Descriptive and Statistical Analysis on the Cancer Patients dataset
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Updated
Jan 13, 2024 - Jupyter Notebook
Descriptive and Statistical Analysis on the Cancer Patients dataset
Machine learning for non small cell lung cancer nodal staging from PETCT molecular imaging
Tool for the annotation of PET-CT images in 3D Slicer. Based on MONAILabel. Paper: Multimodal Interactive Lung Lesion Segmentation: A Framework for Annotating PET/CT Images based on Physiological and Anatomical Cues
Identification of Lung Cancer in Smoker Person Using Ensemble Methods Based on Gene Expression Data. Presented in IC2IE and published to IEEE.
EasyNodule is a software made to help clinicinas to classify Lung Cancer. This will help in elaborating a traitement for the patient which will reduce the progress of the cancer which considered the most killer cancer in the world.
Correlation Between IBSI Morphological Features and Manually-Annotated Shape Attributes on Lung Lesions at CT (MIUA 2022)
I explored lung cancer mortality Kaggle dataset with SQL to generate insights.
Plasma cell-free DNA hydroxymethylomes discriminate disease state in EGFR-mutant non-small cell lung cancer.
Oxygen-driven tumorigenesis on the interactome
Scripts Used in Lung Cancer Gene Analysis (LUAD + LUSC)
Companion for the 2023 manuscript in Cancer Epidemiology, Biomarkers & Prevention entitled "Geographic Patterns in U.S. Lung Cancer Mortality and Cigarette Smoking"
Mutation Detection in Lung Cancer Cell Lines using CNNs
This repository contains a Jupyter Notebook for the analysis of Non-Small Cell Lung Cancer (NSCLC) data using various machine learning techniques.
Exploratory Data Analysis of Lung Cancer Risk Factors
Lung Preneoplasia Progression via Pathomics
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