Back

Multi-Omics Integration for Identification of Prognostic Molecular Signatures for Survival Stratification in Lung Cancer

2026-03-02 oncology Title + abstract only
View on medRxiv
Show abstract

AO_SCPLOWBSTRACTC_SCPLOWLung cancer is characterized by profound intratumoral and inter-patient heterogeneity, spanning histological subtypes, molecular landscapes, and the tumor microenvironment. While multi-omics integration is essential for capturing this complexity, leveraging these data to explicitly define survival-associated subpopulations remains a significant challenge. In this study, we developed NeuroMDAVIS-FS, an unsupervised deep learning framework designed to stratify lung cancer p...

Predicted journal destinations

1
Nature Communications
483 training papers
Top 0.7% 23.1%
2
Cancers
57 training papers
Top 1% 15.2%
3
Scientific Reports
701 training papers
Top 25% 8.1%
4
PLOS ONE
1737 training papers
Top 74% 6.2%
5
Frontiers in Oncology
34 training papers
Top 3% 6.2%
6
Clinical Cancer Research
22 training papers
Top 1% 6.2%
7
eLife
262 training papers
Top 7% 5.2%
8
BMC Cancer
21 training papers
Top 2% 2.0%
9
British Journal of Cancer
22 training papers
Top 3% 2.0%
10
Cancer Medicine
17 training papers
Top 3% 2.0%
11
iScience
74 training papers
Top 2% 2.0%
12
Proceedings of the National Academy of Sciences
100 training papers
Top 8% 1.6%
13
npj Digital Medicine
85 training papers
Top 16% 1.0%
14
PLOS Computational Biology
141 training papers
Top 11% 1.0%
15
JAMA Network Open
125 training papers
Top 23% 1.0%
16
Cell Reports
25 training papers
Top 2% 0.7%
17
Communications Biology
36 training papers
Top 4% 0.7%
18
Genome Medicine
56 training papers
Top 8% 0.7%
19
Nature Genetics
72 training papers
Top 10% 0.7%
20
Nature
58 training papers
Top 9% 0.7%
21
Communications Medicine
63 training papers
Top 6% 0.7%
22
PNAS Nexus
22 training papers
Top 0.7% 0.7%