Perspective: Julia for Biologists

Overview

Perspective: Julia for Biologists

1. Examples

Speed: Example 1 - Single cell data and network inference

  • Domain: Single cell data
  • Methodology: Network inference
  • Feature: Speed for vectorisable code
  • Packages: InformationMeasures.jl

Speed: Example 2 - Dynamical systems and pharmacology

  • Domain: Pharmacology
  • Methodology: Dynamical systems
  • Feature: Speed for non linear system code
  • Packages: DifferentialEquations.jl

Abstraction: Example 1 - Structural bioinformatics

  • Domain: Structural bioinformatics
  • Methodology: Alignments, protein structure
  • Feature: Package composability
  • Packages: BioStructures.jl, BioSequences.jl, Bio3DViewer.jl, MetaGraphs.jl, LightGraphs.jl

Abstraction: Example 2 - Image processing in Julia

Metaprogramming: Example - Biochemical reaction networks

  • Domain: Biochemistry
  • Methodology: Dynamical systems
  • Feature: Metaprogramming
  • Packages: DifferentialEquations.jl, Catalyst.jl, GpABC.jl, Turing.jl

2. Links for learning more about Julia

General resources

Intermediate language features

Switching to Julia

Julia for biologists

Community

Owner
Elisabeth Roesch
Researcher + Theoretical(ly) Biologist @MelbIntGen/@unimelb, Maschinenlehrerin, Bayesian statistics, Developmental biology.
Elisabeth Roesch
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Source code for paper "Deep Superpixel-based Network for Blind Image Quality Assessment"

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An implementation demo of the ICLR 2021 paper Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks in PyTorch.

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PyMatting: A Python Library for Alpha Matting

Given an input image and a hand-drawn trimap (top row), alpha matting estimates the alpha channel of a foreground object which can then be composed onto a different background (bottom row).

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PyTorch implementation of the TTC algorithm

Trust-the-Critics This repository is a PyTorch implementation of the TTC algorithm and the WGAN misalignment experiments presented in Trust the Critic

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Multi-task Learning of Order-Consistent Causal Graphs (NeuRIPs 2021)

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Efficient Lottery Ticket Finding: Less Data is More

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CCNet: Criss-Cross Attention for Semantic Segmentation (TPAMI 2020 & ICCV 2019).

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