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5mt003

Project in molecular techniques in life science at the Karolinska Institute. Part of the Master's course in Molecular Techniques in Life Science associated with the SciLifeLab and conducted at the Royal Institute of Technology (KTH), Karolinska Institute & Stockholm University.

ann_weather_prediction

Historical weather data from Basel is used to predict the amount of Short wave radiation on a hourly basis. The data covers 1863 days from 07/2013 to 07/2018. The model is tested on data from 07/2012 to 07/2013.

bb2491

Project in high-throughput analysis of data from molecular biology experiments at KTH Royal Institute of Technology. Part of the Master's course in Molecular Techniques in Life Science associated with the SciLifeLab and conducted at the Royal Institute of Technology (KTH), Karolinska Institute & Stockholm University.

chrombpnet_preprocessing

Create training data for ChromBPNet from fragment files.

clinsim

Generative Adversarial Network (GAN) based clinical data simulator. The adversarial neural network is used to create simulated data from the original data distribution. Optional parameters can be passed to simulate data for a subset of the original data. The purpose is to generate data that has the same properties as original clinical data but no constraints on public sharing, thus providing a means of public reproduction of results, increase data availability and promote data integration.

cy2path

Factorial latent dynamic models trained on Markovian simulations of biological processes using single cell RNA sequencing data.

cy2path_notebooks

cytopath

Simulation based inference of differentiation trajectories from RNA velocity fields.

cytopath-notebooks

Demonstration of cytopath on publicly available single cell RNA sequencing datasets

insilico_mutagenesis

Moduating gene expression via in-silico mutagenesis

integrated_pathway_analysis

Masters thesis project titled 'Personal Pathway Analysis as a means to integrate metabolomic and transcriptomic data in T2D intervention studies.' performed in Lukas Käll's group at Science for Life Lab, Stockholm

ionisation_efficiency_predictor

A convolutional layer based autoencoder coupled with a deep neural network predicts the order of ionisation efficiencies for peptides, from their sequence and, therefore, can assist the quantification of peptides, thus improving its accuracy.

kb8019

Project in comparitive genomics at Stockholm University. Part of the Master's course in Molecular Techniques in Life Science associated with the SciLifeLab and conducted at the Royal Institute of Technology (KTH), Karolinska Institute & Stockholm University.

kb8024

Project in protein topology prediction at Stockholm University. Part of the Master's course in Molecular Techniques in Life Science associated with the SciLifeLab and conducted at the Royal Institute of Technology (KTH), Karolinska Institute & Stockholm University.

scdibs

Dynamical Integration of BatcheS