Predictive circulating biomarkers of the response to anti‐PD‐1 immunotherapy in advanced HER2 negative breast cancer by Yuhan Wei, Hewei Ge, Yalong Qi, Cheng Zeng, Xiaoying Sun, Hongnan Mo, Fei Ma

“…Patients were categorized as responders and non‐responders according to 6‐month progression‐free survival (PFS), followed by phenotypic and functional comparations to identify candidate predictive biomarkers. …”
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Plasma phospho-tau217 as a predictive biomarker for Alzheimer’s disease in a large south American cohort by Neetesh Pandey, Zikun Yang, Basilio Cieza, Dolly Reyes-Dumeyer, Min Suk Kang, Rosa Montesinos, Marcio Soto-Añari, Nilton Custodio, Lawrence S. Honig, Giuseppe Tosto

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Comparing statistical learning methods for complex trait prediction from gene expression. by Noah Klimkowski Arango, Fabio Morgante

“…Thus, a comprehensive assessment of methods is needed to fully evaluate the potential of gene expression as a predictor of complex trait phenotypes. Here, we used data from the Drosophila Genetic Reference Panel (DGRP) to compare the ability of several existing statistical learning methods to predict starvation resistance and startle response from gene expression in the two sexes separately. …”
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Environment ensemble models for genomic prediction in common bean (Phaseolus vulgaris L.) by Isabella Chiaravallotti, Owen Pauptit, Valerio Hoyos‐Villegas

“…In certain locations, prediction accuracy was able to overcome narrow‐sense heritability, indicating that genomic selection is more efficient than phenotypic selection in these locations. …”
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Metabolic profiling of glioblastoma and identification of G0S2 as a metabolic target by Jianlei Kang, Yujie Xu, Qitai Zhao, Ying Wang, Zhenyan He, Xin Xu

“…This study systematically explores the relationship between glioma metabolomics, tumor phenotype, and the immune microenvironment.MethodsBulk RNA sequencing data were retrieved from the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA). …”
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Multimodal Feature-Driven Deep Learning for the Prediction of Duck Body Dimensions and Weight by Wenbo Xiao, Qiannan Han, Gang Shu, Guiping Liang, Hongyan Zhang, Song Wang, Zhihao Xu, Weican Wan, Chuang Li, Guitao Jiang, Yi Xiao

“…A Transformer encoder is then utilized to capture long-range dependencies and refine feature interactions, thereby enhancing prediction robustness. The model achieved a mean absolute percentage error (MAPE) of 5.73% and an R² of 0.953 across seven morphometric parameters describing body dimensions, and an MAPE of 10.49% with an R² of 0.952 for body weight, indicating robust and consistent predictive performance across both structural and mass-related phenotypes. …”
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Genomic prediction of bone strength in laying hens using different sources of information by M. Sallam, H. Wall, P.W. Wilson, B. Andersson, M. Schmutz, C. Benavides, M. Checa, E. Sanchez‑Rodriguez, A.B. Rodriguez‑Navarro, A. Kindmark, I.C. Dunn, D‑J. de Koning, M. Johnsson

“…Breeding companies rely on selective cross-breeding of purebred birds to produce commercial hybrids, which farmers raise for table-egg production. Genomic prediction is a potential tool to improve bone quality in laying hens. …”
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Patient-derived colorectal microtumors predict response to anti-PD-1 therapy by Duy T. Nguyen, Matthew A. Schaller, Krista P. Terracina, Xia Xu, Diego I. Pedro, Alfonso Pepe, Juan M. Urueña, Zadia Dupee, Nickolas Diodati, Ryan A. Smolchek, Jack E. Famiglietti, Nhi Tran Yen Nguyen, Gerik W. Tushoski-Alemán, Kuoyuan Cheng, Lan Chen, Doug Linn, Vania Vidimar, Aquila Fatima, Soon Woo Kwon, Dongyu Sun, Hongmin Chen, Haiyan Xu, Brian Long, Lily Y. Moy, Bonnie J. Howell, George H. Addona, W. Gregory Sawyer

“…Extensive research has focused on identifying predictive biomarkers to understand treatment response in CRC. …”
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A new hERG allosteric modulator rescues genetic and drug‐induced long‐QT syndrome phenotypes in cardiomyocytes from isogenic pairs of patient induced pluripotent stem cells by Luca Sala, Zhiyi Yu, Dorien Ward‐van Oostwaard, Jacobus PD van Veldhoven, Alessandra Moretti, Karl‐Ludwig Laugwitz, Christine L Mummery, Adriaan P IJzerman, Milena Bellin

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mindLAMPVis as a Co-Designed Clinician-Facing Data Visualization Portal to Integrate Clinical Observations From Digital Phenotyping in Schizophrenia: User-Centered Design Process a... by Karthik Sama, Jaya Sreevalsan-Nair, Soumya Choudhary, Srilakshmi Nagendra, Preethi V Reddy, Asher Cohen, Urvakhsh Meherwan Mehta, John Torous

“…Through case examples focusing on relapse risk prediction in schizophrenia, mindLAMPVis is used to identify different visualization methods to compare different analytical results for each patient. …”
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Incorporating Differential Gene Expression Analysis with Predictive Biomarkers to Identify Novel Therapeutic Drugs for Fuchs Endothelial Corneal Dystrophy by Huaming Wen, Ryan A. Gallo, Xiaosheng Huang, Jiamin Cai, Shaoyi Mei, Ammad Ahmad Farooqi, Jun Zhao, Wensi Tao

