A novel computational approach for predicting complex phenotypes in Drosophila (starvation-sensitive and sterile) by deriving their gene expression signatures from public data. by Dobril K Ivanov, Gerrit Bostelmann, Benoit Lan-Leung, Julie Williams, Linda Partridge, Valentina Escott-Price, Janet M Thornton

“…The results from our study show that it is possible to integrate seemingly different gene-expression microarray data and predict a potential phenotypic manifestation with a relatively high degree of confidence (>80% AUC). …”
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Whole-genome sequencing of two clinical strains of <i>Mycobacterium tuberculosis</i> with phenotypic susceptibility to rifampicin but predicted resistance by Xpert MTB/RIF by Oleg B. Ogarkov, Viacheslav V. Sinkov, Tatyana A. Kuhtina, Svetlana N. Zhdanova, Ilya G. Kondratov

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Remote myocardial fibrosis predicts adverse outcome in patients with myocardial infarction on clinical cardiovascular magnetic resonance imaging by Nicholas Black, Joshua Bradley, Erik B. Schelbert, Laura J. Bonnett, Gavin A. Lewis, Jakub Lagan, Christopher Orsborne, Pamela F. Brown, Fardad Soltani, Fredrika Fröjdh, Martin Ugander, Timothy C. Wong, Miho Fukui, Joao L. Cavalcante, Josephine H. Naish, Simon G. Williams, Theresa McDonagh, Matthias Schmitt, Christopher A. Miller

“…Results: Remote myocardial fibrosis was a strong predictor of primary outcome (χ2: 15.6, hazard ratio [HR]: 1.07 per 1% increase in ECV, 95% confidence interval [CI]: 1.04–1.11, p < 0.001) and was separately predictive of both HHF and death. The strongest predictors of remote ECV were diabetes, sex, natriuretic peptides, and body mass index, but, despite extensive phenotyping, the adjusted model R2 was only 0.283. …”
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Gut health predictive indices linking gut microbiota dysbiosis with healthy state, mild gut discomfort, and inflammatory bowel disease phenotypes using gut microbiome profiling by Joann Phan, Suneer Jain, Jurgen F. Nijkamp, Rajkumar Sasidharan, Ashish Agarwal, Julia K. Bird, Anneleen Spooren, Jonas Wittwer Schegg, Emiel Ver Loren van Themaat, Tim N. Mak

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Maturity Prediction in Soybean Breeding Using Aerial Images and the Random Forest Machine Learning Algorithm by Osvaldo Pérez, Brian Diers, Nicolas Martin

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Prakriti elucidates the inter-individual variability in coronary artery disease risk-predicting biomarkers: A tertiary care hospital-based case control study by Pamila Dua, Bhavana Prasher, Sandeep Seth, Shivam Pandey, Subir Kumar Maulik, K.H. Reeta

“…However, conflicting results pose a significant challenge probably due to phenotypic heterogeneity. In Ayurveda, individuals are classified into phenotypes- Prakriti, which helps in predicting an individual's susceptibility to disease, its prognosis and selection of therapy. …”
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Exploring the relationship between predicted negative energy balance and its biomarkers of Holstein cows in first-parity early lactation by Hongqing Hu, Sébastien Franceschini, Pauline Lemal, Clément Grelet, Yansen Chen, Hadi Atashi, Katrien Wijnrocx, Hélène Soyeurt, Nicolas Gengler

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Lethal versus surviving sepsis phenotypes displayed a partly differential regional expression of neurotransmitters and inflammation and did not modify the blood–brain barrier perme... by Fatemeh Azizian-Farsani, Katrin Weixelbaumer, Daniel Mascher, Andrea Klang, Sandra Högler, Nora Dinhopl, Barbara Bauder, Herbert Weissenböck, Alexander Tichy, Peter Schmidt, Hermann Mascher, Marcin F. Osuchowski

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Predicting high anger intensity using ecological momentary assessment and wearable-derived physiological data in a trauma-affected sample by Olivia Metcalf, Karen E. Lamb, David Forbes, Meaghan L. O’Donnell, Tianchen Qian, Tracey Varker, Sean Cowlishaw, Sophie Zaloumis

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Image-based yield prediction for tall fescue using random forests and convolutional neural networks by Sarah Ghysels, Bernard De Baets, Dirk Reheul, Steven Maenhout

“…The field of automated high-throughput phenotyping aims to resolve these issues. A widely adopted strategy uses drone images processed by machine learning algorithms to characterise phenotypes. …”
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Human limits in machine learning: prediction of potato yield and disease using soil microbiome data by Rosa Aghdam, Xudong Tang, Shan Shan, Richard Lankau, Claudia Solís-Lemus

