Airborne LiDAR data manipulation and visualisation for forestry application
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Updated
Mar 25, 2026 - R
Airborne LiDAR data manipulation and visualisation for forestry application
Machine Learning and Deep Learning Course
Repository containing source code, data, etc necessary to run Gene Set Analysis, Celltyping using MAGMA/Celltyping and Tissue Enrichment Analysis. Some of the formulas have been adapted from: NathanSkene/ALS_Human_EWCE, neurogenomics/EWCE, NathanSkene/MAGMA_Celltyping and jbryois/scRNA_disease github repositories.
🧠️🖥️2️⃣️0️⃣️0️⃣️1️⃣️💾️📜️ The sourceCode:ALS category for AI2001, containing ALS programming language datasets
ALS Functional Rating Scale using K means Clustering (NOTE: This project is in R language, not HTML. Open to View)
R pipeline for identifying a six-gene peripheral blood diagnostic biomarker signature for ALS using WGCNA, consensus machine learning, and multi-cohort transcriptomics.
Data used in the paper "How to get closer to actual forest stand height using GEDI? A case study in central European Scots pine stands", authored by Wojciech Krawczyk and Piotr Wężyk, published in European Journal of Remote Sensing.
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