Back

Proteomic cellular signatures of kinase inhibitor-induced cardiotoxicity: Mount Sinai DToxS LINCS Center Dataset

Xiong, Y.; Liu, T.; Chen, T.; Hansen, J.; Hu, B.; Chen, Y.; Jayaraman, G.; Schürer, S.; Vidovic, D.; Goldfarb, J.; Sobie, E. A.; Birtwistle, M. R.; Iyengar, R.; Li, H.; Azeloglu, E. U.

2020-02-26 pharmacology and toxicology
10.1101/2020.02.26.966606 bioRxiv
Show abstract

The Drug Toxicity Signature Generation Center (DToxS) at the Icahn School of Medicine at Mount Sinai is one of the centers of the NIH Library of Integrated Network-Based Cellular Signatures (LINCS) program. A key aim of DToxS is to generate both proteomic and transcriptomic signatures that can predict adverse effects, especially cardiotoxicity, of kinase inhibitors approved by the Food and Drug Administration. Towards this goal, high throughput shot-gun proteomics experiments (317 cell line/drug combinations + 64 control lysates) have been conducted at the Center for Advanced Proteomics Research at Rutgers University - New Jersey Medical School. Using computational network analyses, these proteomic data can be integrated with transcriptomic signatures generated in tandem to identify cellular signatures of cardiotoxicity that may predict kinase inhibitor-induced toxicity and possible mitigation. Both raw and processed proteomics data have been carefully screened for quality and made publicly available via the PRIDE database. As such, this broad protein kinase inhibitor-stimulated cardiomyocyte proteomic data and signature set is valuable for the prediction of drug toxicities. Links to: Metadata Tables O_TBL View this table: org.highwire.dtl.DTLVardef@1cda9f8org.highwire.dtl.DTLVardef@1520e05org.highwire.dtl.DTLVardef@16a35borg.highwire.dtl.DTLVardef@3ee6e7org.highwire.dtl.DTLVardef@1a98664_HPS_FORMAT_FIGEXP M_TBL C_TBL

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

1
Journal of Proteome Research
215 papers in training set
Top 0.1%
29.3%
2
PLOS ONE
4510 papers in training set
Top 11%
15.2%
3
Frontiers in Pharmacology
100 papers in training set
Top 0.3%
7.2%
50% of probability mass above
4
Data in Brief
13 papers in training set
Top 0.1%
3.8%
5
ACS Omega
90 papers in training set
Top 0.7%
2.9%
6
Analytical Chemistry
205 papers in training set
Top 1%
1.9%
7
ACS Pharmacology & Translational Science
40 papers in training set
Top 0.3%
1.8%
8
Disease Models & Mechanisms
119 papers in training set
Top 1.0%
1.8%
9
Molecular Biology of the Cell
272 papers in training set
Top 1%
1.6%
10
Biomedicines
66 papers in training set
Top 1%
1.6%
11
PLOS Computational Biology
1633 papers in training set
Top 19%
1.3%
12
Scientific Reports
3102 papers in training set
Top 65%
1.3%
13
Scientific Data
174 papers in training set
Top 1%
1.3%
14
Journal of Translational Medicine
46 papers in training set
Top 2%
1.0%
15
MethodsX
14 papers in training set
Top 0.2%
0.9%
16
Clinical and Translational Science
21 papers in training set
Top 0.7%
0.9%
17
International Journal of Molecular Sciences
453 papers in training set
Top 13%
0.8%
18
BMC Medical Genomics
36 papers in training set
Top 1%
0.8%
19
Nature Communications
4913 papers in training set
Top 60%
0.8%
20
Molecular & Cellular Proteomics
158 papers in training set
Top 2%
0.8%
21
Frontiers in Chemistry
14 papers in training set
Top 0.3%
0.8%
22
Toxicological Sciences
38 papers in training set
Top 0.6%
0.8%
23
Molecules
37 papers in training set
Top 2%
0.8%
24
Database
51 papers in training set
Top 1%
0.5%
25
Bioinformatics
1061 papers in training set
Top 10%
0.5%