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Yifeng Tao
Yifeng Tao
Quantitative Researcher at Citadel Securities
Verified email at cs.cmu.edu - Homepage
Title
Cited by
Cited by
Year
From genome to phenome: Predicting multiple cancer phenotypes based on somatic genomic alterations via the genomic impact transformer
Y Tao, C Cai, WW Cohen, X Lu
Pacific Symposium on Biocomputing 25, 79-90, 2020
192020
Effective feature representation for clinical text concept extraction
Y Tao, B Godefroy, G Genthial, C Potts
Proceedings of the Clinical Natural Language Processing Workshop, 1-14, 2019
92019
Tumor heterogeneity assessed by sequencing and fluorescence in situ hybridization (FISH) data
H Lei, EM Gertz, AA Schäffer, X Fu, Y Tao, K Heselmeyer-Haddad, ...
bioRxiv, 2020.02.29.970392, 2020
82020
Automatic human-like mining and constructing reliable genetic association database with deep reinforcement learning
H Wang, X Liu, Y Tao, W Ye, Q Jin, WW Cohen, EP Xing
Pacific Symposium on Biocomputing 24, 112-123, 2019
82019
Robust and accurate deconvolution of tumor populations uncovers evolutionary mechanisms of breast cancer metastasis
Y Tao, H Lei, X Fu, AV Lee, J Ma, R Schwartz
Bioinformatics 36 (Supplement_1), i407-i416, 2020
52020
Phylogenies derived from matched transcriptome reveal the evolution of cell populations and temporal order of perturbed pathways in breast cancer brain metastases
Y Tao, H Lei, AV Lee, J Ma, R Schwartz
International Symposium on Mathematical and Computational Oncology, 3-28, 2019
42019
Joint clustering of single-cell sequencing and fluorescence in situ hybridization data for reconstructing clonal heterogeneity in cancers
X Fu, H Lei, Y Tao, K Heselmeyer-Haddad, I Torres, M Dean, T Ried, ...
Journal of Computational Biology 28 (11), 1035-1051, 2021
32021
Neural network deconvolution method for resolving pathway-level progression of tumor clonal expression programs with application to breast cancer brain metastases
Y Tao, H Lei, AV Lee, J Ma, R Schwartz
Frontiers in physiology 11, 1055, 2020
32020
Predicting drug sensitivity of cancer cell lines via collaborative filtering with contextual attention
Y Tao, S Ren, MQ Ding, R Schwartz, X Lu
Proceedings of Machine Learning Research 126, 660-684, 2020
32020
Improving personalized prediction of cancer prognoses with clonal evolution models
Y Tao, A Rajaraman, X Cui, Z Cui, J Eaton, H Kim, J Ma, R Schwartz
bioRxiv, 761510, 2019
32019
De novo prediction of cell-drug sensitivities using deep learning-based graph regularized matrix factorization
S Ren, Y Tao, K Yu, Y Xue, R Schwartz, X Lu
Pacific Symposium on Biocomputing 27, 278-289, 2022
22022
Assessing the contribution of tumor mutational phenotypes to cancer progression risk
Y Tao, A Rajaraman, X Cui, Z Cui, H Chen, Y Zhao, J Eaton, H Kim, J Ma, ...
PLoS computational biology 17 (3), e1008777, 2021
12021
Interpretable deep learning for chromatin-informed inference of transcriptional programs driven by somatic alterations across cancers
Y Tao, X Ma, GI Laliotis, AG Zuniga, D Palmer, E Toska, R Schwartz, X Lu, ...
bioRxiv, 2021
12021
Reconstructing tumor clonal lineage trees incorporating single-nucleotide variants, copy number alterations and structural variations
X Fu, H Lei, Y Tao, R Schwartz
Bioinformatics 38 (Supplement_1), i125-i133, 2022
2022
Semi-deconvolution of bulk and single-cell RNA-seq data with application to metastatic progression in breast cancer
H Lei, XA Guo, Y Tao, K Ding, X Fu, S Oesterreich, AV Lee, R Schwartz
Bioinformatics 38 (Supplement_1), i386-i394, 2022
2022
Improved deconvolution of combined bulk and single-cell RNA-sequencing data
H Lei, XA Guo, Y Tao, K Ding, X Fu, S Oesterreich, AV Lee, R Schwartz
Cancer Research 82 (12_Supplement), 5031-5031, 2022
2022
Reconstructing clonal lineage trees incorporating single nucleotide variants (SNVs), copy number alterations (CNAs), and structural variations (SVs)
X Fu, H Lei, Y Tao, R Schwartz
2022
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Articles 1–17