2018/10/30 by Mei Lin, Lin Mei, Lin, Mei +17
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · Medicine · #Acute Myeloid Leukemia Research #Artificial intelligence #Biology #Cancer Genomics and Diagnostics #Chromosome #Computational biology #Computer science #Cytogenetics #Deep learning #Digital Imaging for Blood Diseases #FOS: Biological sciences #FOS: Computer and information sciences #Gene #Genetics #Internal medicine #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Medicine #Myeloid #Myeloid leukemia #Oncology #Quantitative Methods (q-bio.QM) #cs.LG #q-bio.QM #stat.ML
paper · pdf · doi:10.48550/arxiv.1810.13247
11 pages, 1 table, 1 figure. arXiv admin note: substantial text overlap with arXiv:1801.01019
arxiv created 2018/10/30 · openalex publication_date 2018/10/30 · arxiv updated 2018/11/01 · openalex created_date 2018/11/09 · openalex updated_date 2026/07/28
We explore how Deep Learning (DL) can be utilized to predict prognosis of acute myeloid leukemia (AML). Out of TCGA (The Cancer Genome Atlas) database, 94 AML cases are used in this study. Input data include age, 10 common cytogenetic and 23 most common mutation results; output is the prognosis (diagnosis to death, DTD). In our DL network, autoencoders are stacked to form a hierarchical DL model from which raw data are compressed and organized and high-level features are extracted. The network is written in R language and is designed to predict prognosis of AML for a given case (DTD of more than or less than 730 days). The DL network achieves an excellent accuracy of 83% in predicting prognosis. As a proof-of-concept study, our preliminary results demonstrate a practical application of DL in future practice of prognostic prediction using next-gen sequencing (NGS) data.