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  2. Machine learning in bioinformatics - Wikipedia

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

    In general, a machine learning system can usually be trained to recognize elements of a certain class given sufficient samples. [30] For example, machine learning methods can be trained to identify specific visual features such as splice sites. [31] Support vector machines have been extensively used in cancer genomic studies. [32]

  3. File : Step-by-step procedure of using T-RFLP analysis in ...

    en.wikipedia.org/wiki/File:Step-by-step...

    Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.2 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts.

  4. Outline of machine learning - Wikipedia

    en.wikipedia.org/wiki/Outline_of_machine_learning

    Machine learning (ML) is a subfield of artificial intelligence within computer science that evolved from the study of pattern recognition and computational learning theory. [1] In 1959, Arthur Samuel defined machine learning as a "field of study that gives computers the ability to learn without being explicitly programmed". [ 2 ]

  5. Multiple instance learning - Wikipedia

    en.wikipedia.org/wiki/Multiple_Instance_Learning

    Depending on the type and variation in training data, machine learning can be roughly categorized into three frameworks: supervised learning, unsupervised learning, and reinforcement learning. Multiple instance learning (MIL) falls under the supervised learning framework, where every training instance has a label, either discrete or real valued ...

  6. Biological computing - Wikipedia

    en.wikipedia.org/wiki/Biological_computing

    A ribosome is a biological machine that uses protein dynamics on nanoscales to translate RNA into proteins. The behavior of biologically derived computational systems such as these relies on the particular molecules that make up the system, which are primarily proteins but may also include DNA molecules.

  7. Autoencoder - Wikipedia

    en.wikipedia.org/wiki/Autoencoder

    An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning).An autoencoder learns two functions: an encoding function that transforms the input data, and a decoding function that recreates the input data from the encoded representation.

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