AI Medical Compendium Journal:
Combinatorial chemistry & high throughput screening

Showing 51 to 60 of 68 articles

Identification of Cell Cycle-Regulated Genes by Convolutional Neural Network.

Combinatorial chemistry & high throughput screening Jan 1, 2017
BACKGROUND: The cell cycle-regulated genes express periodically with the cell cycle stages, and the identification and study of these genes can provide a deep understanding of the cell cycle process. Large false positives and low overlaps are big pro...
Databases, Genetic Saccharomyces cerevisiae Saccharomyces cerevisiae Proteins Cell Cycle Proteins Neural Networks, Computer Algorithms
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Recognizing and Predicting Thioether Bridges Formed by Lanthionine and β-Methyllanthionine in Lantibiotics Using a Random Forest Approach with Feature Selection.

Combinatorial chemistry & high throughput screening Jan 1, 2017
BACKGROUND: Lantibiotics, which are usually produced from Gram-positive bacteria, are regarded as one type of special bacteriocins. Lantibiotics have unsaturated amino acid residues formed by lanthionine (Lan) and β-methyllanthionine (MeLan) residues...
Alanine Bacteriocins Machine Learning Databases, Protein Sulfides
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Prediction and Identification of Krüppel-Like Transcription Factors by Machine Learning Method.

Combinatorial chemistry & high throughput screening Jan 1, 2017
AIM AND OBJECTIVE: The Krüppel-like factors (KLFs) are a family of containing Zn finger(ZF) motif transcription factors with 18 members in human genome, among them, KLF18 is predicted by bioinformatics. KLFs possess various physiological function inv...
Humans Amino Acids Kruppel-Like Transcription Factors Zinc Fingers Machine Learning Databases, Protein
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Machine Learning and Molecular Dynamics Based Insights into Mode of Actions of Insulin Degrading Enzyme Modulators.

Combinatorial chemistry & high throughput screening Jan 1, 2017
BACKGROUND: Alzheimer's disease (AD) is one of the most common lethal neurodegenerative disorders having impact on the lives of millions of people worldwide. The disease lacks effective treatment options and the unavailability of the drugs to cure th...
Insulysin Molecular Docking Simulation Drug Discovery Molecular Dynamics Simulation Humans Machine Learning
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Development of a Web-Enabled SVR-Based Machine Learning Platform and its Application on Modeling Transgene Expression Activity of Aminoglycoside-Derived Polycations.

Combinatorial chemistry & high throughput screening Jan 1, 2017
OBJECTIVE: Support Vector Regression (SVR) has become increasingly popular in cheminformatics modeling. As a result, SVR-based machine learning algorithms, including Fuzzy-SVR and Least Square-SVR (LS-SVR) have been developed and applied in various r...
Regression Analysis Least-Squares Analysis Models, Biological Anti-Bacterial Agents Cell Line, Tumor Aminoglycosides Transgenes Polyelectrolytes Quantitative Structure-Activity Relationship Polyamines Transfection Humans Software Algorithms Machine Learning
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SAnDReS a Computational Tool for Statistical Analysis of Docking Results and Development of Scoring Functions.

Combinatorial chemistry & high throughput screening Jan 1, 2016
BACKGROUND: Docking allows to predict ligand binding to proteins, since the 3D-structure for the target is available. Several docking studies have been carried out to identify potential ligands for drug targets. Many of these studies resulted in the ...
Databases, Protein Data Interpretation, Statistical Machine Learning Molecular Docking Simulation
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Structural Comparison of Gene Relevance Networks for Breast Cancer Tissues in Different Grades.

Combinatorial chemistry & high throughput screening Jan 1, 2016
BACKGROUND: The breast is an important biological system of human with two distinct states, i.e. normal and tumoral. Research on breast cancer could be based on systematic modeling to contrast the system structures of these two states.
Genes, Neoplasm Proto-Oncogene Proteins c-fos ZAP-70 Protein-Tyrosine Kinase ErbB Receptors ras Proteins bcl-2 Homologous Antagonist-Killer Protein Female Breast Neoplasms Support Vector Machine Lipocalin-2 Gene Regulatory Networks Humans
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Cheminformatics Based Machine Learning Approaches for Assessing Glycolytic Pathway Antagonists of Mycobacterium tuberculosis.

Combinatorial chemistry & high throughput screening Jan 1, 2016
BACKGROUND: Tuberculosis is the second leading cause of death from an infectious disease worldwide after HIV, thus reasoning the expeditions in antituberculosis research. The rising number of cases of infection by resistant forms of M. tuberculosis h...
Enzyme Inhibitors Fructose-Bisphosphate Aldolase Tuberculosis Machine Learning Mycobacterium tuberculosis Algorithms High-Throughput Screening Assays Drug Design Glycolysis Computational Biology Humans
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Discrimination of Active and Weakly Active Human BACE1 Inhibitors Using Self-Organizing Map and Support Vector Machine.

Combinatorial chemistry & high throughput screening Jan 1, 2016
β-secretase (BACE1) is an aspartyl protease, which is considered as a novel vital target in Alzheimer`s disease therapy. We collected a data set of 294 BACE1 inhibitors, and built six classification models to discriminate active and weakly active inh...
Sensitivity and Specificity Drug Design Humans Aspartic Acid Endopeptidases Enzyme Inhibitors Linear Models Amyloid Precursor Protein Secretases Support Vector Machine Alzheimer Disease
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Self-Organizing Map (SOM) and Support Vector Machine (SVM) Models for the Prediction of Human Epidermal Growth Factor Receptor (EGFR/ ErbB-1) Inhibitors.

Combinatorial chemistry & high throughput screening Jan 1, 2016
EGFR (ErbB-1/HER1) kinase plays an important role in cancer therapy. Two classification models were established to predict whether a compound is an inhibitor or a decoy of human EGFR (ErbR-1) by using Kohonen's self-organizing map (SOM) and support v...
Computing Methodologies Binding Sites Substrate Specificity Humans Software Molecular Structure ErbB Receptors Support Vector Machine
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