Gadoxetic Acid-Enhanced Magnetic Resonance Imaging Features Can Predict Immune-Excluded Phenotype of Hepatocellular Carcinoma by Eisuke Ueshima, Keitaro Sofue, Takahiro Kodama, Shuhei Yamamoto, Masato Komatsu, Shohei Komatsu, Nobuaki Ishihara, Akihiro Umeno, Takeru Yamaguchi, Masatoshi Hori, Takumi Fukumoto, Tetsuo Takehara, Takamichi Murakami

“…Conclusion: HCCs with rim APHE, peritumoral enhancement in arterial phase, and intermediate intensity on HBP with visual 3-point scale could be non-invasive biomarkers to predict the immune-excluded phenotype with the tumor immune barrier. …”
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Genomic prediction with kinship-based multiple kernel learning produces hypothesis on the underlying inheritance mechanisms of phenotypic traits by Daniele Raimondi, Nora Verplaetse, Antoine Passemiers, Deborah Sarah Jans, Isabelle Cleynen, Yves Moreau

“…Abstract Background Genomic prediction encompasses the techniques used in agricultural technology to predict the genetic merit of individuals towards valuable phenotypic traits. …”
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Phenotypic and genetic characterisation of methane emission predicted from milk fatty acid profile of Sarda dairy ewes by F. Correddu, S. Carta, M. Congiu, A. Cesarani, C. Dimauro, N. P. P. Macciotta

“…Results of the present study, even if based on prediction equations developed for cows, increased the knowledge about the phenotypic and genetic background of methane emissions in Sarda dairy sheep.…”
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Optimal machine learning algorithms and UAV multispectral imagery for crop phenotypic trait estimation: a comprehensive review and meta-analysis by Adama Ndour, Gerald Blasch, João Valente, Bisrat Haile Gebrekidan, Tesfaye Shiferaw Sida

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Clinical characterization of blood pressure phenotypes: the BP phenotype score by Maha A. Al-Mohaissen, Maisa A. Al Zohaifi, Terry Lee, Nada A. Almalki, Hend Aleiban, Rabah A. Al-Mehisen

“…Background Evidence has linked blood pressure (BP) phenotypes with certain clinical, psychosocial, and occupational features, and characteristic BP variability.Objective We aimed to evaluate the value of a diagnostic score developed from these characteristics in predicting BP phenotypes, when used in a manner comparable to the application of out-of-office techniques.Methods Adult patients with no prior diagnosis of hypertension attending their office appointments, were prospectively enrolled. …”
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Advances in modeling cellular state dynamics: integrating omics data and predictive techniques by Sungwon Jung

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Hybrid Deep Learning Approaches for Improved Genomic Prediction in Crop Breeding by Ran Li, Dongfeng Zhang, Yanyun Han, Zhongqiang Liu, Qiusi Zhang, Qi Zhang, Xiaofeng Wang, Shouhui Pan, Jiahao Sun, Kaiyi Wang

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Prediction of Long-Term Benefits of Inhaled Steroids by Phenotypic Markers in Moderate-to-Severe COPD: A Randomized Controlled Trial. by Jiska B Snoeck-Stroband, Therese S Lapperre, Peter J Sterk, Pieter S Hiemstra, Henk A Thiadens, H Marike Boezen, Nick H T Ten Hacken, Huib A M Kerstjens, Dirkje S Postma, Wim Timens, Jacob K Sont, GLUCOLD Study Group

“…We aimed to identify which clinical, physiological and non-invasive inflammatory characteristics predict the benefits of ICS on lung function decline in COPD.…”
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Fast decliner phenotype of chronic obstructive pulmonary disease (COPD): applying machine learning for predicting lung function loss by David B Price, Vasilis Nikolaou, Sebastiano Massaro, Wolfgang Garn, Masoud Fakhimi, Lampros Stergioulas

“…We characterised patients with the ‘fast decliner’ phenotype, determined its reproducibility and predicted lung function decline after COPD diagnosis.Methods A prospective 4 years observational study that applies machine learning tools to identify COPD phenotypes among 13 260 patients from the UK Royal College of General Practitioners and Surveillance Centre database. …”
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Personalized prediction of negative affect in individuals with serious mental illness followed using long-term multimodal mobile phenotyping by Christian A. Webb, Boyu Ren, Habiballah Rahimi-Eichi, Bryce W. Gillis, Yoonho Chung, Justin T. Baker

