AIMC Topic: Amino Acids

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Enabling full-length evolutionary profiles based deep convolutional neural network for predicting DNA-binding proteins from sequence.

Proteins
Sequence based DNA-binding protein (DBP) prediction is a widely studied biological problem. Sliding windows on position specific substitution matrices (PSSMs) rows predict DNA-binding residues well on known DBPs but the same models cannot be applied ...

Recognition of early and late stages of bladder cancer using metabolites and machine learning.

Metabolomics : Official journal of the Metabolomic Society
INTRODUCTION: Bladder cancer (BCa) is one of the most common and aggressive cancers. It is the sixth most frequently occurring cancer in men and its rate of occurrence increases with age. The current method of BCa diagnosis includes a cystoscopy and ...

The Classifying Autoencoder: Gaining Insight into Amyloid Assembly of Peptides and Proteins.

The journal of physical chemistry. B
Despite the importance of amyloid formation in disease pathology, the understanding of the primary structure?activity relationship for amyloid-forming peptides remains elusive. Here we use a new neural-network based method of analysis: the classifyin...

Auto-encoding NMR chemical shifts from their native vector space to a residue-level biophysical index.

Nature communications
Chemical shifts (CS) are determined from NMR experiments and represent the resonance frequency of the spin of atoms in a magnetic field. They contain a mixture of information, encompassing the in-solution conformations a protein adopts, as well as th...

Discrimination power of knowledge-based potential dictated by the dominant energies in native protein structures.

Amino acids
Extracting a well-designed energy function is important for protein structure evaluation. Knowledge-based potential functions are one type of the energy functions which can be obtained from known protein structures. The pairwise potential between ato...

Effects of combining modern recovery techniques with neurotrophic medication and standard treatment in stroke patients.

The Science of the total environment
Stroke is the main cause of disability after age 65, leaving survivors with sequels that require care and recovery treatment lasting years. It is estimated that by the year 2030 this pathology will be leading cause of mortality. To determine the effi...

Prediction of aptamer-protein interacting pairs based on sparse autoencoder feature extraction and an ensemble classifier.

Mathematical biosciences
Aptamer-protein interacting pairs play important roles in physiological functions and structural characterization. Identifying aptamer-protein interacting pairs is challenging and limited, despite of the tremendous applications of aptamers. Therefore...

Intact metabolite spectrum mining by deep learning in proton magnetic resonance spectroscopy of the brain.

Magnetic resonance in medicine
PURPOSE: To develop a robust method for brain metabolite quantification in proton magnetic resonance spectroscopy ( H-MRS) using a convolutional neural network (CNN) that maps in vivo brain spectra that are typically degraded by low SNR, line broaden...

Prediction of apoptosis protein subcellular localization via heterogeneous features and hierarchical extreme learning machine.

SAR and QSAR in environmental research
Apoptosis is a fundamental process controlling normal tissue homeostasis by regulating a balance between cell proliferation and death. Predicting the subcellular location of apoptosis proteins is very helpful for understanding the mechanism of progra...

Integration of A Deep Learning Classifier with A Random Forest Approach for Predicting Malonylation Sites.

Genomics, proteomics & bioinformatics
As a newly-identified protein post-translational modification, malonylation is involved in a variety of biological functions. Recognizing malonylation sites in substrates represents an initial but crucial step in elucidating the molecular mechanisms ...