Red Blood Cell Distribution Width May Predict Drug-Induced Anemia and Prognosis in Patients Affected by Primary/Secondary Myelofibrosis Treated with Ruxolitinib by Alessandro Laganà, Emilia Scalzulli, Ida Carmosino, Maria L. Bisegna, Maurizio Martelli, Massimo Breccia

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Development of a Cohesive Predictive Model for Substance Use Disorder Rehabilitation Using Passive Digital Biomarkers, Psychological Assessments, and Automated Facial Emotion Recog... by Andrea P Garzón-Partida, Kimberly Magaña-Plascencia, Diana Emilia Martínez-Fernández, Joaquín García-Estrada, Sonia Luquin, David Fernández-Quezada

“…Digital health technologies, including wearables and machine learning, show promise for diagnosis, monitoring, and intervention, from relapse prediction to early detection of comorbidities. With high relapse rates and younger patient cases, these innovations could enhance the treatment outcomes of SUD. …”
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Machine learning on multiple epigenetic features reveals H3K27Ac as a driver of gene expression prediction across patients with glioblastoma. by Yusuke Suita, Hardy Bright, Yuan Pu, Merih Deniz Toruner, Jordan Idehen, Nikos Tapinos, Ritambhara Singh

“…These findings suggest that GSCs share a common distributional pattern of enhancer activity characterized by H3K27Ac, which can be utilized to predict gene expression in GSCs across patients. In summary, while GSCs are known for their transcriptomic and phenotypic heterogeneity, we propose that they share a common epigenetic pattern of enhancer activation that defines their underlying transcriptomic expression pattern. …”
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Integrative radiomics analyses identify universal signature for predicting prognosis and therapeutic vulnerabilities across primary and secondary liver cancers: A multi-cohort stud... by Hongjie Xin, Qianwei Lai, Yanping Liu, Naying Liao, Ying Wang, Bihong Liao, Keyang Zhou, Yuchen Zhou, Yang Bai, Zhihua Chen, Yuanping Zhou

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Frailty Index-laboratory and lymphocyte subset patterns in predicting 28-day mortality among elderly sepsis patients: a multicenter observational cohort study by Dongkai Li, Jiatong Hou, Zhan Shi, Xiao Li, Jiahui Zhang, Guoyu Zhao, Xianli Lei, Yawen Xie, Yuefu Wang, Hao Wang, Zhigang Chang, Na Cui

“…Lymphocyte count trajectories were classified into four phenotypes based on patterns during the first 72 hours. …”
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Serum metabolomic profiling uncovered metabolic shifts in individuals upon moderate-altitude exposure and identified the potentiality of beta-alanine to ameliorate hyperuricemia by Xuanfu Chen, Guoxiang Zou, Zhibo Yang, Xin Qi, Feier Song, Long Peng, Dingchen Wang, Jingyan Zhou, Jiahui Ma, Haiwei He, Yimei Hong, Yu-E Wang, Yanqun Fan, Zhipeng Liu, Xin Li

“…Furthermore, the 10-fold cross-validation random forest classification (RFC) predictive modeling based on selected metabolites and phenotypes achieved an area under receiver operating characteristic (ROC) curve (AUC) value of 0.93 (95 % confidence interval (CI): 0.85–1.00) and 0.79 (95 % CI: 0.59–0.98) for distinguishing individuals with high risk of asymptomatic HU (AHU) in the training dataset and validation dataset, respectively. …”
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SugarViT-Multi-objective regression of UAV images with Vision Transformers and Deep Label Distribution Learning demonstrated on disease severity prediction in sugar beet. by Maurice Günder, Facundo Ramón Ispizua Yamati, Abel Barreto, Anne-Katrin Mahlein, Rafet Sifa, Christian Bauckhage

“…The efficient retrieval of large-scale field imagery combined with machine learning techniques shows success in various tasks like phenotyping, weeding, cropping, and disease control. …”
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Identification and deletion of Tft1, a predicted glycosyltransferase necessary for cell wall β-1,3;1,4-glucan synthesis in Aspergillus fumigatus. by Danial Samar, Joshua B Kieler, J Stacey Klutts

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Liquid biopsy using plasma proteomics in predicting efficacy and tolerance of PD-1/PD-L1 blockades in NSCLC: a prospective exploratory study by Yuan Gao, Fei Qi, Wenhao Zhou, Peng Jiang, Mingming Hu, Ying Wang, Congcong Song, Yi Han, Dongdong Li, Na Qin, Hongmei Zhang, Haitao Luo, Tongmei Zhang, Hongxia Li

“…I-SCORE demonstrated strong predictive power for overall survival (12-month AUC = 0.94), progression-free survival (12-month AUC = 0.75), and treatment response (AUC = 0.62). …”
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Targeted genotyping (90K-SPET) facilitates genome-wide association studies and the prediction of yield-related traits in faba bean (Vicia faba L.) by Antonio Lippolis, Salvador A. Gezan, Jorrit Zuidgeest, Valeria Cafaro, Bert-Jan van Dinter, Geert Elzes, Maria-João Paulo, Luisa M. Trindade

