Early Diagnosis: End-to-End CNN-LSTM Models for Mass Spectrometry Data Classification.

ABSTRACT: Liquid chromatography-mass spectrometry (LC-MS) is a powerful method for cell profiling. The use of LC-MS technology is a tool of choice for cancer research since it provides molecular fingerprints of analyzed tissues. However, the ubiquitous presence of noise, the peaks shift between acquisitions, and the huge amount of information owing to the high dimensionality of the data make rapid and accurate cancer diagnosis a challenging task. Deep learning (DL) models are not only effective classifiers but are also well suited to jointly learn feature representation and classification tasks. This is particularly relevant when applied to raw LC-MS data and hence avoid the need for costly preprocessing and complicated feature selection. In this study, we propose a new end-to-end DL methodology tha

SUBMITTER: Seddiki K 

PROVIDER: S-EPMC10501374 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature