RT Journal Article
SR Electronic
T1 Biological Sequence Modeling with Convolutional Kernel Networks
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 217257
DO 10.1101/217257
A1 Chen, Dexiong
A1 Jacob, Laurent
A1 Mairal, Julien
YR 2018
UL http://biorxiv.org/content/early/2018/10/08/217257.abstract
AB The growing number of annotated biological sequences available makes it possible to learn genotype-phenotype relationships from data with increasingly high accuracy. When large quantities of labeled samples are available for training a model, convolutional neural networks can be used to predict the phenotype of unannotated sequences with good accuracy. Unfortunately, their performance with medium- or small-scale datasets is mitigated, which requires inventing new data-efficient approaches. In this paper, we introduce a hybrid approach between convolutional neural networks and kernel methods to model biological sequences. Our method enjoys the ability of convolutional neural networks to learn data representations that are adapted to a specific task, while the kernel point of view yields algorithms that perform significantly better when the amount of training data is small. We illustrate these advantages for transcription factor binding prediction and protein homology detection, and we demonstrate that our model is also simple to interpret, which is crucial for discovering predictive motifs in sequences. The source code is freely available at https://gitlab.inria.fr/dchen/CKN-seq.