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7 Repos

Awesome GitHub RepositoriesGenomic Sequence Interpreters

Deep learning models for processing and interpreting genetic data to identify variants.

Distinct from Sequence Analysis: Distinct from general sequence analysis: focuses on genomic-specific deep learning interpretation.

Explore 7 awesome GitHub repositories matching data & databases · Genomic Sequence Interpreters. Refine with filters or upvote what's useful.

Awesome Genomic Sequence Interpreters GitHub Repositories

Finde die besten Repos mit KI.Wir suchen mit KI nach den am besten passenden Repositories.
  • google-research/google-researchAvatar von google-research

    google-research/google-research

    38,139Auf GitHub ansehen↗

    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

    Applies deep learning to process and interpret genetic data for variant identification.

    Jupyter Notebookaimachine-learningresearch
    Auf GitHub ansehen↗38,139
  • k-dense-ai/claude-scientific-skillsAvatar von K-Dense-AI

    K-Dense-AI/claude-scientific-skills

    8,907Auf GitHub ansehen↗

    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

    Analyzes DNA and protein sequences to annotate genetic variants and identify pathogenicity.

    Pythonai-scientistbioinformaticschemoinformatics
    Auf GitHub ansehen↗8,907
  • deepchem/deepchemAvatar von deepchem

    deepchem/deepchem

    6,545Auf GitHub ansehen↗

    DeepChem is an open-source Python framework for applying deep learning to molecular, chemical, and biological data, serving as a comprehensive toolkit for drug discovery and materials science. At its core, it provides a featurizer-pipeline abstraction that converts raw molecular data into numerical representations, including graph-based molecular structures, SMILES tokenization vocabularies, and disk-sharded dataset persistence for handling large-scale data that exceeds RAM capacity. The framework distinguishes itself through integrated molecular docking workflows that automate pocket detecti

    Featurizes genomic and proteomic sequences from alignment files for downstream machine learning models.

    Pythonbiologydeep-learningdrug-discovery
    Auf GitHub ansehen↗6,545
  • biopython/biopythonAvatar von biopython

    biopython/biopython

    5,078Auf GitHub ansehen↗

    Biopython ist eine Bioinformatik-Bibliothek für Python, die Werkzeuge zum Parsen, Manipulieren und Analysieren biologischer Sequenzen, molekularer Strukturen und phylogenetischer Bäume bereitstellt. Sie dient als Parser für biologische Sequenzen für genomische und proteomische Daten in verschiedenen Industriestandard-Formaten und fungiert als Schnittstelle zum Abfragen biologischer Daten und Zitate aus NCBI Entrez-Repositories. Das Projekt zeichnet sich durch spezialisierte Toolkits für die Analyse von Proteinstrukturen und die Konstruktion phylogenetischer Bäume aus. Es enthält einen Proteinstruktur-Analysator zur Verarbeitung von PDB- und mmCIF-Dateien zur Berechnung der Molekulargeometrie sowie ein Toolkit für phylogenetische Bäume zur Analyse evolutionärer Beziehungen zwischen Arten. Die Bibliothek deckt ein breites Spektrum bioinformatischer Funktionen ab, einschließlich der Analyse genomischer Sequenzen für Transkription und Translation, der Verwaltung von Sequenzalignments und populationsgenetischer Berechnungen. Sie bietet zudem Tools zur strukturellen Analyse für die Manipulation von 3D-Atomkoordinaten sowie Dienstprogramme zur Visualisierung genomischer Merkmale und zur Modellierung biogeografischer Daten. Das System integriert externe Bioinformatik-Binärdateien durch Tool-Wrapping und unterstützt die persistente Speicherung biologischer Datensätze durch SQL-basierte Sequenzspeicherung.

    Isolates non-coding DNA sequences located between genes from a larger genomic sequence.

    Pythonbioinformaticsbiopythondna
    Auf GitHub ansehen↗5,078
  • jwohlwend/boltzAvatar von jwohlwend

    jwohlwend/boltz

    4,038Auf GitHub ansehen↗

    Boltz is a deep learning molecular modeler and biomolecular structure prediction system. It uses neural network architectures to simulate the physical folding and docking of biomolecules, specifically predicting the three-dimensional shapes of protein and ligand complexes. The project functions as a protein-ligand complex predictor and binding affinity predictor, estimating the strength and probability of molecular interactions between ligands and targets. These capabilities are applied to computer aided drug design, including ligand binding affinity prediction and protein-ligand interaction

    Transforms raw protein sequences into high-dimensional feature vectors using evolutionary information from related sequences.

