Statistical Machine Intelligence & Learning Engine

Single cell trajectory detection

🔴 MiniSom is a minimalistic implementation of the Self Organizing Maps

Manifold learning for single-cell single-nucleotide genetic variations

This is the code implementation for the GMML algorithm.

CellRank: dynamics from multi-view single-cell data

A computational method to rank and infer drug-responsive cell population towards in-silico drug perturbation using a target-perturbed gene regulatory network (tpGRN) for single-cell transcriptomic data

Companion repository for the paper "Representation Learning via Manifold Flattening and Reconstruction"

Implementation of Low Distortion Local Eigenmaps and several variations of it

Diffusion based distances in torch

A Julia package for manifold learning and nonlinear dimensionality reduction

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

Filling the 3D 'scattering volume' by appropriately-oriented 2D scattering patterns. An analytical model suggests a numerical procedure (using Diffusion Map and the fisrt 9 non-trivial eigenvectors). The Matlab code here 1) synthesizes 2D scattering patterns; 2) Forms the Distance Matrix of mimages; and 3) retrieves the (relative) orientations u…

A Python implementation of Manifold Sculpting

Extended Dynamic Mode Decomposition for system identification from time series data (with dictionary learning, control and streaming options). Diffusion Maps to extract geometric description from data.

A model-based, unsupervised manifold learning method that factors complex cellular trajectories into interpretable bifurcating Gaussian processes of transcription.

topological deep learning

Geometry Regularized Autoencoders (GRAE) for large-scale visualization and manifold learning

Este repositorio contiene la colección de Jupyter Notebooks generados para implementar la metodología descrita en el documento de Tesis "Detección de genomas anómalos de sars-cov-2 por medio de algoritmos de aprendizaje no supervisados".