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Multimodal Deep Learning Integration of Image, Weather, and Phenotypic Data Under Temporal Effects for Early Prediction of Maize Yield by Danial Shamsuddin, Monica F. Danilevicz, Hawlader A. Al-Mamun, Mohammed Bennamoun, David Edwards

“…In this paper, we showcase the development of a multimodal deep learning model using RGB images, phenotypic, and weather data under temporal effects to predict the yield potential of maize before or during anthesis and silking stages. …”
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Leveraging Multi-Omics Data with Machine Learning to Predict Grain Yield in Small vs. Big Plot Wheat Trials by Jordan McBreen, Md Ali Babar, Diego Jarquin, Yiannis Ampatzidis, Naeem Khan, Sudip Kunwar, Janam Prabhat Acharya, Samuel Adewale, Gina Brown-Guedira

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Robust Fusion of Time Series and Image Data for Improved Multimodal Clinical Prediction by Ali Rasekh, Reza Heidari, Amir Hosein Haji Mohammad Rezaie, Parsa Sharifi Sedeh, Zahra Ahmadi, Prasenjit Mitra, Wolfgang Nejdl

“…Our motivation comes from the important areas of predicting mortality and phenotyping where using different modalities of data could significantly improve our ability to predict. …”
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Effects of measurement errors on relationships between resting-state functional connectivity and psychological phenotypes by Tomosumi Haitani, Yuki Sakai, Saori C. Tanaka

“…We also accounted for measurement errors in psychological phenotypes at the test-, subscale-, or item-level. This study investigates: (i) how measurement errors, including state effects, weaken the associations between RSFC and psychological phenotypes, including cognition, mental health, and personality, and influence sample size planning and (ii) predictive accuracy on the phenotypes from RSFC, using SEM. …”
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Genomic prediction of plant traits by popular machine learning methods by K. N. Kozlov, M. P. Bankin, E. A. Semenova, M. G. Samsonova

Subjects: “…genomic prediction…”
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ORF1ab codon frequency model predicts host-pathogen relationship in orthocoronavirinae by Phillip E. Davis, Joseph A. Russell

“…Predicting phenotypic properties of a virus directly from its sequence data is an attractive goal for viral epidemiology. …”
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Phenotypic plasticity evolves at multiple biological levels in response to environmental predictability in a long-term experiment with a halotolerant microalga. by Christelle Leung, Daphné Grulois, Leandro Quadrana, Luis-Miguel Chevin

“…However, the extent to which patterns of evolution of plasticity for more integrated traits are mirrored by their underlying molecular mechanisms remains unclear, especially in response to well-characterized selective pressures exerted by environmental predictability. Here, we used experimental evolution with the microalgae Dunaliella salina under controlled environmental fluctuations, to test whether the evolution of phenotypic plasticity in responses to environmental predictability (as measured by the squared autocorrelation ρ2) occurred across biological levels, going from DNA methylation to gene expression to cell morphology. …”
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Prediction for Weight Loss and Regain Based on Multi-Omics and Phenotypic Features: Results From a Caloric-Restricted Feeding Trial by Lin Li, Rui Li, Ruyi Li, Kai Zhu, Zixin Qiu, Hancheng Yu, An Pan, Gang Liu

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Baseline T cell immune phenotypes predict virologic and disease control upon SARS-CoV infection in Collaborative Cross mice. by Jessica B Graham, Jessica L Swarts, Sarah R Leist, Alexandra Schäfer, Vineet D Menachery, Lisa E Gralinski, Sophia Jeng, Darla R Miller, Michael A Mooney, Shannon K McWeeney, Martin T Ferris, Fernando Pardo-Manuel de Villena, Mark T Heise, Ralph S Baric, Jennifer M Lund

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Promising photosynthetic parameters Y(II) and NPQ for predicting barley drought tolerance by Yu. A. Nemtsova, D. V. Kuznetsova, M. A. Grinberg, V. A. Vodeneev, A. A. Dontsova, D. P. Dontsov, V. S. Bondarenko, P. Yu. Volkova, E. V. Bondarenko

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