AIMC Topic: Machine Learning

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An Approach of Anomaly Detection and Neural Network Classifiers to Measure Cellulolytic Activity.

Combinatorial chemistry & high throughput screening Jan 1, 2018
AIM AND OBJECTIVE: A common method used for massive detection of cellulolytic microorganisms is based on the formation of halos on solid medium. However, this is a subjective method and real-time monitoring is not possible. The objective of this work...
Coloring Agents Hydrolysis Neural Networks, Computer Cellulose Models, Molecular Machine Learning Gene Library Pattern Recognition, Automated Color Congo Red
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In silico Prediction of Inhibitory Constant of Thrombin Inhibitors Using Machine Learning.

Combinatorial chemistry & high throughput screening Jan 1, 2018
BACKGROUND: Thrombin is the central protease of the vertebrate blood coagulation cascade, which is closely related to cardiovascular diseases. The inhibitory constant Ki is the most significant property of thrombin inhibitors.
Thermodynamics Thrombin Computer Simulation Drug Discovery Molecular Structure Anticoagulants Humans Linear Models Support Vector Machine Machine Learning Quantitative Structure-Activity Relationship Models, Molecular
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A big data approach with artificial neural network and molecular similarity for chemical data mining and endocrine disruption prediction.

Indian journal of pharmacology Jan 1, 2018
CONTEXT: Chemical toxicity prediction at early stage drug discovery phase has been researched for years, and newest methods are always investigated. Research data comprising chemical physicochemical properties, toxicity, assay, and activity details c...
Humans Animals Drug Discovery Big Data Neural Networks, Computer Machine Learning Receptors, Estrogen Inhibitory Concentration 50 Models, Theoretical Data Mining Receptors, Androgen Endocrine Disruptors
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New Experimental and Computational Tools for Drug Discovery: Medicinal Chemistry, Molecular Docking, and Machine Learning - Part-VI.

Current topics in medicinal chemistry Jan 1, 2018
Chemistry, Pharmaceutical Machine Learning Molecular Docking Simulation Drug Discovery
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Computational Identification of Chemical Compounds with Potential Activity against Leishmania amazonensis using Nonlinear Machine Learning Techniques.

Current topics in medicinal chemistry Jan 1, 2018
Leishmaniasis is a poverty-related disease endemic in 98 countries worldwide, with morbidity and mortality increasing daily. All currently used first-line and second-line drugs for the treatment of leishmaniasis exhibit several drawbacks including to...
Machine Learning Models, Molecular Drug Evaluation, Preclinical Software Leishmania Antiprotozoal Agents Parasitic Sensitivity Tests
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Intelligently Applying Artificial Intelligence in Chemoinformatics.

Current topics in medicinal chemistry Jan 1, 2018
The intertwining of chemoinformatics with artificial intelligence (AI) has given a tremendous fillip to the field of drug discovery. With the rapid growth of chemical data from high throughput screening and combinatorial synthesis, AI has become an i...
Algorithms Drug Discovery High-Throughput Screening Assays Quantitative Structure-Activity Relationship Data Mining Artificial Intelligence Machine Learning
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AI Tackles Hospital Infections: Machine Learning Is Helping Clinicians.

IEEE pulse Jan 1, 2018
For Ashley Zappia (Figure 1), getting her hands dirty was part of her job. Even though she always tried to remain as clean as possible, her work as a nursing aide at a Southern California hospital required a lot of diapering, changing, and other hand...
Machine Learning Risk Factors Infection Control Cross Infection Humans Clostridium Infections Immunocompromised Host Clostridioides difficile Medical Informatics Gastrointestinal Microbiome
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Machine Learning Methods in Precision Medicine Targeting Epigenetic Diseases.

Current pharmaceutical design Jan 1, 2018
BACKGROUND: On a tide of big data, machine learning is coming to its day. Referring to huge amounts of epigenetic data coming from biological experiments and clinic, machine learning can help in detecting epigenetic features in genome, finding correl...
Precision Medicine Epigenomics Disease Humans Machine Learning
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The Potential of Quantum Computing and Machine Learning to Advance Clinical Research and Change the Practice of Medicine.

Missouri medicine Jan 1, 2018
Humans Delivery of Health Care Biomedical Research Machine Learning Quantum Theory
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Predicting Inhibitors for Multidrug Resistance Associated Protein-2 Transporter by Machine Learning Approach.

Combinatorial chemistry & high throughput screening Jan 1, 2018
BACKGROUND: The efflux transporter multidrug resistance associated protein-2 belongs to ATP-binding cassette superfamily which plays an important role in multidrug resistance and drugdrug interactions. Efflux transporters are considered to be importa...
Multidrug Resistance-Associated Proteins Multidrug Resistance-Associated Protein 2 Quantitative Structure-Activity Relationship Databases, Pharmaceutical Machine Learning Algorithms Support Vector Machine Molecular Structure Drug Discovery
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