Artikel mit Open-Access-Mandaten - Xia JiangWeitere Informationen
Nicht verfügbar: 2
The Bayesian network story
R Neapolitan, X Jiang
Mandate: US National Institutes of Health
Defining and Discovering Interactive Causes
X Jiang, R Neapolitan
Advances in Biomedical Informatics, 53-78, 2017
Mandate: US National Institutes of Health
Verfügbar: 40
Inferring causal molecular networks: empirical assessment through a community-based effort
SM Hill, LM Heiser, T Cokelaer, M Unger, NK Nesser, DE Carlin, Y Zhang, ...
Nature methods 13 (4), 310-318, 2016
Mandate: US National Institutes of Health, UK Medical Research Council, Government of …
Learning genetic epistasis using Bayesian network scoring criteria
X Jiang, RE Neapolitan, MM Barmada, S Visweswaran
BMC bioinformatics 12, 1-12, 2011
Mandate: US National Institutes of Health
Using natural language processing and machine learning to identify breast cancer local recurrence
Z Zeng, S Espino, A Roy, X Li, SA Khan, SE Clare, X Jiang, R Neapolitan, ...
BMC bioinformatics 19, 65-74, 2018
Mandate: US National Institutes of Health
An informatics research agenda to support precision medicine: seven key areas
JD Tenenbaum, P Avillach, M Benham-Hutchins, MK Breitenstein, ...
Journal of the American Medical Informatics Association 23 (4), 791-795, 2016
Mandate: US National Institutes of Health
Deep learning and machine learning with grid search to predict later occurrence of breast cancer metastasis using clinical data
X Jiang, C Xu
Journal of clinical medicine 11 (19), 5772, 2022
Mandate: US Department of Defense
A clinical decision support system learned from data to personalize treatment recommendations towards preventing breast cancer metastasis
X Jiang, A Wells, A Brufsky, R Neapolitan
PloS one 14 (3), e0213292, 2019
Mandate: US National Institutes of Health
Identifying genetic interactions in genome‐wide data using Bayesian networks
X Jiang, MM Barmada, S Visweswaran
Genetic epidemiology 34 (6), 575-581, 2010
Mandate: US National Institutes of Health
Discovering causal interactions using Bayesian network scoring and information gain
Z Zeng, X Jiang, R Neapolitan
BMC bioinformatics 17, 1-14, 2016
Mandate: US National Institutes of Health
A novel artificial neural network method for biomedical prediction based on matrix pseudo-inversion
B Cai, X Jiang
Journal of biomedical informatics 48, 114-121, 2014
Mandate: US National Institutes of Health
Systematic discovery of the functional impact of somatic genome alterations in individual tumors through tumor-specific causal inference
C Cai, GF Cooper, KN Lu, X Ma, S Xu, Z Zhao, X Chen, Y Xue, AV Lee, ...
PLoS computational biology 15 (7), e1007088, 2019
Mandate: US Department of Defense, US National Institutes of Health
Pan-cancer analysis of TCGA data reveals notable signaling pathways
R Neapolitan, CM Horvath, X Jiang
BMC cancer 15, 1-12, 2015
Mandate: US National Institutes of Health
A Bayesian method for evaluating and discovering disease loci associations
X Jiang, MM Barmada, GF Cooper, MJ Becich
PloS one 6 (8), e22075, 2011
Mandate: US National Institutes of Health
A fast algorithm for learning epistatic genomic relationships
X Jiang, RE Neapolitan, MM Barmada, S Visweswaran, GF Cooper
AMIA annual Symposium proceedings 2010, 341, 2010
Mandate: US National Institutes of Health
Modeling miRNA-mRNA interactions that cause phenotypic abnormality in breast cancer patients
S Lee, X Jiang
PloS one 12 (8), e0182666, 2017
Mandate: US National Institutes of Health
Computational methods for ubiquitination site prediction using physicochemical properties of protein sequences
B Cai, X Jiang
BMC bioinformatics 17, 1-12, 2016
Mandate: US National Institutes of Health
A new method for predicting patient survivorship using efficient Bayesian network learning
X Jiang, D Xue, A Brufsky, S Khan, R Neapolitan
Cancer informatics 13, CIN. S13053, 2014
Mandate: US National Institutes of Health
Mining pure, strict epistatic interactions from high-dimensional datasets: ameliorating the curse of dimensionality
X Jiang, RE Neapolitan
Public Library of Science 7 (10), e46771, 2012
Mandate: US National Institutes of Health
Empirical study of overfitting in deep learning for predicting breast cancer metastasis
C Xu, P Coen-Pirani, X Jiang
Cancers 15 (7), 1969, 2023
Mandate: US Department of Defense
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