Biology subjects

Chuangsuwanich, E.

Publications and source records attributed to Chuangsuwanich, E..

3 recordsLinked to original sources

bioRxiv2023

Label-free tumor cells classification using deep learning and high-content imaging

Many studies have shown that cellular morphology can be used to distinguish spiked-in tumor cells in blood sample background. However, most validation experiments included only homogeneous cell lines and inadequately captured the broad morphological heterogeneity of cancer cells. Further-more, normal, non-blood cells could be erroneously classified as cancer because their morphology differ from blood cells. Here, we constructed a database of microscopic images of organoid-derived cancer and normal cell with diverse morphology and developed a proof-of-concept deep learning model that can distinguish cancer cells from normal cells within an unlabeled microscopy image. In total, more than 75,000 organoid-drived cells from 3 cholangiocarcinoma patients were collected. The model achieved an area under the receiver operating characteristics curve (AUROC) of 0.78 and can generalize to cell images from an unseen patient. These resources serve as a foundation for an automated, robust platform for circulating tumor cell detection.

bioinformatics↗

bioRxiv2019

Uncovering thousands of new HLA antigens and phosphopeptides with deep learning-based sequence-mask-search de novo peptide sequencing framework

Typical analyses of mass spectrometry data only identify amino acid sequences that exist in reference databases. This restricts the possibility of discovering new peptides such as those that contain uncharacterized mutations or originate from unexpected processing of RNAs and proteins. De novo peptide sequencing approaches address this limitation but often suffer from low accuracy and require extensive validation by experts. Here, we develop SMSNet, a deep learning-based hybrid de novo peptide sequencing framework that achieves >95% amino acid accuracy while retaining good identification coverage. Applications of SMSNet on landmark proteomics and peptideomics studies reveal over 10,000 previously uncharacterized HLA antigens and phosphopeptides and in conjunction with database-search methods, expand the coverage of peptide identification by almost 30%. The power to accurately identify new peptides of SMSNet would make it an invaluable tool for any future proteomics and peptidomics studies - especially cancer neoantigen discovery and proteome characterization of non-model organisms.

bioinformatics↗

bioRxiv2018

Universal MHC binding prediction

Immunotherapy is an emerging approach in cancer treatment that activates the host immune system to destroy cancer cells that express unique peptide signatures (neoepitopes). Administrations of cancer-specific neoepitopes in the form of synthetic peptide vaccine have been proven effective in both mouse models and human patients. Because only a tiny fraction of cancer-specific neoepitopes actually elicit immune response, selection of potent, immunogenic neoepitopes remains a challenging step in cancer vaccine development. A basic approach for immunogenicity prediction is based on the premise that effective neoepitope should bind with the Major Histocompatibility Complex (MHC) with high affinity. In this study, we developed MHCSeqNet, an open-source deep learning model, which not only competes favorably with state-of-the-art predictors but also exhibits promising generalization to new MHC class I and class II alleles. MHCSeqNet employed neural network architectures developed for natural language processing to model amino acid sequence representations of MHC allele and epitope peptide as sentences with amino acids as individual words. This consideration allows MHCSeqNet to accept new MHC alleles as well as peptides of any length. The flexibility offered by MHCSeqNet should make it a valuable tool for screening effective neoepitopes in cancer vaccine development.\n\nAvailability and implementationhttps://github.com/cmbcu/MHCSeqNet

bioinformatics↗