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Oligopeptides

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Beyond Tripeptides Two-Step Active Machine Learning for Very Large Data sets.

Journal of chemical theory and computation
Self-assembling peptide nanostructures have been shown to be of great importance in nature and have presented many promising applications, for example, in medicine as drug-delivery vehicles, biosensors, and antivirals. Being very promising candidates...

Engineered Extracellular Matrices with Integrated Wireless Microactuators to Study Mechanobiology.

Advanced materials (Deerfield Beach, Fla.)
Mechanobiology explores how forces regulate cell behaviors and what molecular machinery are responsible for the sensing, transduction, and modulation of mechanical cues. To this end, probing of cells cultured on planar substrates has served as a prim...

Local Kernel Regression and Neural Network Approaches to the Conformational Landscapes of Oligopeptides.

Journal of chemical theory and computation
The application of machine learning to theoretical chemistry has made it possible to combine the accuracy of quantum chemical energetics with the thorough sampling of finite-temperature fluctuations. To reach this goal, a diverse set of methods has b...

Deep Learning Empowers the Discovery of Self-Assembling Peptides with Over 10 Trillion Sequences.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Self-assembling of peptides is essential for a variety of biological and medical applications. However, it is challenging to investigate the self-assembling properties of peptides within the complete sequence space due to the enormous sequence quanti...

Machine learning-based analysis of Ga-PSMA-11 PET/CT images for estimation of prostate tumor grade.

Physical and engineering sciences in medicine
Early diagnosis of prostate cancer, the most common malignancy in men, can improve patient outcomes. Since the tissue sampling procedures are invasive and sometimes inconclusive, an alternative image-based method can prevent possible complications an...

The impact of multicentric datasets for the automated tumor delineation in primary prostate cancer using convolutional neural networks on F-PSMA-1007 PET.

Radiation oncology (London, England)
PURPOSE: Convolutional Neural Networks (CNNs) have emerged as transformative tools in the field of radiation oncology, significantly advancing the precision of contouring practices. However, the adaptability of these algorithms across diverse scanner...

Augmenting Human Expertise in Weighted Ensemble Simulations through Deep Learning-Based Information Bottleneck.

Journal of chemical theory and computation
The weighted ensemble (WE) method stands out as a widely used segment-based sampling technique renowned for its rigorous treatment of kinetics. The WE framework typically involves initially mapping the configuration space onto a low-dimensional colle...

Dynamics and Machine Learning Reveal the Link between Tripeptide Sequences and Evaporation-Driven Material Properties.

Nano letters
Previous research showed that a peptide composed of three tyrosines (YYY) can turn into organic glass and cause strong adhesion between substrates via evaporation. However, the mechanisms of these processes remain unclear, and the exploration of appl...

The Use of Maximum-Intensity Projections and Deep Learning Adds Value to the Fully Automatic Segmentation of Lesions Avid for [F]FDG and [Ga]Ga-PSMA in PET/CT.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
This study investigated the added value of using maximum-intensity projection (MIP) images for fully automatic segmentation of lesions using deep learning (DL) in [F]FDG and [Ga]Ga-prostate-specific membrane antigen (PSMA) PET/CT scans. We used 489 ...