PMID- 38106027
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231229
DP  - 2023 Dec 8
TI  - Large-Scale Information Retrieval and Correction of Noisy Pharmacogenomic 
      Datasets through Residual Thresholded Deep Matrix Factorization.
LID - 2023.12.07.570723 [pii]
LID - 10.1101/2023.12.07.570723 [doi]
AB  - Pharmacogenomics studies are attracting an increasing amount of interest from 
      researchers in precision medicine. The advances in high-throughput experiments 
      and multiplexed approaches allow the large-scale quantification of drug 
      sensitivities in molecularly characterized cancer cell lines (CCLs), resulting in 
      a number of open drug sensitivity datasets for drug biomarker discovery. However, 
      a significant inconsistency in drug sensitivity values among these datasets has 
      been noted. Such inconsistency indicates the presence of substantial noise, 
      subsequently hindering downstream analyses. To address the noise in drug 
      sensitivity data, we introduce a robust and scalable deep learning framework, 
      Residual Thresholded Deep Matrix Factorization (RT-DMF). This method takes a 
      single drug sensitivity data matrix as its sole input and outputs a corrected and 
      imputed matrix. Deep Matrix Factorization (DMF) excels at uncovering subtle 
      patterns, due to its minimal reliance on data structure assumptions. This 
      attribute significantly boosts DMF's ability to identify complex hidden patterns 
      among nuisance effects in the data, thereby facilitating the detection of signals 
      that are therapeutically relevant. Furthermore, RT-DMF incorporates an iterative 
      residual thresholding (RT) procedure, which plays a crucial role in retaining 
      signals more likely to hold therapeutic importance. Validation using simulated 
      datasets and real pharmacogenomics datasets demonstrates the effectiveness of our 
      approach in correcting noise and imputing missing data in drug sensitivity 
      datasets (open source package available at https://github.com/tomwhoooo/rtdmf).
FAU - Hu, Zhiyue Tom
AU  - Hu ZT
AD  - Division of Biostatistics, University of California Berkeley, Berkeley, 94720, 
      U.S.A.
FAU - Yu, Yaodong
AU  - Yu Y
AD  - Department of Electrical Engineer and Computer Science, University of California 
      Berkeley, Berkeley, 94720, U.S.A.
FAU - Chen, Ruoqiao
AU  - Chen R
AD  - Department of Pharmacology and Toxicology, Michigan State University, 48824, 
      U.S.A.
FAU - Yeh, Shan-Ju
AU  - Yeh SJ
AD  - School of Medicine, National Tsing Hua University, Hsinchu, 300044, Taiwan R.O.C.
FAU - Chen, Bin
AU  - Chen B
AD  - Department of Pharmacology and Toxicology, Michigan State University, 48824, 
      U.S.A.
AD  - Department of Pediatrics and Human Development, Michigan State University, 48824, 
      U.S.A.
FAU - Huang, Haiyan
AU  - Huang H
AD  - Department of Statistics, University of California Berkeley, Berkeley, 94720, 
      U.S.A.
LA  - eng
GR  - R01 GM134307/GM/NIGMS NIH HHS/United States
PT  - Preprint
DEP - 20231208
PL  - United States
TA  - bioRxiv
JT  - bioRxiv : the preprint server for biology
JID - 101680187
PMC - PMC10723412
OTO - NOTNLM
OT  - Deep matrix factorization
OT  - Noisy data
OT  - Open-sourced
OT  - Pharmacogenomics datasets
OT  - drug sensitivity data
EDAT- 2023/12/18 06:41
MHDA- 2023/12/18 06:42
PMCR- 2023/12/15
CRDT- 2023/12/18 04:32
PHST- 2023/12/18 06:41 [pubmed]
PHST- 2023/12/18 06:42 [medline]
PHST- 2023/12/18 04:32 [entrez]
PHST- 2023/12/15 00:00 [pmc-release]
AID - 2023.12.07.570723 [pii]
AID - 10.1101/2023.12.07.570723 [doi]
PST - epublish
SO  - bioRxiv [Preprint]. 2023 Dec 8:2023.12.07.570723. doi: 10.1101/2023.12.07.570723.