Proinflammatory cytokines have the capacity to increase inflammatory reaction and play a central role in first line of defence against invading pathogens. Proinflammatory inducing peptides (PIPs) have been used as an antineoplastic agent, an antibact...
BACKGROUND: Long short-term memory (LSTM) is one of the most attractive deep learning methods to learn time series or contexts of input data. Increasing studies, including biological sequence analyses in bioinformatics, utilize this architecture. Ami...
RNA protein interactions (RPI) play a pivotal role in the regulation of various biological processes. Experimental validation of RPI has been time-consuming, paving the way for computational prediction methods. The major limiting factor of these meth...
Engineering of GPCR constructs with improved thermostability is a key for successful structural and biochemical studies of this transmembrane protein family, targeted by 40% of all therapeutic drugs. Here we introduce a comprehensive computational ap...
Constructive (generative) machine learning enables the automated generation of novel chemical structures without the need for explicit molecular design rules. This study presents the experimental application of such a deep machine learning model to d...
A DNA-binding protein (DNA-BP) is a protein that can bind and interact with a DNA. Identification of DNA-BPs using experimental methods is expensive as well as time consuming. As such, fast and accurate computational methods are sought for predicting...
This study examines accurate and efficient computational method for identification of 5-methylcytosine sites in RNA modification. The occurrence of 5-methylcytosine (mC) plays a vital role in a number of biological processes. For better comprehension...
BACKGROUND: Protein superfamilies can be divided into subfamilies of proteins with different functional characteristics. Their sequences can be classified hierarchically, which is part of sequence function assignation. Typically, there are no clear s...
Designing protein sequences that can fold into a given structure is a well-known inverse protein-folding problem. One important characteristic to attain for a protein design program is the ability to recover wild-type sequences given their native bac...
Protein secondary structure prediction can provide important information for protein 3D structure prediction and protein functions. Deep learning offers a new opportunity to significantly improve prediction accuracy. In this article, a new deep neura...
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