Sebastian Raschka
Sebastian Raschka
Assistant Professor of Statistics @ UW-Madison

Software

Listed below is only a very small selection of software. For a complete list, please see my GitHub account at https://github.com/rasbt and the [Code] links provided on the Publications pages.

Machine Learning & Deep Learning

MLxtend

MLxtend (machine learning extensions) is a Python library of useful tools for the day-to-day data science tasks.

Deep Learning Models

Various deep learning models implemented in PyTorch and TensorFlow.

Semi-Adversarial Neural Networks Implementation

Implementation of the SAN architecture and model for imparting gender privacy to face images.

Python Utilities

Mputil

Mputil is a library that provides functions for memory-efficient multi-processing, based Python’s multiprocessing standard library.

Watermark

An IPython magic extension for printing date and time stamps, version numbers, and hardware information to aid reproducible research.

PyBibTex

Utility functions for parsing BibTeX files and creating citation reference lists.

Computational Biology/Bioinformatics

BioPandas

Biopandas is a Python library for Working with molecular structures in pandas DataFrames.

ScreenLamp

ScreenLamp is a Python toolkit that enables the hypothesis-driven, ligand-based screening of large molecule libraries containing millions of compounds as well as the generation of molecular fingerprints for machine learning and data mining applications.

SiteInterlock

A novel approach to pose selection in protein-ligand docking based on graph theory. SiteInterlock is a Python package for selecting near-native protein-ligand docking poses based upon the hypothesis that interfacial rigidification of both the protein and ligand prove to be important characteristics of the native binding mode and are sensitive to the spatial coupling of interactions and bond-rotational degrees of freedom in the interface.

Protein Recognition Index (PRI)

The Protein Recognition Index (PRI) measures the similarity between H-bonding features in a given complex (predicted or designed) and the characteristic H-bond trends from crystallographic complexes based on hydrogen-bond interactions identified by Hbind (software accompanying the paper for rigorously defining intermolecular H-bonds by donor/acceptor chemistry and geometric constraints).