Machine learning solutions for predicting protein–protein interactions
Proteins are “social molecules.” Recent experimental evidence supports the notion that
large protein aggregates, known as biomolecular condensates, affect structurally and …
large protein aggregates, known as biomolecular condensates, affect structurally and …
Modeling aspects of the language of life through transfer-learning protein sequences
Background Predicting protein function and structure from sequence is one important
challenge for computational biology. For 26 years, most state-of-the-art approaches …
challenge for computational biology. For 26 years, most state-of-the-art approaches …
A survey on algorithms to characterize transcription factor binding sites
Transcription factors (TFs) are key regulatory proteins that control the transcriptional rate of
cells by binding short DNA sequences called transcription factor binding sites (TFBS) or …
cells by binding short DNA sequences called transcription factor binding sites (TFBS) or …
Discriminative embeddings of latent variable models for structured data
Kernel classifiers and regressors designed for structured data, such as sequences, trees
and graphs, have significantly advanced a number of interdisciplinary areas such as …
and graphs, have significantly advanced a number of interdisciplinary areas such as …
Protein embeddings and deep learning predict binding residues for various ligand classes
One important aspect of protein function is the binding of proteins to ligands, including small
molecules, metal ions, and macromolecules such as DNA or RNA. Despite decades of …
molecules, metal ions, and macromolecules such as DNA or RNA. Despite decades of …
Semi-Supervised Learning (Chapelle, O. et al., Eds.; 2006) [Book reviews]
This book addresses some theoretical aspects of semisupervised learning (SSL). The book
is organized as a collection of different contributions of authors who are experts on this topic …
is organized as a collection of different contributions of authors who are experts on this topic …
Predicting protein–protein interactions through sequence-based deep learning
Motivation High-throughput experimental techniques have produced a large amount of
protein–protein interaction (PPI) data, but their coverage is still low and the PPI data is also …
protein–protein interaction (PPI) data, but their coverage is still low and the PPI data is also …
A new learning paradigm: Learning using privileged information
In the Afterword to the second edition of the book “Estimation of Dependences Based on
Empirical Data” by V. Vapnik, an advanced learning paradigm called Learning Using …
Empirical Data” by V. Vapnik, an advanced learning paradigm called Learning Using …
LocTree3 prediction of localization
The prediction of protein sub-cellular localization is an important step toward elucidating
protein function. For each query protein sequence, LocTree2 applies machine learning …
protein function. For each query protein sequence, LocTree2 applies machine learning …
Predicting anticancer peptides with Chou′ s pseudo amino acid composition and investigating their mutagenicity via Ames test
Z Hajisharifi, M Piryaiee, MM Beigi… - Journal of theoretical …, 2014 - Elsevier
Cancer is an important reason of death worldwide. Traditional cytotoxic therapies, such as
radiation and chemotherapy, are expensive and cause severe side effects. Currently, design …
radiation and chemotherapy, are expensive and cause severe side effects. Currently, design …