kdsf.ffk

Family of open time-series foundation models (MOMENT-1 small/base/large, T5-style encoder-only architecture with time series patching) pretrained on the curated Time Series Pile corpus, enabling zero/few-shot forecasting, classification, anomaly detection, imputation, and representation learning across scientific and real-world temporal data, with open weights on HuggingFace and pip-installable `momentfm` package (Auton Lab, CMU & UPenn, MIT License, 850+ stars)

Analyze and visualize Mutation Annotation Format (MAF) files from large scale sequencing studies. This package provides various functions to perform most commonly used analyses in cancer genomics and to create feature rich customizable visualzations with minimal effort.

Open-source deep learning toolbox for bioimage analysis providing a unified, configuration-driven framework for 2D/3D semantic segmentation, instance segmentation, classification, denoising, super-resolution, and self-supervised learning; integrates state-of-the-art architectures including U-Net, Vision Transformers, and ConvNeXt, designed for microscopy and biomedical imaging researchers without extensive coding expertise (MIT License, actively maintained)

Performs unbiased cell type recognition from single-cell RNA sequencing data, by leveraging reference transcriptomic datasets of pure cell types to infer the cell of origin of each single cell independently.

Simple statistical identification of contaminating sequence features in marker-gene or metagenomics data. Works on any kind of feature derived from environmental sequencing data (e.g. ASVs, OTUs, taxonomic groups, MAGs,...). Requires DNA quantitation data or sequenced negative control samples.

A tool for unsupervised clustering and analysis of single cell RNA-Seq data.