Circulating Hsp70: a tumor biomarker for lymph node metastases and early relapse in thoracic cancer by Dominik Lobinger, Nicholas Taylor, Verena Messner, Sophie Seier, Johannes Bodner, Erika Roberts, Ali Bashiri Dezfouli, Alan Graham Pockley, Seyer Safi, Gabriele Multhoff

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Errors-in-variables and validation problems in reaction norm predictions for wild populations by Rolf Ergon

“…Studies of phenotypic responses in wild populations are often based on reaction norm models where the environmental drivers in many cases are related to climate change. …”
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Genotype But Not Body Shape Predicts River Migration Success in Atlantic Salmon by Paolo Moccetti, Jonathan D. Bolland, Colin E. Adams, Jessica R. Rodger, Hannele M. Honkanen, Matthew Newton, Angus J. Lothian, Andy D. Nunn, Domino A. Joyce

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Sendotypes predict worsening renal function in chronic kidney disease patients by Thomas McLarnon, Steven Watterson, Sean McCallion, Eamonn Cooper, Andrew R. English, Ying Kuan, David S. Gibson, Elaine K. Murray, Frank McCarroll, Shu‐Dong Zhang, Anthony J. Bjourson, Taranjit Singh Rai

“…Abstract Background Senescence associated secretory phenotype (SASP) contributes to age‐related pathology, however the role of SASP in Chronic Kidney Disease (CKD) is unclear. …”
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Prediction of Antibiotic Resistance Genes in Cyanobacterial Strains by Whole Genome Sequencing by Duarte Balata, Tânia Rosado, Francisco Pina-Martins, Vera Manageiro, Carina Menezes, Eugénia Ferreira, Octávio S. Paulo, Manuela Caniça, Elsa Dias

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Enhancing prediction accuracy of grain yield in wheat lines adapted to the southeastern United States through multivariate and multi‐environment genomic prediction models incorpora... by Jordan McBreen, Md. Ali Babar, Diego Jarquin, Naeem Khan, Steve Harrison, Noah DeWitt, Mohamed Mergoum, Ben Lopez, Richard Boyles, Jeanette Lyerly, J. Paul Murphy, Ehsan Shakiba, Russel Sutton, Amir Ibrahim, Kimberly Howell, Jared H. Smith, Gina Brown‐Guedira, Vijay Tiwari, Nicholas Santantonio, David A. Van Sanford

“…Abstract Enhancing predictive modeling accuracy in wheat (Triticum aestivum) breeding through the integration of high‐throughput phenotyping (HTP) data with genomic information is crucial for maximizing genetic gain. …”
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The relationship between biological aging and psoriasis: evidence from three observational studies by Zheng Lin, Hong-fei Wang, Lu-yan Yu, Jia Chen, Cheng-cheng Kong, Bin Zhang, Xuan Wu, Hao-nan Wang, Yi Cao, Ping Lin

“…Finally, we used PhenoAge advance to predict death, with an AUC of 0.71 in the NHANES, an ACU of 0.79 for predicting death within 1 years in the general ward of MIMIC-IV. …”
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MRI-based machine learning reveals proteasome subunit PSMB8-mediated malignant glioma phenotypes through activating TGFBR1/2-SMAD2/3 axis by Dongling Pei, Zeyu Ma, Yuning Qiu, Minkai Wang, Zilong Wang, Xianzhi Liu, Long Zhang, Zhenyu Zhang, Ran Li, Dongming Yan

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Water temperature modulates multidimensional plastic responses to water flow during the ontogeny of a neotropical fish (Astyanax lacustris, characiformes) by Leandro Lofeu, Bianca Bonini-Campos, Tiana Kohlsdorf

“…Combination of high temperature and water flow has a major effect on body shape and unveils unique phenotypic patterns, supporting the prediction that high temperatures can amplify plastic responses to external signals. …”
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Links between self-monitoring data collected through smartphones and smartwatches and the individual disease trajectories of adult patients with depressive disorders: Study protoco... by Hanna Reich, Simon Schreynemackers, Rebeka Amin, Sascha Ludwig, Jil Zippelius, Johannes Leimhofer, Tobias Dunker, Elisabeth Schriewer, Angela Carell, Yvonne Weber, Ulrich Hegerl

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Integration of UAV Multi-Source Data for Accurate Plant Height and SPAD Estimation in Peanut by Ning He, Bo Chen, Xianju Lu, Bo Bai, Jiangchuan Fan, Yongjiang Zhang, Guowei Li, Xinyu Guo

