Results 81 to 90 of about 333,629 (311)

Selected traits for genomic prediction.

open access: yes, 2021
Selected traits for genomic prediction.
Guillaume P. Ramstein (5515949)   +3 more
core   +1 more source

Enhancing Genome-Enabled Prediction by Bagging Genomic BLUP

open access: yesPLoS ONE, 2014
We examined whether or not the predictive ability of genomic best linear unbiased prediction (GBLUP) could be improved via a resampling method used in machine learning: bootstrap aggregating sampling ("bagging"). In theory, bagging can be useful when the predictor has large variance or when the number of markers is much larger than sample size ...
Gianola D   +4 more
openaire   +4 more sources

Establishment of a humanized patient‐derived xenograft mouse model of high‐grade serous ovarian cancer for preclinical evaluation of combination immunotherapy

open access: yesMolecular Oncology, EarlyView.
We have established a humanized orthotopic patient‐derived xenograft (Hu‐oPDX) mouse model of high‐grade serous ovarian cancer (HGSOC) that recapitulates human tumor–immune interactions. Using combined anti‐PD‐L1/anti‐CD73 immunotherapy, we demonstrate the model's improved biological relevance and enhanced translational value for preclinical ...
Luka Tandaric   +10 more
wiley   +1 more source

Multi-population genomic prediction using a multi-task Bayesian learning model

open access: yes, 2014
Background Genomic prediction in multiple populations can be viewed as a multi-task learning problem where tasks are to derive prediction equations for each population and multi-task learning property can be improved by sharing information across ...
Li, C.   +3 more
core   +1 more source

Hippo pathway at the crossroads of stemness and therapeutic resistance in breast cancer

open access: yesMolecular Oncology, EarlyView.
Dysregulation of the Hippo pathway drives nuclear accumulation of YAP/TAZ, activating stemness‐related transcriptional programs that sustain breast cancer stemness and fuel therapeutic resistance across subtypes, underscoring Hippo signaling as a targetable vulnerability. Figure created and edited with BioRender.com.
Giulia Schiavoni   +11 more
wiley   +1 more source

Differential expression of cancer‐related genes supports prediction of poor response to first‐line treatments in T‐ALL pediatric patients with high minimal residual disease

open access: yesMolecular Oncology, EarlyView.
In the present work, we have identified a transcriptional signature based on the differential expression of six genes (BCL2&MAST4, HSH2D&LAT2, METRN&PITPNM2) that would facilitate the early detection of T‐cell acute lymphoblastic leukemia (T‐ALL) patients prone to a poor treatment response and could be implemented at diagnosis, along with other risk ...
Antonio Lahera   +11 more
wiley   +1 more source

Impact of Genotype Imputation on the Performance of GBLUP and Bayesian Methods for Genomic Prediction

open access: yes, 2014
The aim of this study was to evaluate the impact of genotype imputation on the performance of the GBLUP and Bayesian methods for genomic prediction. A total of 10,309 Holstein bulls were genotyped on the BovineSNP50 BeadChip (50 k).
Li, C.   +3 more
core   +1 more source

CCDC80 suppresses high‐grade serous ovarian cancer migration via negative regulation of B7‐H3

open access: yesMolecular Oncology, EarlyView.
PAX8 is a lineage‐specific master regulator of transcription in high‐grade serous ovarian cancer (HGSC) progression. We show for the first time that PAX8 facilitates proliferation and metastasis by repressing the cell autonomous tumor suppressor CCDC80 and inducing B7‐H3 expression.
Aya Saleh   +12 more
wiley   +1 more source

Multi-omics-data-assisted genomic feature markers preselection improves the accuracy of genomic prediction

open access: yesJournal of Animal Science and Biotechnology, 2020
Background Presently, multi-omics data (e.g., genomics, transcriptomics, proteomics, and metabolomics) are available to improve genomic predictors. Omics data not only offers new data layers for genomic prediction but also provides a bridge between ...
Shaopan Ye, Jiaqi Li, Zhe Zhang
doaj   +1 more source

Impact of Phenotypic Correction Method and Missing Phenotypic Data on Genomic Prediction of Maize Hybrids

open access: yes, 2018
Phenotypic datasets in plant breeding are commonly incomplete due to missing phenotypic information. The best approach for correcting these datasets for a stage-wise genomic prediction (GP) is not unanimous in the scientific community.
Galli, G   +5 more
core   +1 more source

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