“…Based on the differential gene expression analysis for predictive biomarkers, we evaluated the efficacy of LINCS perturbagen prediction software to identify novel therapeutic targets that can revert the pathogenic gene expression signatures and reverse disease phenotypes in FECD. …”
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Accuracy of predicting the genetic risk of disease using a genome-wide approach. by Hans D Daetwyler, Beatriz Villanueva, John A Woolliams

“…We show that the prediction equations are special cases of the more general problem of predicting the accuracy of estimates of genetic values of a continuous phenotype. …”
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Applied pharmacogenetics to predict response to treatment of first psychotic episode: study protocol by Sergi Mas, Sergi Mas, Sergi Mas, Laura Julià, Manuel J. Cuesta, Manuel J. Cuesta, Manuel J. Cuesta, Benedicto Crespo-Facorro, Benedicto Crespo-Facorro, Benedicto Crespo-Facorro, Benedicto Crespo-Facorro, Javier Vázquez-Bourgon, Javier Vázquez-Bourgon, Javier Vázquez-Bourgon, Carlos Spuch, Carlos Spuch, Ana Gonzalez-Pinto, Ana Gonzalez-Pinto, Angela Ibañez, Angela Ibañez, Judith Usall, Judith Usall, Cristina Romero-López-Alberca, Cristina Romero-López-Alberca, Ana Catalan, Ana Catalan, Ana Catalan, Ana Catalan, Ana Catalan, Anna Mané, Anna Mané, Anna Mané, Anna Mané, Miquel Bernardo, Miquel Bernardo, Miquel Bernardo, Miquel Bernardo

“…To do this, the FarmaPRED-PEP project will use data from two already recruited cohorts: the PEPS cohort from the “Genotype-Phenotype Interaction and Environment. Application to a Predictive Model in First Psychotic Episodes” study (the PEPs study from the Spanish abbreviation) (N=335) and the PAFIP cohort from “Clinical Program on Early Phases of Psychosis” (PAFIP from the Spanish abbreviation) (N = 350). …”
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Genomic Prediction in a Self-Fertilized Progenies of <i>Eucalyptus</i> spp. by Guilherme Ferreira Melchert, Filipe Manoel Ferreira, Fabiana Rezende Muniz, Jose Wilacildo de Matos, Thiago Romanos Benatti, Itaraju Junior Baracuhy Brum, Leandro de Siqueira, Evandro Vagner Tambarussi

“…Our objective was to predict the genomic breeding values (GEBVs) of individuals lacking phenotypic information, with a particular focus on inbred line development. …”
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DEVELOPMENT OF THE SOCIAL TENSION RISK PREDICTING ALGORITHM IN THE POPULATION OF CERTAIN REGIONS OF RUSSIA by A. B. Mulik, B. I. Kochurov, V. N. Bodrova, G. V. Antonov, I. V. Ulesikova, N. O. Nazarov, Yu. A. Shatyr

“…The aim of the study was development of approaches to predict the risk of social tension for population of the Russian Federation regions.Methods. …”
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Clinical relevance of the somatic mutational landscaping in predicting outcome of bladder cancer patients by Taoufik Nedjadi, Hifzur Ansari, Muhammad A. Khan, Naif Sannan, Mubarak Al-Mansour, Jaudah Al-Maghrabi, Ashraf Dallol

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Drug-induced cytotoxicity prediction in muscle cells, an application of the Cell Painting assay. by Roman Lambert, Eva Serrano Candelas, Pablo Aparicio, Aisling Murphy, Rafael Gozalbes, Howard Oliver Fearnhead

“…The use of Cell Painting to detect various phenotypic changes induced by chemicals is emerging as a powerful technique in toxicity prediction. …”
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GAN-based image prediction of maize growth across varieties and developmental stages by Xinyi Wang, Shilong Liu, Zhihao Wang, Zedong Geng, Weikun Li, Chengxiu Wu, Yingjie Xiao, Wanneng Yang, Lingfeng Duan

“…Traditional growth prediction methods mainly focused on phenotypic traits instead of images, which leads to limited visual interpretability. …”
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Allogeneic HLA Humoral Immunogenicity and the Prediction of Donor-Specific HLA Antibody Development by Vadim Jucaud

“…The development of de novo donor-specific HLA antibodies (dnDSAs) following solid organ transplantation is considered a major risk factor for poor long-term allograft outcomes. The prediction of dnDSA development is a boon to transplant recipients, yet the assessment of allo-HLA immunogenicity remains imprecise. …”
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Predicting Response to Neoadjuvant Chemotherapy with PET Imaging Using Convolutional Neural Networks. by Petros-Pavlos Ypsilantis, Musib Siddique, Hyon-Mok Sohn, Andrew Davies, Gary Cook, Vicky Goh, Giovanni Montana

“…Our experimental results, based on a sample of 107 patients with esophageal cancer, provide initial evidence that convolutional neural networks have the potential to extract PET imaging representations that are highly predictive of response to therapy. On this dataset, 3S-CNN achieves an average 80.7% sensitivity and 81.6% specificity in predicting non-responders, and outperforms other competing predictive models.…”
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