“…Recent unmanned aerial vehicle (UAV) technology advancements have enabled high-throughput phenotyping at field scales. As a globally strategic oilseed crop, peanut plays a vital role in ensuring food and edible oil security. …”
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Expanding the Reach of Structured EHR Data with Clinical Notes by Seda Bilaloglu, Vincent J Major, Himanshu Grover, Isabel Metzger, Yindalon Aphinyanaphongs

“…We use History and Physical (H&P) notes written within 16 hours of hospitalization to predict 60-day, all-cause mortality. We test several neural network approaches and observe little improvement over a CNN by adding bi-directional recurrence or convolutional attention. …”
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Integrated clinical and proteomic-based model for diagnostic and prognostic prediction in pRCC by Zeya Xu, Linhui Zhang, Jiacheng Lyu, Maoping Cai, Tao Ji, Lin Bai, Liqing Li, Yao Zhu, Huashan Xu, Subei Tan, Hualei Gan, Shujuan Ni, Wenhao Xu, Xi Tian, Aihetaimujiang Anwaier, Beiyan Liu, Qinqin Hou, Guohai Shi, Hailiang Zhang, Jianyuan Zhao, Dingwei Ye, Yuanyuan Qu, Chen Ding

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Benchmarking of feed-forward neural network models for genomic prediction of quantitative traits in pigs by Junjian Wang, Francesco Tiezzi, Yijian Huang, Christian Maltecca, Jicai Jiang

“…These advanced modeling capabilities make them promising candidates for genomic prediction by potentially capturing the intricate relationships between genetic variants and phenotypes. …”
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Assessment of S100A8/A9 and resistin as predictive biomarkers for mortality in critically ill patients with sepsis by Jing Chen, Jing Chen, Jing Chen, Jing Chen, Jing Chen, Jing Chen, Zhengquan Liu, Zhengquan Liu, Zhengquan Liu, Zhengquan Liu, Zhengquan Liu, Zhengquan Liu, Fan Zhou, Fan Zhou, Fan Zhou, Fan Zhou, Fan Zhou, Fan Zhou, Ye Sun, Zhenyou Jiang, Pingsen Zhao, Pingsen Zhao, Pingsen Zhao, Pingsen Zhao, Pingsen Zhao, Pingsen Zhao

“…Additionally, resistin levels at ICU admission play an important role in predicting 28-day mortality risk in patients with both normal and mixed phenotypes with hyperinflammation. …”
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Multi‐trait/environment sparse genomic prediction using the SFSI R‐package by Marco Lopez‐Cruz, Gustavo delos Campos

“…Abstract Sparse selection indices (SSIs) can be used to predict the genetic merit of selection candidates using high‐dimensional phenotypes (e.g., crop imaging) measured on each of the candidates of selection. …”
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Improvement of prediction ability for genomic selection of dairy cattle by including dominance effects. by Chuanyu Sun, Paul M VanRaden, John B Cole, Jeffrey R O'Connell

“…However, nearly all prediction models for dairy cattle have included only additive effects because of the limited number of cows with both genotypes and phenotypes. …”
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Agent-based modeling for personalized prediction of an experimental immune response to immunotherapeutic antibodies. by Omri Matalon, Andrea Perissinotto, Kuti Baruch, Shai Braiman, Anat Geiger Maor, Eti Yoles, Ella Wilczynski, Uri Nevo, Avner Priel

“…In this work we developed and tested the ability of an Agent-Based Model (ABM) to predict the ex vivo immune response of memory T cells to anti-PD-L1 blocking antibody, based on personalized immune-phenotypes. …”
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Securing fruit trees future: AI-driven early warning and predictive systems for abiotic stress in changing climate by Muhammad Ahtasham Mushtaq, Muhammad Ateeq, Muhammad Ikram, Shariq Mahmood Alam, Muhammad Mohsin Kaleem, Muhammad Atiq Ashraf, Muhammad Asim, Khalid F. Almutairi, Mahmoud F. Seleiman, Fareeha Shireen

“…AI integrated approaches such as stress prediction, irrigation optimization, and image-based phenotyping have enhanced agriculture, while machine learning models like Random Forest and Gradient Boosting improve stress management. …”
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A framework for predictive modeling of microbiome multi-omics data: latent interacting variable-effects (LIVE) modeling by Javier Munoz Briones, Douglas K. Brubaker

“…LIVE integrates multi-omics data using single-omic latent variables (LV) organized in a structured meta-model to determine the combinations of features most predictive of a phenotype or condition. Results We developed a supervised version of LIVE leveraging sparse Partial Least Squares Discriminant Analysis (sPLS-DA) LVs, and an unsupervised version leveraging sparse Principal Component Analysis (sPCA) principal components which both can incorporate covariate awarness. …”
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