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Relapse prediction using wearable data through convolutional autoencoders and clustering for patients with psychotic disorders by April Yujie Yan, Traci Jenelle Speed, Casey Overby Taylor

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Hormonal predictors of the lean phenotype in humans by Mohamed Badie Ahmed, Abdella M. Habib, Saif Badran, Abeer Alsherawi, Fatima Al-Mohannadi, Sherouk Essam Elnefaily, Atalla Hammouda, Graeme E. Glass, Ibrahem Abdalhakam, Abdul-Badi Abou-Samra, Suhail A. Doi

“…History of bariatric surgery weakly predicted the lean phenotype after adjusting for leptin and gut hormone levels. …”
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Genotypic-Phenotypic Diversity in Beta Thalassemia by Ravindra Kumar, Vandana Arya

“…Genes governing the hyperbilirubinemia (UGT1A1), Iron overload (HFE), skeletal deformity and bone diseases (VDR, COL1A1& COL1A2), hypercoagulable state (FVL, PT& MTHFR) and cardiac complications (Apo E, GST) may also have predictable role in increasing phenotypic complexity in thalassemia patients.…”
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The genotype‐phenotype landscape of an allosteric protein by Drew S Tack, Peter D Tonner, Abe Pressman, Nathan D Olson, Sasha F Levy, Eugenia F Romantseva, Nina Alperovich, Olga Vasilyeva, David Ross

“…We find that in many cases, allosteric phenotypes can be quantitatively predicted with additive or neural‐network models, but unpredictable changes also occur. …”
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IMMUNOLOGICAL FEATURES OF SEPSIS PHENOTYPES AND ENDOTYPES by Mariia B. Potapova, Vitaly V. Zverev, Maxim A. Babaev, Ekaterina A. Bogdanova, Oksana O. Grin, Irina B. Semenova, Ekaterina A. Meremianina, Oxana A. Svitich

“…In recent years, research has shifted from clinical signs to the analysis of immunological and molecular mechanisms, which has led to the identification of  specific phenotypes and endotypes of the disease. Sepsis phenotypes are based on clinical manifestations and biomarkers, while endotypes are defined by molecular mechanisms, including immune gene expression patterns.This article reviews key aspects of the innate and adaptive immune responses in sepsis, including the activation of proinflammatory cytokines, the development of coagulopathies, and disruptions of endothelial integrity and microvascular regulation.Moreover, the importance of mechanisms such as hyperinflammation, the simultaneous development of immunosuppression and functional exhaustion of immunocompetent cells is emphasized. …”
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ARRHYTHMIC PHENOTYPE OF NON-COMPACTION CARDIOMYOPATHY by S. M. Komissarova, N. N. Chakova, N. M. Rinejskaya, T. V. Dolmatovich, S. S. Niyazova

“…To evaluate the genotype-phenotype association in Belarusian patients with non-compaction cardiomyopathy (NCCM) and clinically significant ventricular arrhythmias.Materials and methods. …”
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E2F1-induced autocrine IL-6 inflammatory loop mediates cancer-immune crosstalk that predicts T cell phenotype switching and therapeutic responsiveness by Alf Spitschak, Prabir Dhar, Krishna P. Singh, Rosaely Casalegno Garduño, Shailendra K. Gupta, Shailendra K. Gupta, Julio Vera, Luca Musella, Nico Murr, Anja Stoll, Brigitte M. Pützer, Brigitte M. Pützer

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Pediatric sepsis phenotypes for enhanced therapeutics: An application of clustering to electronic health records by Ioannis Koutroulis, Tom Velez, Tony Wang, Seife Yohannes, Jessica E. Galarraga, Joseph A. Morales, Robert J. Freishtat, James M. Chamberlain

“…Additional prospective studies are needed to validate clinical utility of predictive models that target derived pediatric sepsis phenotypes in emergency department settings.…”
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VMGP: A unified variational auto-encoder based multi-task model for multi-phenotype, multi-environment, and cross-population genomic selection in plants by Xiangyu Zhao, Fuzhen Sun, Jinlong Li, Dongfeng Zhang, Qiusi Zhang, Zhongqiang Liu, Changwei Tan, Hongxiang Ma, Kaiyi Wang

“…It is particularly advantageous for breeders who prioritize phenotype prediction yet may not possess extensive knowledge in deep learning or proficiency in parameter tuning.…”
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