valentinastoma
Researcher with experience in systems biology and microbiology, interested in solving problems in life sciences and healthcare through data.
Boston
Pinned Repositories
AML-ALL-Cancer-Classification
Exploration of Golub et al classic data set of gene expression for AML and ALL cancer types patients
DESeq2-RNA-seq-Analysis
machine-learning-project-walkthrough
An implementation of a complete machine learning solution in Python on a real-world dataset. This project is meant to demonstrate how all the steps of a machine learning pipeline come together to solve a problem!
National-Parks-in-USA
Nations Parks in the US. In this repository, I scraped publicly available information about national park in the US. All steps were achieved using R. General national park information, including trail and difficulty levels.
NationalHealth
Classification machine learning approach to predict health status based on blood lab results and survey responds from National Health Examination Survey 2007-2008
Single-Cell-RNA-Seq
Single Cell RNA-Seq analysis with Seurat package in R
Statistics-in-R
In this repository, I perform a range of standard statistical techniques in R, including regression, classification, cross validation, and model selection. Questions and learning material supported by "Introduction to Statistical Learning" by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani.
Variant-Calling-Cancer-Genomics
In this repository, I use two bioinformatics tools for somatic variant calling and cancer genomics - DeepVariant and maftools, respectively.
valentinastoma's Repositories
valentinastoma/Single-Cell-RNA-Seq
Single Cell RNA-Seq analysis with Seurat package in R
valentinastoma/AML-ALL-Cancer-Classification
Exploration of Golub et al classic data set of gene expression for AML and ALL cancer types patients
valentinastoma/DESeq2-RNA-seq-Analysis
valentinastoma/machine-learning-project-walkthrough
An implementation of a complete machine learning solution in Python on a real-world dataset. This project is meant to demonstrate how all the steps of a machine learning pipeline come together to solve a problem!
valentinastoma/National-Parks-in-USA
Nations Parks in the US. In this repository, I scraped publicly available information about national park in the US. All steps were achieved using R. General national park information, including trail and difficulty levels.
valentinastoma/NationalHealth
Classification machine learning approach to predict health status based on blood lab results and survey responds from National Health Examination Survey 2007-2008
valentinastoma/Statistics-in-R
In this repository, I perform a range of standard statistical techniques in R, including regression, classification, cross validation, and model selection. Questions and learning material supported by "Introduction to Statistical Learning" by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani.
valentinastoma/Variant-Calling-Cancer-Genomics
In this repository, I use two bioinformatics tools for somatic variant calling and cancer genomics - DeepVariant and maftools, respectively.