    Python
    Auf GitHub ansehen↗4,038
  • arcinstitute/evo2Avatar von ArcInstitute

    ArcInstitute/evo2

    3,951Auf GitHub ansehen↗

    evo2 ist ein genomisches Large Language Model und Foundation Model, das darauf ausgelegt ist, genetische Informationen über verschiedene Spezies hinweg vorherzusagen, zu generieren und zu analysieren. Es fungiert als Nukleotid-Sequenz-Modellierer und DNA-Sequenz-Generator und nutzt Transformer-basiertes Sequenz-Modeling zur Verarbeitung genomischer Daten. Das System bietet Funktionen zur synthetischen DNA-Generierung, um neue genetische Sequenzen basierend auf biologischen Prompts oder spezies-spezifischen Tags zu erstellen. Es führt zudem Nukleotid-Wahrscheinlichkeitsvorhersagen durch, um genomische Varianten zu bewerten und biologische Eigenschaften innerhalb von DNA-Sequenzen zu analysieren. Das Modell unterstützt die Analyse genomischer Sequenzen durch die Extraktion hochdimensionaler Repräsentationen aus Zwischenschichten. Diese Embeddings ermöglichen spezialisierte Klassifizierungen und weiterführende Analysen genetischer Daten.

    Predicts nucleotide likelihoods across a sequence to score genomic variants or analyze biological properties.

    Jupyter Notebook
    Auf GitHub ansehen↗3,951
  • space-wizards/space-station-14Avatar von space-wizards

    space-wizards/space-station-14

    3,523Auf GitHub ansehen↗

    Space Station 14 is a C# multiplayer game and roleplay simulation framework. It is built upon an Entity-Component-System (ECS) game engine that separates logic into systems and data into components to manage complex entity interactions. The project functions as a grid-based physics simulator with a YAML data-driven prototype system for defining game objects. The project features a specialized 2D sprite rendering engine that maps server-side appearance data to client-side shaders. It implements a networking model with client-side prediction and dirty-flagged state synchronization to reduce inp

    Allows editing base pairs within a plant's genetic sequence via a hex-style interface.

    C#c-sharpgamehacktoberfest
    Auf GitHub ansehen↗3,523
  1. Home
  2. Data & Databases
  3. Data Analysis & Visualization
  4. Analytical Platforms and Engines
  5. Sequence Analysis
  6. Genomic Sequence Interpreters

Unter-Tags erkunden

  • Genome Editing InterfacesUser interfaces and logic for directly modifying base pairs within a genetic sequence. **Distinct from Genomic Sequence Interpreters:** Distinct from Genomic Sequence Interpreters: focuses on the editing/modification of the sequence via an interface rather than interpretation/analysis.
  • Genomic TokenizationProcesses raw nucleotide sequences into discrete tokens for structured genomic analysis. **Distinct from Genomic Sequence Interpreters:** Focuses on the encoding/tokenization process for genomic data, whereas Genomic Sequence Interpreters focus on the analysis of the resulting data.
  • Non-Coding Region ExtractionIdentifying and isolating intergenic or non-coding DNA sequences within a genome. **Distinct from Genomic Sequence Interpreters:** Distinct from Genomic Sequence Interpreters: focuses on the specific task of isolating intergenic regions rather than general variant interpretation.
  • Sequence FeaturizationProcessing and featurizing genomic and proteomic sequences from alignment files for downstream modeling. **Distinct from Genomic Sequence Interpreters:** Distinct from Genomic Sequence Interpreters: focuses on featurizing sequences into numerical representations, not deep learning interpretation.
  • Therapeutic Variant MatchingMatching genetic variants to targeted therapies using pharmacogenomics and cancer genomics data. **Distinct from Genomic Sequence Interpreters:** Distinct from Genomic Sequence Interpreters: focuses on the clinical application of matching variants to specific therapies.