The need to understand cell developmental processes has spawned a plethora of computational methods for discovering hierarchies from scRNAseq data. However, existing techniques are based on Euclidean geometry which is not an optimal choice for modeling complex cell trajectories with multiple branches. To overcome this fundamental representation issue we propose Poincaré maps, a method harnessing the power of hyperbolic geometry into the realm of single-cell data analysis.

pomegranate

Fast, flexible and easy to use probabilistic modelling in Python.

poolformer

PoolFormer: MetaFormer is Actually What You Need for Vision

poweru

Power analysis code and data processing code for PowerU workflow

practical-applications-in-r-for-psychologists

Lesson files for Practical Applications in R for Psychologists.

practical-deep-learning-at-scale-with-mlflow

Practical Deep Learning at Scale with MLFlow, published by Packt

practicallombscargle

Source for my paper, Understanding the Lomb-Scargle Periodogram

practicalsessions2020

Repository for tutorial sessions at EEML2020

prediction_performance_survival

Code repository for the manuscript: 'Assessing performance in prediction models with survival outcomes: practical guidance for Cox proportional hazards models' (published in Annals of Internal Medicine)

predictivecodingbackprop

Code for the paper "Predictive Coding Approximates Backprop along Arbitrary Computation Graphs"

prism

An alternative to MCMC for rapid analysis of models

promec-indexer

Index Commet Data into Elasticsearch

promec-watcher

A Watcher whose task is to watch a directory and once a change detected, launch a Comet-indexer job in kubernetes using Job API

promise

PROMISE is a distributed pipeline to detect post-translation modification on Mass Spectrometry data. The pipeline is solely for academic research, non-commercial or educational purposes; for other uses, please contact Weizmann institution.

pronto

A Python frontend to (Open Biomedical) Ontologies.

prospr

ProSPr: Protein Structure Prediction

protein-contaminant-libraries-for-dda-and-dia-proteomics

This project is created by the Hao Lab at the Chemistry Department in George Washington University, Washington, D.C..(Publication pending) This project aims to provide contaminant protein FASTA and spectral libraries that can be universally applied to DDA and DIA proteomics and freely accessible for the proteomics community.

protein-localization

Using Transformer protein embeddings with a linear attention mechanism to make SOTA de-novo predictions for the subcellular location of proteins :microscope:

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