Name: Jann Miko Ingel Rabago
Type: User
Bio: Aspires to be a MD-PhD in Molecular Medicine. Searching for research potentials for drug discovery and development.
Twitter: RabagoMiko
Location: Laoag City, Ilocos Norte, Philippines
Blog: https://jannmikorabago.wordpress.com/
Jann Miko Ingel Rabago's Projects
source-code
A community-maintained repository of cancer clinical knowledge bases and databases focused on cancer variants.
A repository containing all the file projects that I have for the Basic Bioinformatics (Bioinformatics 1) (BIO 4312d) course.
A repository containing all the file projects that I have for the BIO 1311: Introduction to Biology course.
:microscope: Path to a free self-taught education in Bioinformatics!
A repository containing all the file projects that I have for the Bioinformatics 2 (COMP 4312a) course.
A repository containing all the file projects that I have for the Bioinformatics 3 (COMP 4312b) course.
A repository containing all the file projects that I have for the Bioinformatics 4 (COMP 4312c) course.
A repository containing all the file projects that I have for the Bioinformatics 5 (COMP 4312d) course.
A repository containing all the file projects that I have for the Bioinformatics 6 (COMP 4312e) course.
Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening
cBioPortal for Cancer Genomics
The CLAuDE model
Machine Learning model to predict lymph-node metastasis
A Python package for molecular docking with an extensive, highly-curated dataset and a set of realistic benchmark tasks for drug discovery.
My Hello World project.
Molecular Oncology Almanac, a clinical interpretation algorithm paired with a novel underlying knowledge base for precision cancer medicine
Analyses related to Reardon et al. 2020, Clinical interpretation of integrative molecular profiles to guide precision cancer medicine
Cancer metastasis detection with neural conditional random field (NCRF)
Personalized-Medicine-Redefining-Cancer-Treatment is a problem of classifying the given genetic mutations based on the literature available in the medical domain into one of the given 9 classes. In reality, The molecular pathologist searches for evidence in the medical literature that is relevant to the given genetic variations. Finally, this molecular pathologist spends a huge amount of time analyzing the evidence related to each of the variations to classify them. The goal of this case study is to replace the last step with a machine learning model.
Protein-Ligand Interaction Profiler - Analyze and visualize non-covalent protein-ligand interactions in PDB files according to 📝 Salentin et al. (2015), https://www.doi.org/10.1093/nar/gkv315
ResearchKit is an open source software framework that makes it easy to create apps for medical research or for other research projects.
SuperFolder COVID mRNA vaccines, stabilized for in vitro storage and shipping
Files for the Zelda in python tutorial