Protein secondary structure prediction continues to rise
B Rost - Journal of structural biology, 2001 - Elsevier
Methods predicting protein secondary structure improved substantially in the 1990s through
the use of evolutionary information taken from the divergence of proteins in the same …
the use of evolutionary information taken from the divergence of proteins in the same …
Use of machine learning approaches for novel drug discovery
abstract Introduction: The use of computational tools in the early stages of drug development
has increased in recent decades. Machine learning (ML) approaches have been of special …
has increased in recent decades. Machine learning (ML) approaches have been of special …
[BOOK][B] Bioinformatics: the machine learning approach
A guide to machine learning approaches and their application to the analysis of biological
data. An unprecedented wealth of data is being generated by genome sequencing projects …
data. An unprecedented wealth of data is being generated by genome sequencing projects …
[BOOK][B] Kernel methods in computational biology
A detailed overview of current research in kernel methods and their application to
computational biology. Modern machine learning techniques are proving to be extremely …
computational biology. Modern machine learning techniques are proving to be extremely …
Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles
Secondary structure predictions are increasingly becoming the workhorse for several
methods aiming at predicting protein structure and function. Here we use ensembles of …
methods aiming at predicting protein structure and function. Here we use ensembles of …
A generic method for assignment of reliability scores applied to solvent accessibility predictions
Background Estimation of the reliability of specific real value predictions is nontrivial and the
efficacy of this is often questionable. It is important to know if you can trust a given prediction …
efficacy of this is often questionable. It is important to know if you can trust a given prediction …
[BOOK][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 …
A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction
Ab initio protein secondary structure (SS) predictions are utilized to generate tertiary
structure predictions, which are increasingly demanded due to the rapid discovery of …
structure predictions, which are increasingly demanded due to the rapid discovery of …
Porter: a new, accurate server for protein secondary structure prediction
Porter is a new system for protein secondary structure prediction in three classes. Porter
relies on bidirectional recurrent neural networks with shortcut connections, accurate coding …
relies on bidirectional recurrent neural networks with shortcut connections, accurate coding …
CPHmodels-3.0—remote homology modeling using structure-guided sequence profiles
Abstract CPHmodels-3.0 is a web server predicting protein 3D structure by use of single
template homology modeling. The server employs a hybrid of the scoring functions of …
template homology modeling. The server employs a hybrid of the scoring functions of …