Machine Learning in Bioinformatics

By Pedro Larrañaga, Borja Calvo, Roberto Santana, Concha Bielza, Josu Galdiano, Iñaki Inza, José Antonio Lozano, Ruben Armañanzas, G. Santafé, Aritz Pérez Martínez and 1 more. 2008

813 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Signal Processing

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Citations in the corpus

Data mining in the Life Sciences with Random Forest: a walk in the park or lost in the jungle? (2012, background): “The theoretical and practical aspects of many of those algorithms and their application in biology have been discussed elsewhere (for example [6, 22, 23]).”
Predictive modeling of structured electronic health records for adverse drug event detection (2015, background): “In areas where there is a need to analyze large amounts of data, such as bioinformatics, machine learning is a key technique, particularly when analyzing “big data” [16].”
Deep learning in bioinformatics (2016, background): “Previous reviews have addressed machine learning in bioinformatics [6, 20] and the fundamentals of deep learning [7, 8, 21].”
Artificial intelligence in drug discovery and development (2020, neutral): “According to the McKinsey Global Institute, the rapid advances in AI-guided automation will be likely to completely change the work culture of society [5,6].”
PredictSNP: Robust and Accurate Consensus Classifier for Prediction of Disease-Related Mutations (2014, methodology): “To identify the most suitable method for combining the selected tools, we trained seven consensus classifiers on the PredictSNP benchmark dataset using seven machine learning methods, which represent the most important classification principles [55].”
A Review of Ensemble Methods in Bioinformatics (2010, neutral): “Various computational techniques, especially machine learning algorithms [1], are applied, for example, to select genes or proteins associated with the trait of interest and to classify different types of samples in gene expression of microarrays data [2] or mass spectrometry (MS)-based proteomics d”
Data Fusion by Matrix Factorization (2013, methodology): “The gene function prediction task considered in the paper, which has traditionally been regarded as a classiﬁcation problem (Larranaga et al., 2006), is just one example of the types of problems that can be addressed with our method.”
Transfer Learning for Molecular Cancer Classification Using Deep Neural Networks (2019, background): “applications in genomics, proteomics, systems biology, evolution and text mining are reviewed in [13].”