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HIFIS Software Spotlights

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Cover image for BioAutoML - Democratizing Machine Learning in Life Sciences

BioAutoML - Democratizing Machine Learning in Life Sciences

BioAutoML automatically runs an end-to-end ML pipeline that can be effectively employed by non-experts. BioAutoML does not require specialized human assistance, i.e., it only needs a training dataset of biological sequences (FASTA files) to perform an end-to-end ML experiment.

  • Artificial Intelligence
  • Big data
  • BioAutoML
  • + 3
  • Jupyter Notebook
  • Python
7
5
Cover image for CellRank

CellRank

CellRank is a computational framework to study cellular fate decisions based on various types of single-cell genomics data. CellRank scales to large cell numbers, is fully compatible with the scverse ecosystem, and is easy to use.

  • Data analysis
  • fate mapping
  • fate prediction
  • + 7
    14
    21
    Cover image for DL4PuDe

    DL4PuDe

    DL4PuDe is an AI-based framework that automatically detects pushing behavior in crowd videos, leading to a better understanding of pushing dynamics. This knowledge is crucial for better crowd management strategies and better design of public places, making public places more comfortable and safer.

    • Artificial Intelligence
    • Crowd Management
    • Deep Learning
    • + 3
    • Jupyter Notebook
    • Python
    • Cuda
    • + 2
    3
    9
    Cover image for EuLerian Identification of ascending AirStreams - ELIAS 2.0

    EuLerian Identification of ascending AirStreams - ELIAS 2.0

    The ELIAS 2.0 software allows the identification of the footprints of rapidly ascending air streams in the atmosphere at substantially reduced computational costs compared to a conventional trajectory approach.

    • Atmospheric science
    • Earth & Environment
    • Machine learning
    • + 1
    • Python
    • Jupyter Notebook
    2
    9
    Cover image for Heat

    Heat

    Heat is a flexible and seamless open-source software for high performance data analytics and machine learning. It provides highly optimized algorithms and data structures for tensor computations using CPUs, GPUs and distributed cluster systems on top of MPI.

    • CPU
    • Data analysis
    • GPU
    • + 4
    • Python
    • Jupyter Notebook
    • Shell
    • + 1
    11
    17
    Cover image for Julearn

    Julearn

    Julearn aims to provide an easy-to-use but flexible interface to build predictive models with cross-validation (CV) consistent performance estimates. It poses as a solution accessible to domain experts without extensive ML training, enabling them to quickly fit and evaluate ML algorithms.

    • Machine learning
    • Neuroimaging
    • python
    • Python
    6
    8
    Cover image for sdaas

    sdaas

    Simple machine learning tool in Python computing an anomaly score of seismic waveform amplitudes. By using a pre-trained Isolation forest model, the program can be used for identification of outliers in semismic data, assign robustness weights, or check instruments and metadata errors

    • Machine learning
    • outlier detection
    • seismic metadata
    • + 2
    • Python
    1
    1
    Cover image for SeisBench: A toolbox for machine learning in seismology

    SeisBench: A toolbox for machine learning in seismology

    SeisBench is an open-source Python toolbox for machine learning in seismology. It brings together the whole machine learning model lifecycle: datasets and benchmarks, models and training pipelines, and efficient implementations for deploying the models in production.

    • Earth & Environment
    • GPU
    • Helmholtz AI
    • + 4
    • Jupyter Notebook
    • Python
    6
    25
    Cover image for Stable-Baselines3

    Stable-Baselines3

    Stable Baselines3 (SB3) is a set of reliable implementations of reinforcement learning algorithms in PyTorch.

    • Machine learning
    • robotics
    • Python
    • Shell
    • Makefile
    • + 1
    26
    21