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  2. List of protein subcellular localization prediction tools

    en.wikipedia.org/wiki/List_of_protein_sub...

    This list of protein subcellular localisation prediction tools includes software, databases, and web services that are used for protein subcellular localization prediction. Some tools are included that are commonly used to infer location through predicted structural properties, such as signal peptide or transmembrane helices , and these tools ...

  3. Protein–protein interaction prediction - Wikipedia

    en.wikipedia.org/wiki/Proteinprotein...

    Protein–protein interaction prediction is a field combining bioinformatics and structural biology in an attempt to identify and catalog physical interactions between pairs or groups of proteins. Understanding protein–protein interactions is important for the investigation of intracellular signaling pathways, modelling of protein complex ...

  4. Protein aggregation predictors - Wikipedia

    en.wikipedia.org/wiki/Protein_aggregation_predictors

    Amyloidogenicity propensity predictor based on a machine learning approach through recursive feature selection and feed-forward neural networks, taking advantage of newly published sequences with experimental, in vitro, evidence of amyloid formation. sequence - Amyloidogenic regions ArchCandy [37] 2015 Download- BiSMM: Secondary structure-related

  5. Machine learning in bioinformatics - Wikipedia

    en.wikipedia.org/wiki/Machine_learning_in...

    In addition, machine learning has been applied to systems biology problems such as identifying transcription factor binding sites using Markov chain optimization. [2] Genetic algorithms, machine learning techniques which are based on the natural process of evolution, have been used to model genetic networks and regulatory structures. [2]

  6. scikit-learn - Wikipedia

    en.wikipedia.org/wiki/Scikit-learn

    scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. [3] It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific ...

  7. Protein–lipid interaction - Wikipedia

    en.wikipedia.org/wiki/Proteinlipid_interaction

    Protein–lipid interaction is the influence of membrane proteins on the lipid physical state or vice versa.. The questions which are relevant to understanding of the structure and function of the membrane are: 1) Do intrinsic membrane proteins bind tightly to lipids (see annular lipid shell), and what is the nature of the layer of lipids adjacent to the protein?

  8. List of protein secondary structure prediction programs

    en.wikipedia.org/wiki/List_of_protein_secondary...

    List of notable protein secondary structure prediction programs. Name Method description Type Link Initial release RaptorX-SS8

  9. Protein design - Wikipedia

    en.wikipedia.org/wiki/Protein_design

    Protein–protein interactions are involved in most biotic processes. Many of the hardest-to-treat diseases, such as Alzheimer's, many forms of cancer (e.g., TP53), and human immunodeficiency virus infection involve protein–protein interactions. Thus, to treat such diseases, it is desirable to design protein or protein-like therapeutics that ...