Giter Site home page Giter Site logo

👋 Hi, I’m @ferdinand-popp

Currently I’m a Master of Science Student Molecular Biology at the University of Heidelberg and the German Cancer Research Center. In this account I forked interesting projects to learn and combine them.

Most projects I did are currently private, until they are published or if they contain sensible patient data. Some of my projects are private due to company affiliation, sorry!

Bioinformatic projects (can be requested):

  • Finding a predictive gene signature for relapse risk in AML Patients with machine learning
  • Cancer patient graphs: Extracting knowledge from multi-omics and clinical datasets using effective graph autoencoders
  • Differential Expression Analysis of RNAseq Data & GSEA
  • Evaluating Oxford Nanopore sequencing for engineered AAV capsid sequencing
  • Pharmaceutical Bioinformatics: Bioactivity Prediction for SMILES on protein

ferdinand-popp's Projects

age icon age

Adapted fromthe KDD 2020 paper "Adaptive Graph Encoder for Attributed Graph Embedding"

bidd icon bidd

Bio info drug discovery

deepan icon deepan

Cluster patient based on their multiomics data utilizing graph autoencoders

emogi-nsclc icon emogi-nsclc

An explainable multi-omics graph integration method based on graph convolutional networks to predict cancer genes.

linear_graph_autoencoders icon linear_graph_autoencoders

Source code from the NeurIPS 2019 workshop article "Keep It Simple: Graph Autoencoders Without Graph Convolutional Networks" (G. Salha, R. Hennequin, M. Vazirgiannis) + k-core framework implementation from IJCAI 2019 article "A Degeneracy Framework for Scalable Graph Autoencoders" (G. Salha, R. Hennequin, V.A. Tran, M. Vazirgiannis)

meds-detection icon meds-detection

Hackathon Q-Summit Result of Team MEDS | Detecting flawed entries in medical health records

mgae icon mgae

Implementation of the CIKM-17 paper “MGAE: Marginalized Graph Autoencoder for Graph Clustering”

mmgl-nsclc icon mmgl-nsclc

Forked: Multi-modal Graph learning for Disease Prediction (IEEE Trans. on Medical imaging, TMI2022)

mogcn icon mogcn

Placeholder for code of the Master Thesis and Poster Presentation ICSB 2022 Berlin

nvidia-sniper icon nvidia-sniper

🎯 Autonomously buy Nvidia Founders Edition GPUs as soon as they become available.

phate icon phate

PHATE (Potential of Heat-diffusion for Affinity-based Transition Embedding) is a tool for visualizing high dimensional data.

pycaret icon pycaret

An open source, low-code machine learning library in Python

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.