Predicting the direction of phenotypic difference by David Gokhman, Keith D. Harris, Shai Carmi, Gili Greenbaum

“…Abstract Predicting phenotypes from genomes is a major goal in genetics, but for most complex phenotypes, predictions are largely inaccurate. …”
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Predictive genetic circuit design for phenotype reprogramming in plants by Ci Kong, Yin Yang, Tiancong Qi, Shuyi Zhang

“…Our versatile and robust framework, validated by constructing 21 two-input circuits with high prediction accuracy (R 2  = 0.81), enables multi-state phenotype control in both Arabidopsis thaliana and Nicotiana benthamiana in response to chemical inducers. …”
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Population FBA predicts metabolic phenotypes in yeast. by Piyush Labhsetwar, Marcelo C R Melo, John A Cole, Zaida Luthey-Schulten

“…We find the partitioning of flux between fermentation and respiration predicted by our model agrees with recent 13C fluxomics experiments, and that our model largely recovers the Crabtree effect (the experimentally known bias among certain yeast species toward fermentation with the production of ethanol even in the presence of oxygen), while FBA without proteomics constraints predicts respirative metabolism almost exclusively. …”
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Predictive evolution of metabolic phenotypes using model‐designed environments by Paula Jouhten, Dimitrios Konstantinidis, Filipa Pereira, Sergej Andrejev, Kristina Grkovska, Sandra Castillo, Payam Ghiachi, Gemma Beltran, Eivind Almaas, Albert Mas, Jonas Warringer, Ramon Gonzalez, Pilar Morales, Kiran R Patil

“…Abstract Adaptive evolution under controlled laboratory conditions has been highly effective in selecting organisms with beneficial phenotypes such as stress tolerance. The evolution route is particularly attractive when the organisms are either difficult to engineer or the genetic basis of the phenotype is complex. …”
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Blood Leukocyte Ratios as Predictive Markers of Chronic Enteropathy Phenotypes in Cats by Alexandros O. Konstantinidis, Katerina K. Adamama-Moraitou, Ashley Griggs, Margaret L. Musser, Ariel S. Nenninger, Nektarios Soubasis, Dimitra Pardali, Mathios E. Mylonakis, Albert E. Jergens

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Human genotype-to-phenotype predictions: Boosting accuracy with nonlinear models. by Aleksandr Medvedev, Satyarth Mishra Sharma, Evgenii Tsatsorin, Elena Nabieva, Dmitry Yarotsky

“…Genotype-to-phenotype prediction is a central problem of human genetics. …”
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Prediction of the Immune Phenotypes of Bladder Cancer Patients for Precision Oncology by Hyuna Cho, Feng Tong, Sungyong You, Sungyoung Jung, Won Hwa Kim, Jayoung Kim

“…Bladder cancer (BC) is the most common urinary malignancy; however accurate diagnosis and prediction of recurrence after therapies remain elusive. …”
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Inositol polyphosphates regulate and predict yeast pseudohyphal growth phenotypes. by Kaitlyn L Norman, Christian A Shively, Amberlene J De La Rocha, Nebibe Mutlu, Sukanya Basu, Paul J Cullen, Anuj Kumar

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Local connectome phenotypes predict social, health, and cognitive factors by Michael A. Powell, Javier O. Garcia, Fang-Cheng Yeh, Jean M. Vettel, Timothy Verstynen

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Author Correction: Predictive evolution of metabolic phenotypes using model-designed environments by Paula Jouhten, Dimitrios Konstantinidis, Filipa Pereira, Sergej Andrejev, Kristina Grkovska, Sandra Castillo, Payam Ghiaci, Gemma Beltran, Eivind Almaas, Albert Mas, Jonas Warringer, Ramon Gonzalez, Pilar Morales, Kiran R Patil

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Explainable Artificial Intelligence Models for Predicting Depression Based on Polysomnographic Phenotypes by Doljinsuren Enkhbayar, Jaehoon Ko, Somin Oh, Rumana Ferdushi, Jaesoo Kim, Jaehong Key, Erdenebayar Urtnasan

“…This study aimed to develop explainable artificial intelligence (AI) models to predict depression using polysomnographic phenotype data, ensuring high predictive performance while providing clear insights into the importance of features influencing the risk of depression. …”
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Automated prediction of fibroblast phenotypes using mathematical descriptors of cellular features by Alex Khang, Abigail Barmore, Georgios Tseropoulos, Kaustav Bera, Dilara Batan, Kristi S. Anseth

“…Here, we develop mathematical descriptors of cell morphology and intracellular structures to identify quantitative and interpretable cell features that capture the fibroblast-to-myofibroblast phenotypic transition in immunostained images. We train and validate models on features extracted from over 3000 primary heart valve interstitial cells and test their predictive performance on cells treated with the small molecule drugs 5-azacytidine and bisperoxovanadium (HOpic), which inhibited and promoted myofibroblast activation, respectively. …”
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Finding a constrained number of predictor phenotypes for multiple outcome prediction by Patrick B Ryan, Egill A Fridgeirsson, Jenna M Reps, Luis H John, Chungsoo Kim, Jenna Wong, Ross D Williams, Renae R Fisher

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Understanding molecular mechanisms and predicting phenotypic effects of pathogenic tubulin mutations. by Thomas J Attard, Julie P I Welburn, Joseph A Marsh

“…We performed a systematic survey of tubulin mutations across all isotypes in order to improve our understanding of how they cause disease, and increase our ability to predict their phenotypic effects. Both protein structural analyses and computational variant effect predictors were very limited in their utility for differentiating between pathogenic and benign mutations. …”
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THE VALUE OF CLINICAL AND LABORATORY PHENOTYPE IN PREDICTING THE COURSE OF COW’S MILK ALLERGY by E. E. Varlamov, A. N. Pampura

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Identification and analysis of individuals who deviate from their genetically-predicted phenotype. by Gareth Hawkes, Loic Yengo, Sailaja Vedantam, Eirini Marouli, Robin N Beaumont, GIANT Consortium, Jessica Tyrrell, Michael N Weedon, Joel Hirschhorn, Timothy M Frayling, Andrew R Wood

“…Using genome-wide imputed genetic data, we aimed to classify 158,951 unrelated individuals from the UK Biobank as either concordant or deviating from two well-measured phenotypes. We first applied our methods to standing height: our primary analysis classified 244 individuals (0.15%) as misaligned to their genetically predicted height. …”
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Normal tissue transcriptional signatures for tumor-type-agnostic phenotype prediction by Corey Weistuch, Kevin A. Murgas, Jiening Zhu, Larry Norton, Ken A. Dill, Allen R. Tannenbaum, Joseph O. Deasy

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Population health management of human phenotype ontology by James Andrew Henry

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Population health management through human phenotype ontology with policy for ecosystem improvement by James Andrew Henry

“…AimThe manuscript “Population Health Management (PHM) Human Phenotype Ontology (HPO) Policy for Ecosystem Improvement” steward safe science and secure technology in medical reform. …”
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Penalized Logistic Regression Models for Phenotype Prediction Based on Single Nucleotide Polymorphisms by seyedeh rezwan Hosseini, Farnaz Ghassemi, Mohammad Hasan Moradi

Subjects: “…complex diseases prediction…”
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