Deep learning in biomedical data science
P Baldi - Annual review of biomedical data science, 2018 - annualreviews.org
Since the 1980s, deep learning and biomedical data have been coevolving and feeding
each other. The breadth, complexity, and rapidly expanding size of biomedical data have …
each other. The breadth, complexity, and rapidly expanding size of biomedical data have …
Deciphering protein–protein interactions. Part II. Computational methods to predict protein and domain interaction partners
BA Shoemaker, AR Panchenko - PLoS computational biology, 2007 - journals.plos.org
Recent advances in high-throughput experimental methods for the identification of protein
interactions have resulted in a large amount of diverse data that are somewhat incomplete …
interactions have resulted in a large amount of diverse data that are somewhat incomplete …
Protein–protein interaction site prediction through combining local and global features with deep neural networks
Abstract Motivation Protein–protein interactions (PPIs) play important roles in many
biological processes. Conventional biological experiments for identifying PPI sites are costly …
biological processes. Conventional biological experiments for identifying PPI sites are costly …
[LIVRE][B] Deep learning in science
P Baldi - 2021 - books.google.com
This is the first rigorous, self-contained treatment of the theory of deep learning. Starting with
the foundations of the theory and building it up, this is essential reading for any scientists …
the foundations of the theory and building it up, this is essential reading for any scientists …
DeepPPI: boosting prediction of protein–protein interactions with deep neural networks
The complex language of eukaryotic gene expression remains incompletely understood.
Despite the importance suggested by many proteins variants statistically associated with …
Despite the importance suggested by many proteins variants statistically associated with …
Coupled folding and binding with α-helix-forming molecular recognition elements
Many protein− protein and protein− nucleic acid interactions involve coupled folding and
binding of at least one of the partners. Here, we propose a protein structural element or …
binding of at least one of the partners. Here, we propose a protein structural element or …
Protein–protein interaction sites prediction by ensemble random forests with synthetic minority oversampling technique
Motivation The prediction of protein–protein interaction (PPI) sites is a key to mutation
design, catalytic reaction and the reconstruction of PPI networks. It is a challenging task …
design, catalytic reaction and the reconstruction of PPI networks. It is a challenging task …
[LIVRE][B] Optimization based data mining: theory and applications
Optimization techniques have been widely adopted to implement various data mining
algorithms. In addition to well-known Support Vector Machines (SVMs)(which are based on …
algorithms. In addition to well-known Support Vector Machines (SVMs)(which are based on …
Hot regions in protein–protein interactions: the organization and contribution of structurally conserved hot spot residues
Structurally conserved residues at protein–protein interfaces correlate with the experimental
alanine-scanning hot spots. Here, we investigate the organization of these conserved …
alanine-scanning hot spots. Here, we investigate the organization of these conserved …
Prediction‐based fingerprints of protein–protein interactions
The recognition of protein interaction sites is an important intermediate step toward
identification of functionally relevant residues and understanding protein function, facilitating …
identification of functionally relevant residues and understanding protein function, facilitating …