Peer victimization but not social anxiety negatively influences predicted enjoyment during peer interactions by Isabel Leiva, Samantha S. Reisman, Chelsea Helion, Vishnu P. Murty, Johanna Jarcho

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Leveraging Machine Learning and Genetic Risk Scores for the Prediction of Metabolic Syndrome in Children with Obesity by Concepción M. Aguilera, Mireia Bustos-Aibar, Augusto Anguita-Ruiz, Álvaro Torres-Martos, Gloria Bueno, Rosaura Leis, Jesús Alcalá-Fernández

“…The predictive machine learning models incorporating prepubertal genetics, high-density lipoprotein, and sedentary lifestyle achieved reasonable performance in predicting pubertal obesity (AUC, accuracy, and sensitivity of 0.89). …”
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Antigen-presenting cancer associated fibroblasts enhance antitumor immunity and predict immunotherapy response by Junquan Song, Rongyuan Wei, Chenchen Liu, Zhenxiong Zhao, Xuanjun Liu, Yanong Wang, Fenglin Liu, Xiaowen Liu

“…This study advances the understanding of CAFs heterogeneity in GC and highlights apCAFs as a potential biomarker for predicting immunotherapy response in pan-cancer.…”
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Maize Kernel Broken Rate Prediction Using Machine Vision and Machine Learning Algorithms by Chenlong Fan, Wenjing Wang, Tao Cui, Ying Liu, Mengmeng Qiao

“…A new dataset of high moisture content maize kernel phenotypic features was constructed by extracting seven features (geometric and shape features). …”
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The relationship between gut and nasopharyngeal microbiome composition can predict the severity of COVID-19 by Benita Martin-Castaño, Patricia Diez-Echave, Jorge García-García, Laura Hidalgo-García, Antonio Jesús Ruiz-Malagon, José Alberto Molina-Tijeras, María Jesús Rodríguez-Sojo, Anaïs Redruello-Romero, Margarita Martínez-Zaldívar, Emilio Mota, Fernando Cobo, Xando Díaz-Villamarin, Marta Alvarez-Estevez, Federico García, Concepción Morales-García, Silvia Merlos, Paula Garcia-Flores, Manuel Colmenero-Ruiz, José Hernández-Quero, Maria Nuñez, Maria Elena Rodriguez-Cabezas, Angel Carazo, Javier Martin, Rocio Moron, Alba Rodríguez Nogales, Julio Galvez

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Multi-task genomic prediction using gated residual variable selection neural networks by Yuhua Fan, Patrik Waldmann

“…Using genomic and pedigree information, GRVSNN achieves a lower mean squared error (MSE), and higher Pearson (r) and distance (dCor) correlation between predicted and true phenotypic values in the test data. …”
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Genomic prediction in Persian walnut: Optimization levers according to genetic architecture of complex traits by Anthony Bernard, Juliette Bénéjam, Morgane Roth, Fabrice Lheureux, Elisabeth Dirlewanger

“…A core‐collection of 170 accessions was phenotyped for 25 traits over 1 or 2 years. Highly heritable traits, such as budbreak date and female flowering date, were predicted with high accuracy (∼0.75) using ridge regression best linear unbiased prediction (rrBLUP). …”
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Prognostic prediction for inflammatory breast cancer patients using random survival forest modeling by Yiwei Jia, Chaofan Li, Cong Feng, Shiyu Sun, Yifan Cai, Peizhuo Yao, Xinyu Wei, Zeyao Feng, Yanbin Liu, Wei Lv, Huizi Wu, Fei Wu, Lu Zhang, Shuqun Zhang, Xingcong Ma

“…Background: Inflammatory breast cancer (IBC) is an aggressive and rare phenotype of breast cancer, which has a poor prognosis. …”
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Genome-Wide Association Analysis and Candidate Gene Prediction of Wheat Grain Copper Concentration by Zhaojun Zou, Xiaofei Liu, Fengfeng Li, Jinna Hou, Zhengfu Zhou, Xiaojing Jing, Yanchun Peng, Jianguo Man, Zhensheng Lei

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Deep Multiple Instance Learning Model to Predict Outcome of Pancreatic Cancer Following Surgery by Caroline Truntzer, Dina Ouahbi, Titouan Huppé, David Rageot, Alis Ilie, Chloe Molimard, Françoise Beltjens, Anthony Bergeron, Angelique Vienot, Christophe Borg, Franck Monnien, Frédéric Bibeau, Valentin Derangère, François Ghiringhelli

