Veerabhadran Baladandayuthapani, Ph.D.
Affiliations: | 2005 | Texas A & M University, College Station, TX, United States |
Area:
Statistics, Bioinformatics Biology, OncologyGoogle:
"Veerabhadran Baladandayuthapani"Parents
Sign in to add mentor| Raymond J. Carroll | grad student | 2005 | Texas A & M | |
| (Bayesian methods in bioinformatics.) | ||||
BETA: Related publications
See more...
Publications
|
You can help our author matching system! If you notice any publications incorrectly attributed to this author, please sign in and mark matches as correct or incorrect. |
| Olivares RJ, Rao A, Morris JS, et al. (2023) Integrative Analysis of Multi-modal Correlated Imaging-Genomics Data in Glioblastoma. Ieee International Workshop On Genomic Signal Processing and Statistics : [Proceedings]. Ieee International Workshop On Genomic Signal Processing and Statistics. 2013: 5-8 |
| Desai N, Morris J, Baladandayuthapani V. (2022) NETCELLMATCH: MULTISCALE NETWORK-BASED MATCHING OF CANCER CELL LINES TO PATIENTS USING GRAPHICAL WAVELETS. Chemistry & Biodiversity |
| Yang H, Baladandayuthapani V, Rao AUK, et al. (2020) Quantile Function on Scalar Regression Analysis for Distributional Data. Journal of the American Statistical Association. 115: 90-106 |
| Gates EDH, Lin JS, Weinberg JS, et al. (2020) Imaging-Based Algorithm for the Local Grading of Glioma. Ajnr. American Journal of Neuroradiology |
| Ha MJ, Stingo FC, Baladandayuthapani V. (2020) Bayesian Structure Learning in Multilayered Genomic Networks Journal of the American Statistical Association. 1-14 |
| Zhang Y, Morris JS, Aerry SN, et al. (2019) RADIO-IBAG: RADIOMICS-BASED INTEGRATIVE BAYESIAN ANALYSIS OF MULTIPLATFORM GENOMIC DATA. The Annals of Applied Statistics. 13: 1957-1988 |
| Das P, Peterson CB, Do KA, et al. (2019) NExUS: Bayesian simultaneous network estimation across unequal sample sizes. Bioinformatics (Oxford, England) |
| Maity AK, Bhattacharya A, Mallick BK, et al. (2019) Bayesian Data Integration and Variable Selection for Pan-Cancer Survival Prediction using Protein Expression Data. Biometrics |
| Lee W, Miranda MF, Rausch P, et al. (2019) Bayesian Semiparametric Functional Mixed Models for Serially Correlated Functional Data, with Application to Glaucoma Data. Journal of the American Statistical Association. 114: 495-513 |
| Ni Y, Stingo FC, Ha MJ, et al. (2019) Bayesian Hierarchical Varying-sparsity Regression Models with Application to Cancer Proteogenomics. Journal of the American Statistical Association. 114: 48-60 |