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Predicting the Toxicity of Druggable Proteins to Human Tissues

Hao, Y.; Thangaraj, P.; Tatonetti, N.

2021-12-09 bioinformatics
10.1101/2021.12.07.471637 bioRxiv
Show abstract

Assessing in vivo tissue toxicity of therapeutic targets remains a major challenge in drug development and drug safety research. We developed TissueTox, an algorithm that learns from multi-omic features of a target protein and predicts toxicity in human body systems and tissues. Predicted TissueTox scores accurately differentiate drugs that failed clinical trials from those that succeeded, and, importantly, can be used to identify the tissues where toxic events occurred.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

1
Nature Communications
○ 5641 papers in training set
Top 4%
26.8%
Similar papers in this journal
2
Scientific Reports
○ 3612 papers in training set
Top 12%
6.3%
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3
Cell Genomics
○ 172 papers in training set
Top 0.6%
6.3%
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4
Communications Biology
○ 993 papers in training set
Top 1%
5.6%
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5
Briefings in Bioinformatics
◐ 354 papers in training set
Top 2%
4.4%
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6
Bioinformatics
◐ 1204 papers in training set
Top 5%
4.4%
50% of probability mass above
7
iScience
○ 1154 papers in training set
Top 4%
4.1%
8
Scientific Data
○ 209 papers in training set
Top 0.8%
3.3%
9
Nature Biomedical Engineering
○ 47 papers in training set
Top 0.6%
2.1%
10
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 23%
2.1%
11
Science Advances
● 1243 papers in training set
Top 18%
2.0%
12
eBioMedicine
○ 183 papers in training set
Top 2%
1.9%
13
Genome Biology
○ 637 papers in training set
Top 6%
1.7%
14
PLOS Computational Biology
● 1863 papers in training set
Top 15%
1.5%
15
Molecular Systems Biology
○ 162 papers in training set
Top 2%
1.3%
16
Cell Systems
○ 201 papers in training set
Top 3%
1.1%
17
eLife
● 5828 papers in training set
Top 58%
1.1%
18
Genome Medicine
○ 183 papers in training set
Top 4%
1.1%
19
Nature Machine Intelligence
○ 70 papers in training set
Top 2%
1.1%
20
Nature Biotechnology
○ 172 papers in training set
Top 4%
1.0%
21
npj Systems Biology and Applications
○ 125 papers in training set
Top 2%
1.0%
22
Nucleic Acids Research
◐ 1281 papers in training set
Top 12%
1.0%
23
Patterns
○ 78 papers in training set
Top 2%
1.0%
24
Genomics, Proteomics & Bioinformatics
188 papers in training set
Top 2%
0.9%
25
Nature Methods
○ 385 papers in training set
Top 6%
0.9%
26
Cell Reports Medicine
○ 153 papers in training set
Top 5%
0.9%
27
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 6%
0.9%
28
Nature
○ 645 papers in training set
Top 11%
0.6%
29
Advanced Science
○ 286 papers in training set
Top 11%
0.6%
30
Bioinformatics Advances
◐ 203 papers in training set
Top 5%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.