim of the study was to investigate the heterogeneity of phenotypic manifestations of cystic fibrosis (CF) in children depending on the CFTR gene mutation and to determine predictors of the disease severity for the personalization of treatment and prevention of complications. …”
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Influence of race, ethnicity, and sex on the performance of epigenetic predictors of phenotypic traits by Dennis Khodasevich, Nicole Gladish, Saher Daredia, Anne K. Bozack, Hanyang Shen, Jamaji C. Nwanaji-Enwerem, Belinda L. Needham, David H. Rehkopf, Andres Cardenas

“…Abstract Background DNA methylation-based predictors of phenotypic traits including leukocyte proportions, smoking activity, biological aging, and circulating levels of plasma proteins are widely used as biomarkers in public health research. …”
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Conserved missense variant pathogenicity and correlated phenotypes across paralogous genes by Tobias Brünger, Alina Ivaniuk, Eduardo Pérez-Palma, Ludovica Montanucci, Stacey Cohen, Lacey Smith, Shridhar Parthasarathy, Ingo Helbig, Michael Nothnagel, Patrick May, Dennis Lal

“…Conclusion Conserved pathogenic missense variants in paralogous genes provide robust, quantifiable support for clinical variant interpretation, and phenotype-informed mapping further improves predictions.…”
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Atypical Presentation of IARS1‐Related Disorder: Expanding the Phenotype and Genotype by Parith Wongkittichote, Kira E. Jonatzke, Benjamin T. Hyde, Lance W. Peterson, Mai He, Robert C. McKinstry, Anthony Antonellis, Marwan Shinawi

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Disease Phenotypes in Refractory Musculoskeletal Pain Syndromes Identified by Unsupervised Machine Learning by Thomas Hügle, Tiffany Prétat, Marc Suter, Chris Lovejoy, Pedro Ming Azevedo

“…Seventy‐eight percent of the patients fulfilled the criteria for fibromyalgia, 77% had a concomitant psychiatric‐mediated disorder, and 22% a concomitant rheumatic immune‐mediated disorder. Five patient phenotypes were identified by hierarchical agglomerative clustering as a form of unsupervised learning, and a predictive model for the Brief Pain Inventory (BPI) response was generated. …”
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