Motivation: Deep neural network architectures such as convolutional and long short-term memory networks have become increasingly popular as machine learning tools during the recent years. The availability of greater computational resources, more data, new algorithms for training deep models and easy to use libraries for implementation and training of neural networks are the drivers of this development. The use of deep learning has been especially successful in image recognition; and the development of tools, applications and code examples are in most cases centered within this field rather than within biology. Results: Here, we aim to further the development of deep learning methods within biology by providing application examples and ready...
In the field of artificial intelligence, a combination of scale in data and model capacity enabled b...
This thesis explores the utility of representation learning for bioinformatics applications. It prop...
Machine learning based predictions of protein–protein interactions (PPIs) could provide valuable ins...
The interest in Deep Learning (DL) has seen an exponential growth in the last ten years, producing a...
Deep learning is playing a vital role in every field which involves data. It has emerged as a strong...
DNA sequences are the basic data type that is processed to perform a generic study of biological dat...
© 2022 The Author(s)Deep learning technologies have been adopted to predict the functions of newly i...
This thesis introduces the application of deep learning in protein analysis. Three research question...
<div><p>We introduce a new representation and feature extraction method for biological sequences. Na...
Gene expression is manifested through the synthesis of proteins within the cell. The Cell Atlas, wit...
The classification of amino acids and their sequence analysis plays a vital role in life sciences an...
Many of the current scientific advances in the life sciences have their origin in the intensive use ...
The classification of amino acids and their sequence analysis plays a vital role in life sciences an...
Composed of amino acid chains that influence how they fold and thus dictating their function and fea...
International audienceThe growing number of annotated biological sequences available makes it possib...
In the field of artificial intelligence, a combination of scale in data and model capacity enabled b...
This thesis explores the utility of representation learning for bioinformatics applications. It prop...
Machine learning based predictions of protein–protein interactions (PPIs) could provide valuable ins...
The interest in Deep Learning (DL) has seen an exponential growth in the last ten years, producing a...
Deep learning is playing a vital role in every field which involves data. It has emerged as a strong...
DNA sequences are the basic data type that is processed to perform a generic study of biological dat...
© 2022 The Author(s)Deep learning technologies have been adopted to predict the functions of newly i...
This thesis introduces the application of deep learning in protein analysis. Three research question...
<div><p>We introduce a new representation and feature extraction method for biological sequences. Na...
Gene expression is manifested through the synthesis of proteins within the cell. The Cell Atlas, wit...
The classification of amino acids and their sequence analysis plays a vital role in life sciences an...
Many of the current scientific advances in the life sciences have their origin in the intensive use ...
The classification of amino acids and their sequence analysis plays a vital role in life sciences an...
Composed of amino acid chains that influence how they fold and thus dictating their function and fea...
International audienceThe growing number of annotated biological sequences available makes it possib...
In the field of artificial intelligence, a combination of scale in data and model capacity enabled b...
This thesis explores the utility of representation learning for bioinformatics applications. It prop...
Machine learning based predictions of protein–protein interactions (PPIs) could provide valuable ins...