ENZYMAP: exploiting protein annotation for modeling and predicting EC number changes in UniProt/Swiss-Prot. by Sabrina de Azevedo Silveira, Sabrina de Azevedo Silveira, Raquel Cardoso de Melo-Minardi, Carlos Henrique da Silveira, Marcelo Matos Santoro, Wagner Meira

“…Finally, we compared ENZYMAP and DETECT with respect to their predictions and checked both against the UniProt/Swiss-Prot annotations. …”
Get full text

Exploring the genetic profiles linked to senescence in thyroid tumors: insights on predicting disease progression and immune responses by Baoliang Zhang, Yanping Pang

“…Kaplan-Meier survival curves were plotted, and Receiver Operating Characteristic (ROC) curves rigorously confirmed the accuracy of model predictions.ResultsTo evaluate the predictive power of prognostic models across different phenotypic traits, we performed survival analysis, Gene Set Enrichment Analysis (GSEA), and immune-related differential analysis. …”
Get full text

Preoperative structural networks based on DTI predicts initial subthalamic nucleus stimulation outcome in parkinson’s disease by Xiaoyue Wang, Chunyao Zhou, Fangfang Xie, Xuyang Wang, Xiaoyu Chen, Junmei Zhang, Haofei Wang, Rong Li, Xinghui He, Zhuanyi Yang, Dingyang Liu, Zhiquan Yang

“…Notably, the fusion method of clinical phenotype and network characteristics has better predictive power for postoperative DBS outcome than either the clinical method or the network method. …”
Get full text

Prediction of bladder cancer prognosis and immune microenvironment assessment using machine learning and deep learning models by Weihao Nie, Yiheng Jiang, Luhan Yao, Xinqing Zhu, Abdullah Y. AL-Danakh, Wenlong Liu, Qiwei Chen, Deyong Yang

“…The high-risk group exhibited an immune-inflamed phenotype, associated with poorer prognosis and higher levels of immune cell infiltration. …”
Get full text

Genome-Wide Association Study and Genomic Prediction of Essential Agronomic Traits in Diversity Panel of Soybean Varieties by Qianli Dong, Yuting Cheng, Yiyang Li, Yan Tong, Dazhuang Liu, Jiaxin Yu, Na Zhao, Bao Liu, Xiaoyang Ding, Chunming Xu

“…Finally, we evaluated the genomic prediction performances of four distinct methods across the three environments, revealing the significant influence of environmental factors on predictive accuracies. …”
Get full text

In silico genomic surveillance by CoVerage predicts and characterizes SARS-CoV-2 variants of interest by Katrina Norwood, Zhi-Luo Deng, Susanne Reimering, Gary Robertson, Mohammad-Hadi Foroughmand-Araabi, Sama Goliaei, Martin Hölzer, Frank Klawonn, Alice C. McHardy

“…Since the beginning of the SARS-CoV-2 pandemic, multiple lineages with concerning phenotypic alterations, so-called Variants of Concern (VOCs), have emerged and risen to predominance. …”
Get full text

Using machine learning and single nucleotide polymorphisms for improving rheumatoid arthritis risk Prediction in postmenopausal women. by Yingke Xu, Qing Wu

“…In addition, the XGBoost model that combines genomic data with conventional phenotypic predictors significantly enhanced predictive accuracy, achieving the highest AUC of 0.90 and an F1 score of 0.83. …”
Get full text

New Parameters for Impedance–pH Monitoring in Diagnosing Gastroesophageal Reflux Disease and Predicting its Severity by O. A. Storonova, A. S. Trukhmanov, A. A. Makushina, A. V. Paraskevova, V. T. Ivashkin

Get full text

PIGR predicts good clinical outcomes and plays a tumor suppressor role in the development of breast cancer by Tengfei Jiang, Zhi Sun, Zhi Sun, Ke Dong, Qian Pu, Haidong Gao, Haidong Gao, Yanrong Lv, Peng Li, Guanghui Zhao, Guanghui Zhao

Get full text

Potential prognostic long non-coding RNA identification and their validation in predicting survival of patients with multiple myeloma by Ai-Xin Hu, Zhi-Yong Huang, Lin Zhang, Jian Shen

“…Stratified analysis indicated that prediction of the prognoses with these long non-coding RNAs was independent from other clinical phenotype of multiple myeloma. …”
Get full text

Association mapping and genomic prediction for processing and end‐use quality traits in wheat (Triticum aestivum L.) by Harsimardeep S. Gill, Emily Conley, Charlotte Brault, Linda Dykes, Jochum C. Wiersma, Katherine Frels, James A. Anderson

