Ensemble deep learning for Alzheimer's disease characterization and estimation
Alzheimer's disease, which is characterized by a continual deterioration of cognitive abilities
in older people, is the most common form of dementia. Neuroimaging data, for example …
in older people, is the most common form of dementia. Neuroimaging data, for example …
Ensemble deep learning in bioinformatics
The remarkable flexibility and adaptability of ensemble methods and deep learning models
have led to the proliferation of their application in bioinformatics research. Traditionally …
have led to the proliferation of their application in bioinformatics research. Traditionally …
Machine learning meets omics: applications and perspectives
The innovation of biotechnologies has allowed the accumulation of omics data at an
alarming rate, thus introducing the era of 'big data'. Extracting inherent valuable knowledge …
alarming rate, thus introducing the era of 'big data'. Extracting inherent valuable knowledge …
Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation
The ever-increasing demand for novel materials with superior properties inspires retrofitting
traditional research paradigms in the era of artificial intelligence and automation. An …
traditional research paradigms in the era of artificial intelligence and automation. An …
A streamlined platform for analyzing tera-scale DDA and DIA mass spectrometry data enables highly sensitive immunopeptidomics
Integrating data-dependent acquisition (DDA) and data-independent acquisition (DIA)
approaches can enable highly sensitive mass spectrometry, especially for …
approaches can enable highly sensitive mass spectrometry, especially for …
[HTML][HTML] Identification and characterization of post-translational modifications: Clinical implications
J Hermann, L Schurgers, V Jankowski - Molecular aspects of medicine, 2022 - Elsevier
Post-translational modifications (PTMs) generate marginally modified isoforms of native
peptides, proteins and lipoproteins thereby regulating protein functions, molecular …
peptides, proteins and lipoproteins thereby regulating protein functions, molecular …
Deep learning neural network tools for proteomics
JG Meyer - Cell Reports Methods, 2021 - cell.com
Mass-spectrometry-based proteomics enables quantitative analysis of thousands of human
proteins. However, experimental and computational challenges restrict progress in the field …
proteins. However, experimental and computational challenges restrict progress in the field …
An end-to-end deep learning framework for translating mass spectra to de-novo molecules
Elucidating the structure of a chemical compound is a fundamental task in chemistry with
applications in multiple domains including drug discovery, precision medicine, and …
applications in multiple domains including drug discovery, precision medicine, and …
Machine learning for the advancement of genome-scale metabolic modeling
Constraint-based modeling (CBM) has evolved as the core systems biology tool to map the
interrelations between genotype, phenotype, and external environment. The recent …
interrelations between genotype, phenotype, and external environment. The recent …
TopFD: A proteoform feature detection tool for top–down proteomics
Top-down liquid chromatography-mass spectrometry (LC-MS) analyzes intact proteoforms
and generates mass spectra containing peaks of proteoforms with various isotopic …
and generates mass spectra containing peaks of proteoforms with various isotopic …