> DevoLearn

DevoLearn is a platform that incorporates a computational analysis platform for embryos of different species

Cell Membrane Segmentor

Understanding the cellular architecture is a fundamental problem in various biological studies. C. elegans is widely used as a model organism in these studies because of its unique fate determinations.
In recent years, researchers have worked extensively on C. elegans to excavate the regulations of genes and proteins on cell mobility and communication. Although various algorithms have been proposed to analyze nucleus, cell shape features are not yet well recorded.
This Segmentor Model proposes a method to systematically analyze three-dimensional morphological cellular features.

Try our online tool for Membrane Segmentor (Click Here)
  • Image segmentation
  • Embryogenesis process
  • PyTorch
  • Unsupervised machine learning
  • Easy to use interface
  • Cell tracking
  • Cell metrics
    • Cell Membrane Segmentation of C. Elegans

Cell Nucleus Segmentor

Cell Nucleus Segmentor is pre-trained model in DevoLearn .Cells are segmented based on Semantic segmentation
Several different methods have attempted to capture expression information with high temporal and spatial resolution on a broad scale
Here DevoLearn cell Segmentor segments the cell and extracts the cell position in 3d axis plane of time series data

Try our online tool for Cell Segmentor (Click Here)
  • PyTorch
  • Cell segmentation
  • Centroid Extraction of Cells
  • Large Dataset
  • Interactive interface
  • Cell Tracking (ID)
  • Cell Metrics (ID)

Lineage Population Model

DevoLearn has Pretrained model for tracking cell Lineage for C.elegans.
Tracking the fate of individual cells and their progeny through lineage tracing has been widely used to investigate various biological processes including embryonic development.
An interactive platform for 3D cell tracking that addresses these challenges by taking an incremental approach to deep learning.

Try our online tool for Lineage Population Tool (Click Here)
  • PyTorch
  • Deep Learning
  • Cell Lineage Tracing
  • C. elegans

Hugging Face | DevoWorm

Our Pre-trained DevoLearn tools are hosted in Hugging Face Spaces under DevoWorm organization

HuggingFace Spaces

HuggingFace Models

HuggingFace DataSets

DevoGraph

DevoGraph is a GSoC 2022 project under the administration of INCF and DevoWorm. Our main goal is to provide examples and components that utlize (Temporal/Directed/...) Graph Neural Networks to model the developmental process of C. elegans
DevoGraph stage 1 extracts centroids of cell, volume of cells from time series data and rewrites them in csv file DevoGraph stage 1 extracts centroids of cells using DevoLearn Pre-trained from time series data and rewrites them in csv file.
DevoGraph stage 2 has KNN-based method constructing temporal graphs.Each node represents a cell at a certain frame, and edges at the same frame connect neighbors according to KNN while edges across different frames connect mother and daughter cells

DevoGraph [Click Here]

History of DevoLearn

How DevoLearn started? Who were the founders, take a look at a complete timeline of DevoLearn from 2020-22
  • 2020
    Dr. Bradly and Mayuk deb started this project under INCF(GSOC 2020)
    1st phase of DevoLearn
    Paper [Click Here]
  • 2021
    Dr. Bradly and Mainak Deb upgraded DevoLearn under INCF(GSOC 2021)
    2nd phase of DevoLearn
    Paper [Click Here]
  • 2022
    Dr. Bradly and Jihang li started DevoGraph under INCF(GSOC 2022).
    KNN-based method constructing temporal graphs
    Paper [Click Here]

Interactive Blogs

We have created blogs to show the work which is done during the past Gsoc's.

Founder

Dr. Bradly Alicea
Head Scientist, Orthogonal Research and Education Laboratory; Senior Contributor, OpenWorm Foundation, Mozilla Open Leader, eLife Ambassador

DevoLearnML Team and Advisors

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