AIMC Topic: High-Throughput Screening Assays

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A Primer on Data Analytics in Functional Genomics: How to Move from Data to Insight?

Trends in biochemical sciences Dec 3, 2018
High-throughput methodologies and machine learning have been central in developing systems-level perspectives in molecular biology. Unfortunately, performing such integrative analyses has traditionally been reserved for bioinformaticians. This is now...
Data Science Genomics High-Throughput Screening Assays Software Animals Humans Machine Learning
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Combining high-throughput imaging flow cytometry and deep learning for efficient species and life-cycle stage identification of phytoplankton.

BMC ecology Dec 3, 2018
BACKGROUND: Phytoplankton species identification and counting is a crucial step of water quality assessment. Especially drinking water reservoirs, bathing and ballast water need to be regularly monitored for harmful species. In times of multiple envi...
Life Cycle Stages Phytoplankton High-Throughput Screening Assays Deep Learning Environmental Monitoring Flow Cytometry
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Application of Bioactivity Profile-Based Fingerprints for Building Machine Learning Models.

Journal of chemical information and modeling Nov 29, 2018
The volume of high throughput screening data has considerably increased since the beginning of the automated biochemical and cell-based assays era. This information-rich data source provides tremendous repurposing opportunities for data mining. It wa...
Neural Networks, Computer Machine Learning Drug Evaluation, Preclinical High-Throughput Screening Assays
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Predicting Thermodynamic Properties of Alkanes by High-Throughput Force Field Simulation and Machine Learning.

Journal of chemical information and modeling Sep 26, 2018
Knowledge of the thermodynamic properties of molecules is essential for chemical process design and the development of new materials. Experimental measurements are often expensive and not environmentally friendly. In the past, studies using molecular...
Machine Learning Temperature Models, Chemical Neural Networks, Computer Alkanes Molecular Dynamics Simulation High-Throughput Screening Assays Thermodynamics
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High-throughput ovarian follicle counting by an innovative deep learning approach.

Scientific reports Sep 10, 2018
The evaluation of the number of mouse ovarian primordial follicles (PMF) can provide important information about ovarian function, regulation of folliculogenesis or the impact of chemotherapy on fertility. This counting, usually performed by speciali...
Female Animals High-Throughput Screening Assays Models, Animal Deep Learning Ovarian Follicle Mice
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Robotic selection for the rapid development of stable CHO cell lines for HIV vaccine production.

PloS one Aug 2, 2018
The production of envelope glycoproteins (Envs) for use as HIV vaccines is challenging. The yield of Envs expressed in stable Chinese Hamster Ovary (CHO) cell lines is typically 10-100 fold lower than other glycoproteins of pharmaceutical interest. M...
Robotics AIDS Vaccines Antibodies, Neutralizing HIV Antibodies Antibody Formation HIV-1 Animals Humans Cricetinae High-Throughput Screening Assays CHO Cells Automation, Laboratory HEK293 Cells Cricetulus
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Network-Based Drug Discovery: Coupling Network Pharmacology with Phenotypic Screening for Neuronal Excitability.

Journal of molecular biology Jul 18, 2018
Diseases such as chronic pain with complex etiologies are unlikely to respond to single, target-specific therapeutics but rather require intervention at multiple points within a perturbed disease system. Such approaches are being enabled by the rise ...
Cell Culture Techniques Drug Discovery ROC Curve Computational Biology Neurons High-Throughput Screening Assays Humans Neural Networks, Computer
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PAIN(S) relievers for medicinal chemists: how computational methods can assist in hit evaluation.

Future medicinal chemistry Jun 29, 2018
Machine Learning Drug Discovery Models, Statistical Drug Evaluation, Preclinical High-Throughput Screening Assays Small Molecule Libraries Humans
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Perturbation-Theory and Machine Learning (PTML) Model for High-Throughput Screening of Parham Reactions: Experimental and Theoretical Studies.

Journal of chemical information and modeling Jun 27, 2018
Machine learning (ML) algorithms are gaining importance in the processing of chemical information and modeling of chemical reactivity problems. In this work, we have developed a perturbation-theory and machine learning (PTML) model combining perturba...
Cyclization Molecular Structure Organometallic Compounds Algorithms Models, Chemical High-Throughput Screening Assays Thermodynamics Lithium Isoquinolines Linear Models Structure-Activity Relationship Databases, Chemical Neural Networks, Computer Machine Learning
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Machine learning in chemoinformatics and drug discovery.

Drug discovery today May 8, 2018
Chemoinformatics is an established discipline focusing on extracting, processing and extrapolating meaningful data from chemical structures. With the rapid explosion of chemical 'big' data from HTS and combinatorial synthesis, machine learning has be...
Humans Quantitative Structure-Activity Relationship Informatics Molecular Structure High-Throughput Screening Assays Pharmaceutical Preparations Pattern Recognition, Automated Diffusion of Innovation Machine Learning Animals Drug Discovery
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