Hierarchical clustering to infer a binary tree with <i>K</i> = 4 sampled populations. by Tristan Mary-Huard (3864)

Visualization 1 (without simplex encoding) by Krzysztof Czajkowski (5141315)

Fig 12 - by Nisha Yadav (366131)

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

“…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. …”

Images of mouse popliteal lymph node vascular structure derived using phase-contrast synchrotron micro-computed tomography (µCT) by Mohammad Jafarnejad (6852338)

“…The LN was then surgically excised and placed in a pipette tip before dissolving the tissue with potassium hydroxide. The freeze-dried samples were scanned with high-resolution synchrotron tomography and the radiographs were reconstructed into stack images using a phase-retrieval algorithm. …”

Machine Learning-Ready Dataset for Cytotoxicity Prediction of Metal Oxide Nanoparticles by Soham Savarkar (21811825)

“…</p><p dir="ltr">Encoding: Categorical variables such as surface coating and cell type were grouped into logical classes and label-encoded to enable model compatibility.</p><p dir="ltr"><b>Applications and Model Compatibility:</b></p><p dir="ltr">The dataset is optimized for use in supervised learning workflows and has been tested with algorithms such as:</p><p dir="ltr">Gradient Boosting Machines (GBM),</p><p dir="ltr">Support Vector Machines (SVM-RBF),</p><p dir="ltr">Random Forests, and</p><p dir="ltr">Principal Component Analysis (PCA) for feature reduction.…”