Super.Complex identifies likely protein complexes within a PPI network using a distributed supervised AutoML method. حسب Meghana Venkata Palukuri (11890672)

Predicted phosphorylation and glycosylation sites in the GroEL protein from <i>R</i>. <i>typhi</i>. حسب Jessica Rauch (3070527)

"…<b>(B)</b> The protein sequence of GroEL was analyzed for the presence of phosphorylation and glycosylation sites employing MPSite and Glycopp prediction algorithms (BPP: Prediction based on Binary Profile Patterns; CPP: Prediction based on Composition Profile Patterns; PPP: Prediction based on PSSM Profile patterns; ASA+PPP: Prediction based on Average Surface Accessibility). …"

Relative azimuth calculations reveal nanoscale cadherin geometry in the desmosome. حسب William F. Dean (19370322)

Data_Sheet_1_Deep Learning-Based Classification of GAD67-Positive Neurons Without the Immunosignal.pdf حسب Kotaro Yamashiro (10502753)

"…To address this question, we stained neurons in layers 2/3 of the primary somatosensory cortex (S1) and the primary motor cortex (M1) of mice and manually labeled the neurons as either cell type using GAD67 immunosignals. We then sought to detect GAD67-positive neurons without GAD67 immunosignals using a custom-made deep learning-based algorithm. …"

Raw LC-MS/MS and RNA-Seq Mitochondria data حسب Stefano Martellucci (16284377)

"…The results were subsequently processed to filter out common contaminants, decoy hits from the reverse database, and protein groups identified by a single peptide. The data were filtered as follows: (a) binary expression of a protein (i.e., protein exclusively identified in either scLRP1+/+ or scLRP1-/-) was only considered relevant if all scLRP1+/+ samples or all scLRP1-/- samples expressed the protein. …"