Publications by authors named "Maximillian T Strauss"
Article Synopsis
- Serous borderline tumors (SBT) are ovarian lesions generally associated with a good prognosis, but 10-15% can progress to low-grade serous cancer (LGSC), which is aggressive and resistant to standard chemotherapy.
- The research uses a combination of spatial proteomics and transcriptomics to understand the transition from SBT to LGSC, identifying an intermediary stage with micropapillary features (SBT-MP) and increased MAPK signaling.
- Key findings include the discovery of specific proteins and transcripts linked to tumor invasiveness, alongside a blueprint for future studies on tumorigenesis and potential new treatment approaches for ovarian cancer.
Machine learning and in particular deep learning (DL) are increasingly important in mass spectrometry (MS)-based proteomics. Recent DL models can predict the retention time, ion mobility and fragment intensities of a peptide just from the amino acid sequence with good accuracy. However, DL is a very rapidly developing field with new neural network architectures frequently appearing, which are challenging to incorporate for proteomics researchers.
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