Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases
The identification of interactions between drugs/compounds and their targets is crucial for
the development of new drugs. In vitro screening experiments (ie bioassays) are frequently …
the development of new drugs. In vitro screening experiments (ie bioassays) are frequently …
Machine learning for big data analytics in plants
Rapid advances in high-throughput genomic technology have enabled biology to enter the
era of 'Big Data'(large datasets). The plant science community not only needs to build its …
era of 'Big Data'(large datasets). The plant science community not only needs to build its …
Sequence-based prediction of protein protein interaction using a deep-learning algorithm
Abstract Background Protein-protein interactions (PPIs) are critical for many biological
processes. It is therefore important to develop accurate high-throughput methods for …
processes. It is therefore important to develop accurate high-throughput methods for …
AnOxPePred: using deep learning for the prediction of antioxidative properties of peptides
Dietary antioxidants are an important preservative in food and have been suggested to help
in disease prevention. With consumer demands for less synthetic and safer additives in food …
in disease prevention. With consumer demands for less synthetic and safer additives in food …
Flaws in evaluation schemes for pair-input computational predictions
Y Park, EM Marcotte - Nature methods, 2012 - nature.com
To the Editor: Computational prediction methods that operate on pairs of objects by
considering features of each (hereafter referred to as pair-input methods) have been crucial …
considering features of each (hereafter referred to as pair-input methods) have been crucial …
Highly efficient framework for predicting interactions between proteins
Protein-protein interactions (PPIs) play a central role in many biological processes. Although
a large amount of human PPI data has been generated by high-throughput experimental …
a large amount of human PPI data has been generated by high-throughput experimental …
Computational prediction of protein–protein interaction networks: algorithms and resources
J Zahiri, J Hannon Bozorgmehr… - Current …, 2013 - ingentaconnect.com
Protein interactions play an important role in the discovery of protein functions and pathways
in biological processes. This is especially true in case of the diseases caused by the loss of …
in biological processes. This is especially true in case of the diseases caused by the loss of …
Machine learning and genome annotation: a match meant to be?
By its very nature, genomics produces large, high-dimensional datasets that are well suited
to analysis by machine learning approaches. Here, we explain some key aspects of …
to analysis by machine learning approaches. Here, we explain some key aspects of …
PPI‐Detect: a support vector machine model for sequence‐based prediction of protein–protein interactions
S Romero‐Molina, YB Ruiz‐Blanco… - Journal of …, 2019 - Wiley Online Library
The prediction of peptide–protein or protein–protein interactions (PPI) is a challenging task,
especially if amino acid sequences are the only information available. Machine learning …
especially if amino acid sequences are the only information available. Machine learning …
Towards a detailed atlas of protein–protein interactions
Highlights•We review current resources that organize experimental knowledge of the
interaction space.•We present the latest efforts in the prediction of interactions and the …
interaction space.•We present the latest efforts in the prediction of interactions and the …