The first step in the construction of a regression model or a data-driven analysis, aiming to
predict or elucidate the relationship between the atomic-scale structure of matter and its …

Acknowledgements We thank H. Pospiech, R. Smith, F. Grosse and S. Hasan for critical
reading of the manuscript and for help with the artwork. UH has been supported by the …

Accurately predicting peptide secondary structures remains a challenging task due to the
lack of discriminative information in short peptides. In this study, PHAT is proposed, a deep …

A two-stage neural network has been used to predict protein secondary structure based on
the position specific scoring matrices generated by PSI-BLAST. Despite the simplicity and …

Pro-inflammatory cytokines activate the transcription factor NF-κB by stimulating the activity
of a protein kinase that phosphorylates IκB, an inhibitor of NF-κB,,,,, at sites that trigger its …

A new dataset of 396 protein domains is developed and used to evaluate the performance of
the protein secondary structure prediction algorithms DSC, PHD, NNSSP, and PREDATOR …

PRofile ALIgNEment (PRALINE) is a fully customizable multiple sequence alignment
application. In addition to a number of available alignment strategies, PRALINE can …

The effect of training a neural network secondary structure prediction algorithm with different
types of multiple sequence alignment profiles derived from the same sequences, is shown to …

Protein existing as an ensemble of structures, called intrinsically disordered, has been
shown to be responsible for a wide variety of biological functions and to be common in …

Background Though most of the transcripts are long non-coding RNAs (lncRNAs), little is
known about their functions. lncRNAs usually function through interactions with proteins …