AIMC Topic: Software

Clear Filters Showing 3021 to 3030 of 3675 articles

Deep learning-enhanced, open-source eigenmode expansion.

Optics letters
We present an open-source eigenmode expansion (EME) software package entirely implemented in the Python programming language. Eigenmode expansion Python (EMEPy) utilizes artificial neural networks to reproduce electromagnetic eigenmode field profiles...

Potential of edge machine learning for instrumentation.

Applied optics
New developments in radiation and photonic detectors improve resolution, sensitivity, size, and rate, all of which contribute to a gigantic increase in the data production rate. Moving data analysis and compression adjacent or even embedded within th...

BERT6mA: prediction of DNA N6-methyladenine site using deep learning-based approaches.

Briefings in bioinformatics
N6-methyladenine (6mA) is associated with important roles in DNA replication, DNA repair, transcription, regulation of gene expression. Several experimental methods were used to identify DNA modifications. However, these experimental methods are cost...

Adapt-Kcr: a novel deep learning framework for accurate prediction of lysine crotonylation sites based on learning embedding features and attention architecture.

Briefings in bioinformatics
Protein lysine crotonylation (Kcr) is an important type of posttranslational modification that is associated with a wide range of biological processes. The identification of Kcr sites is critical to better understanding their functional mechanisms. H...

ASPIRER: a new computational approach for identifying non-classical secreted proteins based on deep learning.

Briefings in bioinformatics
Protein secretion has a pivotal role in many biological processes and is particularly important for intercellular communication, from the cytoplasm to the host or external environment. Gram-positive bacteria can secrete proteins through multiple secr...

Accelerating the discovery of antifungal peptides using deep temporal convolutional networks.

Briefings in bioinformatics
The application of machine intelligence in biological sciences has led to the development of several automated tools, thus enabling rapid drug discovery. Adding to this development is the ongoing COVID-19 pandemic, due to which researchers working in...

Out-of-the-box deep learning prediction of quantum-mechanical partial charges by graph representation and transfer learning.

Briefings in bioinformatics
Accurate prediction of atomic partial charges with high-level quantum mechanics (QM) methods suffers from high computational cost. Numerous feature-engineered machine learning (ML)-based predictors with favorable computability and reliability have be...

CRISP: a deep learning architecture for GC × GC-TOFMS contour ROI identification, simulation and analysis in imaging metabolomics.

Briefings in bioinformatics
Two-dimensional gas chromatography-time-of-flight mass spectrometry (GC × GC-TOFMS) provides a large amount of molecular information from biological samples. However, the lack of a comprehensive compound library or customizable bioinformatics tool is...

A deep learning framework for characterization of genotype data.

G3 (Bethesda, Md.)
Dimensionality reduction is a data transformation technique widely used in various fields of genomics research. The application of dimensionality reduction to genotype data is known to capture genetic similarity between individuals, and is used for v...

ToxIBTL: prediction of peptide toxicity based on information bottleneck and transfer learning.

Bioinformatics (Oxford, England)
MOTIVATION: Recently, peptides have emerged as a promising class of pharmaceuticals for various diseases treatment poised between traditional small molecule drugs and therapeutic proteins. However, one of the key bottlenecks preventing them from ther...