Back

The minimal number of genes needed to identify a tumor

Gil, G.; Gonzalez, A.

2024-07-26 cancer biology
10.1101/2024.07.25.604730 bioRxiv
Show abstract

We demonstrate that the global state of a Gene Regulatory Network [1] may be labeled by a few genes in spite of the fact that there are thousands of genes participating in it. For example, the expression values of only 3 genes are enough to discriminate between a tissue sample coming from a normal lung or a lung adenocarcinoma. We follow a pragmatic procedure, dependent on the sample set, but which is expected to become exact for large enough sets of samples. The proof relies on a scheme for the construction of perfect classification panels of genes [2], inspired by rough set theory [3].

Matching journals

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

1
Physical Review Letters
43 papers in training set
Top 0.1%
23.5%
2
Scientific Reports
3102 papers in training set
Top 5%
10.5%
3
Proceedings of the National Academy of Sciences
2130 papers in training set
Top 13%
5.1%
4
PLOS ONE
4510 papers in training set
Top 30%
5.1%
5
Cancers
200 papers in training set
Top 1.0%
5.1%
6
Physical Review E
95 papers in training set
Top 0.2%
4.5%
50% of probability mass above
7
PLOS Computational Biology
1633 papers in training set
Top 8%
4.0%
8
Royal Society Open Science
193 papers in training set
Top 1%
2.7%
9
Nature Communications
4913 papers in training set
Top 46%
2.2%
10
Journal of The Royal Society Interface
189 papers in training set
Top 2%
2.2%
11
Journal of Mathematical Biology
37 papers in training set
Top 0.1%
2.0%
12
Nature
575 papers in training set
Top 10%
2.0%
13
Communications Biology
886 papers in training set
Top 6%
2.0%
14
The European Physical Journal Plus
13 papers in training set
Top 0.5%
1.5%
15
Cancer Research
116 papers in training set
Top 2%
1.5%
16
Journal of Clinical Medicine
91 papers in training set
Top 4%
1.4%
17
Journal of Computational Biology
37 papers in training set
Top 0.3%
1.4%
18
Bulletin of Mathematical Biology
84 papers in training set
Top 1%
1.3%
19
Physical Review Research
46 papers in training set
Top 0.5%
1.3%
20
Mathematical Biosciences
42 papers in training set
Top 0.8%
1.2%
21
Theoretical Population Biology
47 papers in training set
Top 0.2%
0.9%
22
Science
429 papers in training set
Top 18%
0.9%
23
Cells
232 papers in training set
Top 6%
0.8%
24
Heliyon
146 papers in training set
Top 7%
0.7%
25
Frontiers in Molecular Biosciences
100 papers in training set
Top 6%
0.7%
26
Genetics
225 papers in training set
Top 5%
0.7%
27
Entropy
20 papers in training set
Top 0.6%
0.5%