“…Transcriptomic and genomic features revealed that the poor prognosis group was associated with a squamous phenotype. <b>Conclusions</b>: Our study demonstrates that deep learning could be used to predict PDAC prognosis and offer assistance in better choosing adjuvant treatment.…”
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Studying the role of GCLC gene polymorphisms in predicting the clinical course of acute alcoholic pancreatitis by T. A. Samgina

“…The established genotype – phenotype associations will make it possible to predict the clinical course of AAP in a particular patient, taking into account their genetic makeup, as well as to determine the treatment strategy in a timely manner.…”
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A Combinatorial Functional Precision Medicine Platform for Rapid Therapeutic Response Prediction in AML by Noor Rashidha Binte Meera Sahib, Jameelah Sheik Mohamed, Masturah Bte Mohd Abdul Rashid, Jayalakshmi, Yihao Clement Lin, Yen Lin Chee, Bingwen Eugene Fan, Sanjay De Mel, Melissa Gaik Ming Ooi, Wei‐Ying Jen, Edward Kai‐Hua Chow

“…Methods We have evaluated the clinical applicability of quadratic phenotypic optimization platform (QPOP), to predict clinical response to combination therapies in AML and reveal patient‐centric insights into combination therapy sensitivities. …”
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QTN mapping, gene prediction, and simulation breeding of four-seed pod numbers in soybean by Ming Yuan, Xu Sun, Zhiyuan Yu, Haoyue Sun, Sheng Dong, Jie Zhang, Bo Hu, Wen-Xia Li, Hailong Ning, Wencheng Lu

“…Single-nucleotide polymorphism (SNP) genotype data were obtained based on the Axiom_SoyaSNP 180K chip, and a genome-wide association study (GWAS) was used to locate QTNs, which were used to predict candidate genes and simulate breeding. The results showed that there was genetic variation in NFSP and different gene expression in various environments. …”
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Assessment of compartment-specific CD103-positive cells for prognosis prediction of colorectal cancer by Anpei Huang, Yuanhui Wu, Ji Cui, Muyan Cai, Yumo Xie, Jialin Zou, Maram Alenzi, Hui Yang, Pinzhu Huang, Meijin Huang

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Prediction of Rice Chlorophyll Index (CHI) Using Nighttime Multi-Source Spectral Data by Cong Liu, Lin Wang, Xuetong Fu, Junzhe Zhang, Ran Wang, Xiaofeng Wang, Nan Chai, Longfeng Guan, Qingshan Chen, Zhongchen Zhang

“…This study aimed to explore the feasibility of predicting rice canopy CHI using nighttime multi-source spectral data combined with machine learning models. …”
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Unmasking Nuances Affecting Loneliness: Using Digital Behavioural Markers to Understand Social and Emotional Loneliness in College Students by Malik Muhammad Qirtas, Evi Zafeiridi, Eleanor Bantry White, Dirk Pesch

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Genome−wide association study and genomic prediction of sow resilience based on reproductive traits by L. Shi, W. Hao, Z. Li, H. Chaolu, L. Wang

“…For SDe of ten reproductive traits and their original traits, the phenotypic correlations (|0.01| to |0.90|) and genetic correlations (|0.00| to |0.99|) were observed. …”
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Optimizing Genomic Selection Methods to Improve Prediction Accuracy of Sugarcane Single-Stalk Weight by Zihao Wang, Chengcai Xia, Yanjie Lu, Qi Liu, Meiling Zou, Fenggang Zan, Zhiqiang Xia

“…Initially, we compared the performance of five prediction approaches, including genomic best linear unbiased prediction (GBLUP), single-step genomic best linear unbiased prediction (SSBLUP), Bayes A, machine learning (ML), and deep learning (DL) approaches. …”
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Multi-omic biomarkers associated with multiple sclerosis: from Mendelian randomization to drug prediction by Wei Yang, chenglin Liu, Zhenhua Li, Miao Cui

“…Building on this foundation, we performed Bayesian co-localization analysis of coding genes, followed by a full phenotype-wide association study (PheWAS) on the co-positive genes identified through both analytical methods. …”
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Integrated diagnostics and time series sensitivity assessment for growth monitoring of a medicinal plant (Glycyrrhiza uralensis Fisch.) based on unmanned aerial vehicle multispectr... by Ao Zhang, Haibin Guan, Zhiheng Dong, Xin Jia, Yan Xue, Fengyu Han, Lingjiang Meng, Xiuling Yu, Xiaoqin Wang, Yang Cao

“…PIs collectively achieved high-precision predictions (mean 0.42 ≤ R2 ≤ 0.94), with the prediction of PH using green leaf index (GLI) in BP algorithm attaining peak accuracy (R² = 0.94). …”
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