AIMC Topic: Blood Proteins
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Machine learning assistive rapid, label-free molecular phenotyping of blood with two-dimensional NMR correlational spectroscopy.
Communications biology
Sep 28, 2020
Translation of the findings in basic science and clinical research into routine practice is hampered by large variations in human phenotype. Developments in genotyping and phenotyping, such as proteomics and lipidomics, are beginning to address these...
Direct Comparison of Total Clearance Prediction: Computational Machine Learning Model versus Bottom-Up Approach Using In Vitro Assay.
Molecular pharmaceutics
Jun 12, 2020
The in vitro-in vivo extrapolation (IVIVE) approach for predicting total plasma clearance (CL) has been widely used to rank order compounds early in discovery. More recently, a computational machine learning approach utilizing physicochemical descrip...
Humans
Metabolic Clearance Rate
Membranes, Artificial
Pharmacokinetics
Administration, Intravenous
Plasma
Blood Proteins
Pharmaceutical Preparations
Dogs
Models, Biological
Madin Darby Canine Kidney Cells
Microsomes, Liver
Hepatocytes
Permeability
Protein Binding
Rats
Rats, Sprague-Dawley
Liver
Machine Learning
Animals
Male
Supervised Learning and Mass Spectrometry Predicts the Fate of Nanomaterials.
ACS nano
Jul 3, 2019
The surface of nanoparticles changes immediately after intravenous injection because blood proteins adsorb on the surface. How this interface changes during circulation and its impact on nanoparticle distribution within the body is not understood. He...
fastJT: An R package for robust and efficient feature selection for machine learning and genome-wide association studies.
BMC bioinformatics
Jun 13, 2019
BACKGROUND: Parametric feature selection methods for machine learning and association studies based on genetic data are not robust with respect to outliers or influential observations. While rank-based, distribution-free statistics offer a robust alt...
Computational prediction of plasma protein binding of cyclic peptides from small molecule experimental data using sparse modeling techniques.
BMC bioinformatics
Dec 31, 2018
BACKGROUND: Cyclic peptide-based drug discovery is attracting increasing interest owing to its potential to avoid target protein depletion. In drug discovery, it is important to maintain the biostability of a drug within the proper range. Plasma prot...
R-100 improves pulmonary function and systemic fluid balance in sheep with combined smoke-inhalation injury and Pseudomonas aeruginosa sepsis.
Journal of translational medicine
Dec 28, 2017
BACKGROUND: Septic shock is a major cause of death in intensive care units around the world . The aim of the study was to investigate whether the novel drug R-100 (a superoxide degradation catalyst and nitric oxide donor) improves pulmonary function ...
Identifying novel factor XIIa inhibitors with PCA-GA-SVM developed vHTS models.
European journal of medicinal chemistry
Sep 1, 2017
There currently is renewed interest in blood clotting Factor XII as a potential target for thrombosis inhibition. Historically untargeted, there is little drug information with which to start drug candidate searches. Typical high-throughput screening...
Informing the Human Plasma Protein Binding of Environmental Chemicals by Machine Learning in the Pharmaceutical Space: Applicability Domain and Limits of Predictability.
Journal of chemical information and modeling
Nov 3, 2016
The free fraction of a xenobiotic in plasma (F) is an important determinant of chemical adsorption, distribution, metabolism, elimination, and toxicity, yet experimental plasma protein binding data are scarce for environmentally relevant chemicals. T...
Investigation of the influence of protein corona composition on gold nanoparticle bioactivity using machine learning approaches.
SAR and QSAR in environmental research
Jun 22, 2016
The understanding of the mechanisms and interactions that occur when nanomaterials enter biological systems is important to improve their future use. The adsorption of proteins from biological fluids in a physiological environment to form a corona on...
Machine learning reveals sex-specific 17β-estradiol-responsive expression patterns in white perch (Morone americana) plasma proteins.
Proteomics
Jun 11, 2015
With growing abundance and awareness of endocrine disrupting compounds (EDCs) in the environment, there is a need for accurate and reliable detection of EDC exposure. Our objective in the present study was to observe differences within and between th...
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