Deep Learning skips the manual step of feature extraction and directly uses the raw data. The specific workflow in Machine Learning requires that relevant features most useful in describing the data for classification or regression purposes are first determined. The design of such a network is inspired by the biological neural network of the human brain, leading to a process of learning that is far more capable than that of the standard Machine Learning algorithms. Deep Learning uses a generic layered structure of algorithms called an artificial neural network. Deep Learning is more generic and more complex than Machine Learning.
Both DL and ML are part of the broad field of Artificial Intelligence. The results of this data-driven inference are sometimes difficult to interpret for human beings.ĭeep Learning (DL) is a subset of Machine Learning (ML). The produced knowledge is data-driven and, in the best case scenario, the delivered knowledge is supported by a causal model and not by just a mere stochastic association.
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We confirm that the wide generic capacities and modularity of deep neural networks allow them to be customized easily to learn the deciphering task of the genetic code efficiently.Īrtificial Intelligence (AI) can be used to try to unravel possible links in yet unexplored domains with the help of computers that are trained on data. A test accuracy of 100% and the unequivocal deciphering of rare codons such as the tryptophan codon or the stop codons require a training dataset of the order of 4–22 millions cumulated pairs of codons with their associated amino acids presented to the neural network over around 7–40 training epochs, depending on the architecture and settings. Deciphering the genetic code by a neural network is no exception. Deep neural networks require huge amount of data to train them. We further investigated the benefit of quantifying and using the unbalanced representations of amino acids within real human proteins for a faster deciphering of rare amino acids codons. We also investigated the effect of a codon embedding layer assessing the semantic similarity between codons on the rate of increase of the training accuracy. We compared different Deep Learning neural network architectures and estimated quantitatively the size of the required human transcriptomic training set to achieve the best possible accuracy in the codon-to-amino-acid mapping. The goal of our study was to check whether a neural network could re-discover, on its own, the mapping links between codons and amino acids and build the complete deciphering dictionary upon presentation of transcripts proteins data training pairs. The genetic code is textbook scientific knowledge that was soundly established without resorting to Artificial Intelligence (AI). 5Biomechanics Section, KU Leuven, Heverlee, Belgium.4Skeletal Biology and Engineering Research Center, KU Leuven, Leuven, Belgium.
3Department of Electrical Engineering and Computer Science, Artificial Intelligence and Deep Learning, Montefiore Institute, Liège University, Liège, Belgium.2Cancer Signaling, GIGA Stem Cells, Liège University, Liège, Belgium.1Biomechanics Research Unit, GIGA in Silico Medicine, Liège University, Liège, Belgium.Marc Joiret 1 * Marine Leclercq 2 Gaspard Lambrechts 3 Francesca Rapino 2 Pierre Close 2 Gilles Louppe 3 Liesbet Geris 1,4,5
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