Correlation heatmap of performance scores among different feature selection algorithms: bOcOA, bPSO, bGWO, bFA, bGA, and bWAO. by Doaa Sami Khafaga (21463870)

Density distribution plot of performance metrics for various feature selection algorithms: bOcOA, bPSO, bGWO, bFA, bGA, and bWAO. by Doaa Sami Khafaga (21463870)

Classification baseline performance. by Doaa Sami Khafaga (21463870)

Feature selection results. by Doaa Sami Khafaga (21463870)

ANOVA test result. by Doaa Sami Khafaga (21463870)

Summary of literature review. by Doaa Sami Khafaga (21463870)

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

Experimental observation of gastruloids and shape quantification using LOCO-EFA. by Martijn A. de Jong (17896732)

UK Biobank data wrangling statistics and results. by Simeone Marino (81051)

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

Predicted phosphorylation and glycosylation sites in the GroEL protein from <i>R</i>. <i>typhi</i>. by 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). …”

Data_Sheet_1_Deep Learning-Based Classification of GAD67-Positive Neurons Without the Immunosignal.pdf by 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 by 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. …”