“…In addition, a putative novel multi‐trait association was identified on chromosome 6AL, and candidate gene analysis revealed eight genes of interest. Further, genomic prediction had a high predictive ability (PA) for mixograph and farinograph traits, with PA up to 0.62 and 0.50 in cross‐validation and forward prediction, respectively. …”
Get full text

Genomic insights into oxalate content in spinach: A genome-wide association study and genomic prediction approach by Haizheng Xiong, Kenani Chiwina, Waltram Ravelombola, Yilin Chen, Ibtisam Alatawi, Qun Luo, Theresa Makawa Phiri, Beiquan Mou, Ainong Shi

“…Furthermore, we employed genomic prediction (GP) via cross-prediction, utilizing five GP models, to assess genomic estimated breeding values (GEBVs) for oxalate content. …”
Get full text

Germline mutation analysis and postoperative recurrence risk prediction in breast cancer patients from western China by Zhujun Deng, Xia Xiao, Biqin Mou, Jing Wang, Qiongxia Hu, Juan Jiang, Kang Xie, Wengeng Zhang, Weimin Li, Bojiang Chen

“…By integrating the SNP status and clinical phenotype, a postoperative recurrence risk prediction model was established. …”
Get full text

Genome-wide association analysis study and genomic prediction for resistance to soybean mosaic virus in soybean population by Tiantian Zhao, Fengmin Wang, Jin Qi, Qiang Chen, Lijuan Zhu, Luping Liu, Long Yan, Yuling Chen, Chunyan Yang, Jun Qin

“…Conclusions Our findings underscore the importance of selecting SNPs closely linked to phenotypic traits to refine prediction accuracy in genomic selection models. …”
Get full text

P2Y12 Inhibitors Refill Gap Predicts Death in Medicare Beneficiaries on Chronic Dialysis by Rafia S. Rasu, Milind A. Phadnis, Christy Xavier, Junqiang Dai, Suzanne L. Hunt, Nishank Jain

Get full text

Comprehensive evaluation of the MeltPro MTB/PZA assay for prediction of pyrazinamide resistance in multidrug-resistant tuberculosis by Guiqing He, Chunxia Tu, Yelei Zhu, Qingyong Zheng, Qiulong Zhou, Wenzhen Zhou, Ouyang Huang, Bin Chen, Zhengwei Liu, Ye Xu, Xiangao Jiang

“…ABSTRACT Resistance to pyrazinamide (PZA) poses significant challenges to tuberculosis (TB) management, and prediction of susceptibility to PZA has been challenging. …”
Get full text

Multi-Trait Genomic Prediction of Meat Yield in Pacific Whiteleg Shrimp (<i>Penaeus vannamei</i>) by Shiwei Zhang, Jie Kong, Jian Tan, Xianhong Meng, Ping Dai, Jiawang Cao, Kun Luo, Mianyu Liu, Qun Xing, Yi Tian, Juan Sui, Sheng Luan

“…The meat yield (MY) is a key economic trait in Pacific whiteleg shrimp (<i>Penaeus vannamei</i>) breeding, necessitating accurate genomic prediction for efficient genetic improvement. In this study, we investigated single-trait (STGMs) and multi-trait genomic models (MTGMs) for predicting MY and related traits, using two cross-validation strategies reflecting different data-availability scenarios. …”
Get full text

Spectral enhancement of PlanetScope using Sentinel-2 images to estimate soybean yield and seed composition by Supria Sarkar, Vasit Sagan, Sourav Bhadra, Felix B. Fritschi

Get full text

Computed tomography-derived quantitative imaging biomarkers enable the prediction of disease manifestations and survival in patients with systemic sclerosis by Gabriela Riemekasten, Felix Nensa, Hanna Grasshoff, René Hosch, Malte Maria Sieren, Lennart Berkel, Jörg Barkhausen, Roman Kloeckner, Franz Wegner

“…This study aims to extract quantitative BC imaging biomarkers from CT scans to assess disease severity, define BC phenotypes, track changes over time and predict survival.Materials and methods CT exams were extracted from a prospectively maintained cohort of 452 SSc patients. 128 patients with at least one CT exam were included. …”
Get full text

Prediction of antibiotic resistance from antibiotic susceptibility testing results from surveillance data using machine learning by Swetha Valavarasu, Yasaswini Sangu, Tanmaya Mahapatra

“…XGBoost consistently outperformed other models, achieving AUC values of 0.96 and 0.95 for the Phenotype-Only and Phenotype + Genotype sets, respectively. …”
Get full text