Name
Title | ||
|---|---|---|
Interpretable Machine Learning Approach Reveals Developmental Gene Expression Biomarkers for Cancer Patient Outcomes at Early Stages. |
Abstract | ||
|---|---|---|
Understanding the molecular mechanisms underlying early cancer development is still a challenge. To address this, we developed an interpretable, data-driven machine learning approach to identify the gene biomarkers that predict the clinical outcomes of early cancer patients. As a demonstration, we applied this approach into large-scale pan-cancer datasets including TCGA to find out how effective it would be at identifying the developmental gene expression biomarkers across tumor stages for various cancer types. Results confirmed that artificial neural network prediction embedding nonlinear feature selection outperforms other classifiers. Moreover, and more relevant to the goal of machine learning interpretable classifiers, we found that early cancer patient groups clustered by the biomarkers selected have significantly more survival differences than ones by early TNM stages, suggesting that this method identified novel early cancer molecular biomarkers. Furthermore, using lung cancer as a study case, we leveraged the hierarchical architectures of neural network to identify the developmental regulatory networks controlling the expression of early cancer biomarkers, providing mechanistic insights of functional genomics driving the onset of cancer development. Finally, we reported the drugs targeting early cancer biomarkers, revealing potential genomic medicine affecting the early cancer development.
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Year | Venue | Field |
|---|---|---|
2018 | BCB | Feature selection,Computer science,Functional genomics,Biomarker (medicine),Artificial intelligence,Cancer biomarkers,Bioinformatics,Gene regulatory network,Artificial neural network,Cancer,Machine learning,Cancer staging |
DocType | ISBN | Citations |
Conference | 978-1-4503-5794-4 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alisha Kamat | 1 | 0 | 0.34 |
| Ting Jin | 2 | 0 | 0.34 |
| So Yeon Min | 3 | 0 | 0.34 |
| Flaminia Talos | 4 | 0 | 0.68 |
| Jonas S. Almeida | 5 | 0 | 0.68 |
| Daifeng Wang | 6 | 0 | 1.35 |