Ejercicio portafolio: curvas múltiples de amplificación

In this part 2 analysis of the previous analysis of top genes in EBV+NPC from GSE299775, we don't do any more data analysis or machine learning but use our part 1 results to add those 30 genes to our pathology database that we will use to get as many genes and media we research and find from machine learning of various cell types to predict the class of EBV, EBV associated pathology, Multiple Sclerosis, Fibromyalgia, Nasopharyngeal carcinoma, natural killer t-cell lymphoma with EBV, Lyme disease, and mononucleosis. We have yet to add the gastric and biliary duct carcinoma related to EBV or the Burkitt Lymphoma, Hodgkin's Lymphoma, and Large B-cell Lymphoma all associated with EBV.

Hw 5

Exploratory analysis of the U.S. gender wage gap using 23 years of NLSY97 panel data. Covers wage trends over time, gaps by sex, race, occupation, education, and tenure — including a regional breakdown comparing the West to the rest of the U.S. around California's 2018 salary history ban.

Full cleaning pipeline for the NLSY97 panel dataset — 194,545 records across 21 survey rounds. Covers wage filtering, demographic decoding, missing data audit, and feature engineering. Foundation for all EDA and modeling in this project.

Tarea para metabolomica 2026

Penugasan KOMSTAT D Week 2