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Back to arcinstitute/evo2

Projects sharing features with Evo2

7 open-source projects similar to arcinstitute/evo2, ranked by shared indexed features. Tags may describe platforms or build tools rather than the same primary purpose. Check each project’s use case, license, and deployment requirements before treating it as a replacement.

  • biopython/biopythonbiopython avatar

    biopython/biopython

    5,078View on GitHub↗

    Biopython is a bioinformatics library for Python providing tools to parse, manipulate, and analyze biological sequences, molecular structures, and phylogenetic trees. It serves as a biological sequence parser for genomic and proteomic data across multiple industry-standard file formats and acts as an interface for querying biological data and citations from NCBI Entrez repositories. The project distinguishes itself through specialized toolkits for protein structure analysis and phylogenetic tree construction. It includes a protein structure analyzer for processing PDB and mmCIF files to calcu

    Pythonbioinformaticsbiopythondna
    View on GitHub↗5,078
  • k-dense-ai/claude-scientific-skillsK-Dense-AI avatar

    K-Dense-AI/claude-scientific-skills

    8,907View on GitHub↗

    This project is a scientific agent framework and workflow orchestrator designed to extend large language models with specialized tools for genomic, chemical, and biological research. It provides a system for planning research hypotheses and executing automated workflows by integrating scientific databases with dynamic code execution. The framework includes a cheminformatics modeling suite for predicting molecular bioactivity and performing virtual screening, alongside a bioinformatics analysis toolkit for processing genomic sequences and single-cell data. It also features an academic document

    Pythonai-scientistbioinformaticschemoinformatics
    View on GitHub↗8,907
  • google-research/google-researchgoogle-research avatar

    google-research/google-research

    38,139View on GitHub↗

    This repository serves as a comprehensive research platform and toolkit for advancing machine learning, quantum computing, and large-scale scientific data analysis. It provides foundational frameworks for developing complex algorithmic systems, offering the necessary infrastructure for distributed training, computational graph execution, and high-performance model development. The project distinguishes itself by integrating specialized research domains with robust, privacy-preserving methodologies. It supports diverse scientific discovery through tools for quantum simulation, physics-informed

    Jupyter Notebookaimachine-learningresearch
    View on GitHub↗38,139

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  • hail-is/hailhail-is avatar

    hail-is/hail

    1,064View on GitHub↗

    Cloud-native genomic dataframes and batch computing

    Python
    View on GitHub↗1,064
  • dnanexus-rnd/glnexusdnanexus-rnd avatar

    dnanexus-rnd/GLnexus

    185View on GitHub↗

    Scalable gVCF merging and joint variant calling for population sequencing projects

    C++
    View on GitHub↗185
  • tingsongyu/pytorch-tutorial-2ndTingsongYu avatar

    TingsongYu/PyTorch-Tutorial-2nd

    4,555View on GitHub↗

    This project is a comprehensive instructional resource and course for building neural networks using PyTorch. It covers the fundamental building blocks of deep learning, including tensor manipulation, automatic differentiation, and the construction of modular neural network components. The repository serves as a technical guide for several specialized domains. It provides implementation details for computer vision tasks such as image classification, object detection, and semantic segmentation, as well as natural language processing workflows involving transformers, recurrent networks, and gen

    Jupyter Notebookcomputer-visiondeepsortdiffusion-models
    View on GitHub↗4,555
  • microsoft/synapsemlmicrosoft avatar

    microsoft/SynapseML

    5,230View on GitHub↗

    SynapseML is an Apache Spark machine learning library designed for building and scaling machine learning workflows and data pipelines across distributed clusters. It serves as a distributed machine learning pipeline framework and a distributed inference engine for executing hardware-accelerated predictions and deep learning tasks on large-scale datasets. The project functions as a cloud AI integration layer, allowing users to apply pretrained artificial intelligence services for text, vision, and speech within distributed pipelines. It also includes a dedicated suite of tools for distributed

    Scalaaiapache-sparkazure
    View on GitHub↗5,230