Language model enables end-to-end accurate detection of cancer from cell-free DNA
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View abstract on PubMed
Summary
This summary is machine-generated.We developed the Affordable Cancer Interception and Diagnostics (ACID) model for cancer diagnosis using cell-free DNA (cfDNA) sequencing reads. ACID demonstrates superior performance in detecting cancer and hepatocellular carcinoma, offering an affordable and efficient diagnostic approach.
Area Of Science
- Genomics
- Bioinformatics
- Computational Biology
Background
- Early cancer detection is crucial for improving patient outcomes.
- Current methods for cancer diagnosis from cell-free DNA (cfDNA) can be complex and costly.
- Analyzing raw sequencing reads directly presents a challenge for diagnostic models.
Purpose Of The Study
- To introduce the Affordable Cancer Interception and Diagnostics (ACID) model for cancer diagnosis.
- To evaluate ACID's performance using raw cfDNA sequencing reads.
- To establish an efficient and affordable end-to-end paradigm for cancer detection.
Main Methods
- Formulated ACID as an autoregressive language model.
- Pretrained ACID using concatenated raw sequencing reads and diagnostic labels.
- Benchmarked ACID against three existing methods on whole-genome and bisulfite sequencing datasets.
Main Results
- ACID significantly outperformed the best benchmarked method in overall cancer diagnosis (AUROC, 0.924 vs. 0.853) and hepatocellular carcinoma detection (AUROC, 0.981 vs. 0.917).
- ACID achieved high accuracy using as few as 10,000 reads per sample.
- ACID demonstrated superior performance on bisulfite sequencing datasets compared to benchmarked methods.
Conclusions
- ACID provides an affordable, simple, and efficient end-to-end approach for cancer detection.
- The model effectively utilizes raw cfDNA sequencing reads for diagnosis.
- ACID shows promise for improving accessibility and accuracy in cancer diagnostics.
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