Click Sign in through your institution.Shibboleth / Open Athens technology is used to provide single sign-on between your institution’s website and Oxford Academic. This authentication occurs automatically, and it is not possible to sign out of an IP authenticated account.Ĭhoose this option to get remote access when outside your institution. Typically, access is provided across an institutional network to a range of IP addresses. If you are a member of an institution with an active account, you may be able to access content in one of the following ways: Get help with access Institutional accessĪccess to content on Oxford Academic is often provided through institutional subscriptions and purchases. ![]() Therefore, cooperative machine learning could further exploit models that can perform well in conditional essentiality predictions. The major limiting factor of machine learning to predict essential genes conditionality is the unavailability of labeled data for interest conditions that can train a classifier. ![]() However, the topology feature category provided the highest discriminatory power making it more suitable for essentiality prediction. Gene ontology-based feature category outperformed other categories of features majorly due to its high correlation with the genes’ biological functions. Five categories of features, namely, gene sequence, protein sequence, network topology, homology and gene ontology-based features, were generated for Caenorhabditis elegans to perform a comparative analysis of their essentiality prediction capacity. We discussed categories of features and how they contribute to the classification performance of essentiality prediction models. This review examines various methods applied to essential gene prediction task, their strengths, limitations and the factors responsible for effective computational prediction of essential genes. The essentiality status of a gene can change due to a specific condition of the organism. Findings also show that a significant limitation of the machine learning approach is predicting conditionally essential genes. Previous studies revealed the need to discover relevant features that significantly classify essential genes, improve on the generalizability of prediction models across organisms, and construct a robust gold standard as the class label for the train data to enhance prediction. The machine learning approach complements the experimental methods to minimize the resources required for essentiality assays. Essential genes are critical for the growth and survival of any organism.
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