“…Moreover, modeling the SNP effect simultaneously via Bayesian GS showed promising predictive ability (PA) and prediction accuracy (ACC), supporting their potential application in germplasm-improvement programs. …”
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Genome-wide association mapping and genomic prediction analyses reveal the genetic architecture of grain yield and agronomic traits under drought and optimum conditions in maize by Manigben Kulai Amadu, Yoseph Beyene, Vijay Chaikam, Pangirayi B. Tongoona, Eric Y. Danquah, Beatrice E. Ifie, Juan Burgueno, Boddupalli M. Prasanna, Manje Gowda

“…Haplo-pheno analysis identified superior haplotypes for qGY_DS1.1 (S1_216149215) associated with the higher grain yield under drought stress. Genomic prediction revealed moderate to high prediction accuracies under optimum and drought conditions. …”
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Delayed culture conversion predicts poor outcomes for isoniazid mono-resistant TB in Uganda: a retrospective cross-sectional study from 2017– 2022 by Joel Kabugo, Obondo James Sande, Jupiter Marina Kabahita, Joanita Namutebi, Dennis Mujuni, Hellen Rosette Oundo, Daniel Kisakye, Dorothy Nassozi Batte, Moses Joloba, Gerald Mboowa

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Prediction of exacerbation frequency of AECOPD based on next-generation sequencing and its relationship with imbalance of lung and gut microbiota: a protocol of a prospective cohor... by Yinji Xu, Li Deng, Jiali Yan, Huachong Xu, Chunzhen Huang, Yiwen Lv, Qianxin Wu, Xiaoyin Chen

“…At present, it is impossible to predict patients with COPD with frequent acute exacerbation phenotypes. …”
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Text Mining to Understand Disease-Causing Gene Variants by Leena Nezamuldeen, Mohsin Saleet Jafri

“…Furthermore, there are tools that allow for the prediction of the pathogenicity of variants. However, navigating these disparate sources is time-consuming and sometimes complex. …”
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Whole-genome sequencing of a novel Meyerozyma sp. nov, fungi Isolated from Harumanis Mango, Mangifera indica L. in Malaysia by Muhammad Ikhmal Bin Rosali, Siti Munirah Musa, Siti Khadijah Binti Kiram, Jasmine Teoh Wen Yi, Siew Shing Wei, Hajar Fauzan Ahmad

“…However, the limitations of traditional phenotypic assessments using microscopy and biochemical tests have highlighted the need for more comprehensive methods for microbe identification. …”
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Metabolic reprogramming and prognostic modeling in pancreatic cancer: insights from WGCNA by Zhuo Song, Zhijia Sun, Yupeng Di, Xu Liu, Xiaoli Kang, Gang Ren, Yingjie Wang

“…However, few studies have comprehensively examined metabolic features of PC and provided guidance for their treatment.MethodsThis study tried to identify metabolism-associated hub genes based on metabolic phenotypic levels through weighted gene co-expression network analysis, and constructed a risk model for PC, then verified its accuracy and explored the potential mechanisms.ResultsWe screened out five metabolic hub and prognostic genes (DLX3, HMGA2, SPRR1B, MYEOV, and FAM111B) and constructed a novel metabolism-associated gene signature to predict the prognosis of PC. …”
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Development and validation of identification algorithms for five autoimmune diseases using electronic health records: a retrospective cohort study in China by Junting Yang, Junting Yang, Yunxiao Wu, Yunxiao Wu, Jinxin Guo, Jinxin Guo, Xiaoxuan Wang, Xiaoxuan Wang, Xin Gao, Xin Gao, Xin Chen, Xin Chen, Mengdi Zhang, Mengdi Zhang, Jin Yang, Zuojing Liu, Yan Liu, Zhike Liu, Zhike Liu, Siyan Zhan, Siyan Zhan, Siyan Zhan, Siyan Zhan

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Prediction of altered 3'- UTR miRNA-binding sites from RNA-Seq data: the swine leukocyte antigen complex (SLA) as a model region. by Marie-Laure Endale Ahanda, Eric R Fritz, Jordi Estellé, Zhi-Liang Hu, Ole Madsen, Martien A M Groenen, Dario Beraldi, Ronan Kapetanovic, David A Hume, Robert R R Rowland, Joan K Lunney, Claire Rogel-Gaillard, James M Reecy, Elisabetta Giuffra

“…Twenty-four novel SNPs were predicted to affect miRNA-binding sites in 19 genes of the SLA region. …”
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Predicting patients with septic shock and sepsis through analyzing whole-blood expression of NK cell-related hub genes using an advanced machine learning framework by Chao Du, Chao Du, Chao Du, Stephanie C. Tan, Stephanie C. Tan, Heng-Fu Bu, Heng-Fu Bu, Saravanan Subramanian, Saravanan Subramanian, Hua Geng, Hua Geng, Xiao Wang, Xiao Wang, Hehuang Xie, Xiaowei Wu, Tingfa Zhou, Ruijin Liu, Zhen Xu, Bing Liu, Xiao-Di Tan, Xiao-Di Tan, Xiao-Di Tan

“…Transcriptomics data has recently emerged as a valuable resource for disease phenotyping and endotyping, making it a promising tool for predicting disease stages. …”
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Population modeling with machine learning can enhance measures of mental health - Open-data replication by Ty Easley, Ruiqi Chen, Kayla Hannon, Rosie Dutt, Janine Bijsterbosch

“…Efforts to predict trait phenotypes based on functional MRI data from large cohorts have been hampered by low prediction accuracy and/or small effect sizes. …”
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