AIMC Topic: High-Throughput Screening Assays

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Testing Precision Limits of Neural Network-Based Quality Control Metrics in High-Throughput Digital Microscopy.

Pharmaceutical research Jan 26, 2022
OBJECTIVE: Digital microscopy is used to monitor particulates such as protein aggregates within biopharmaceutical products. The images that result encode a wealth of information that is underutilized in pharmaceutical process monitoring. For example,...
Neural Networks, Computer Microscopy High-Throughput Screening Assays Drug Compounding Protein Aggregates Reproducibility of Results Image Processing, Computer-Assisted Proteins
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Wet-dry-wet drug screen leads to the synthesis of TS1, a novel compound reversing lung fibrosis through inhibition of myofibroblast differentiation.

Cell death & disease Dec 17, 2021
Therapies halting the progression of fibrosis are ineffective and limited. Activated myofibroblasts are emerging as important targets in the progression of fibrotic diseases. Previously, we performed a high-throughput screen on lung fibroblasts and s...
Lung Diseases Transfection High-Throughput Screening Assays Myofibroblasts Drug Screening Assays, Antitumor Cell Differentiation Humans Machine Learning Animals Idiopathic Pulmonary Fibrosis Drug Discovery Bleomycin Mice
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Categorical Matrix Completion With Active Learning for High-Throughput Screening.

IEEE/ACM transactions on computational biology and bioinformatics Dec 8, 2021
The recent advances in wet-lab automation enable high-throughput experiments to be conducted seamlessly. In particular, the exhaustive enumeration of all possible conditions is always involved in high-throughput screening. Nonetheless, such a screeni...
Algorithms Proteins Computer Simulation Supervised Machine Learning High-Throughput Screening Assays Computational Biology
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Integrating deep learning with microfluidics for biophysical classification of sickle red blood cells adhered to laminin.

PLoS computational biology Nov 29, 2021
Sickle cell disease, a genetic disorder affecting a sizeable global demographic, manifests in sickle red blood cells (sRBCs) with altered shape and biomechanics. sRBCs show heightened adhesive interactions with inflamed endothelium, triggering painfu...
Microfluidics Anemia, Sickle Cell High-Throughput Screening Assays Image Interpretation, Computer-Assisted Deep Learning In Vitro Techniques Computational Biology Protein Multimerization Erythrocytes, Abnormal Erythrocyte Deformability Hemoglobin, Sickle Biophysical Phenomena Lab-On-A-Chip Devices Laminin Neural Networks, Computer Diagnosis, Computer-Assisted Humans
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Image-guided MALDI mass spectrometry for high-throughput single-organelle characterization.

Nature methods Sep 30, 2021
Peptidergic dense-core vesicles are involved in packaging and releasing neuropeptides and peptide hormones-critical processes underlying brain, endocrine and exocrine function. Yet, the heterogeneity within these organelles, even for morphologically ...
Machine Learning Organelles Aplysia Animals Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization High-Throughput Screening Assays
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ECNet is an evolutionary context-integrated deep learning framework for protein engineering.

Nature communications Sep 30, 2021
Machine learning has been increasingly used for protein engineering. However, because the general sequence contexts they capture are not specific to the protein being engineered, the accuracy of existing machine learning algorithms is rather limited....
beta-Lactam Resistance Genetic Fitness Deep Learning Mutation Protein Engineering Evolution, Molecular Amino Acid Sequence High-Throughput Screening Assays Sequence Homology, Amino Acid beta-Lactamases Datasets as Topic
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Misic, a general deep learning-based method for the high-throughput cell segmentation of complex bacterial communities.

eLife Sep 9, 2021
Studies of bacterial communities, biofilms and microbiomes, are multiplying due to their impact on health and ecology. Live imaging of microbial communities requires new tools for the robust identification of bacterial cells in dense and often inter-...
Microscopy High-Throughput Screening Assays Bacterial Physiological Phenomena Species Specificity Biofilms Models, Biological Deep Learning Microbiota
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MCN-CPI: Multiscale Convolutional Network for Compound-Protein Interaction Prediction.

Biomolecules Jul 29, 2021
In the process of drug discovery, identifying the interaction between the protein and the novel compound plays an important role. With the development of technology, deep learning methods have shown excellent performance in various situations. Howeve...
Drugs, Investigational Kinetics Protein Conformation, alpha-Helical Humans Binding Sites Deep Learning High-Throughput Screening Assays Protein Conformation, beta-Strand Molecular Docking Simulation Protein Interaction Domains and Motifs Datasets as Topic Protein Binding Drug Discovery Proteins
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The Trifecta of Single-Cell, Systems-Biology, and Machine-Learning Approaches.

Genes Jul 20, 2021
Together, single-cell technologies and systems biology have been used to investigate previously unanswerable questions in biomedicine with unparalleled detail. Despite these advances, gaps in analytical capacity remain. Machine learning, which has re...
Algorithms Precision Medicine Animals Drug Discovery High-Throughput Screening Assays Single-Cell Analysis Humans Computational Biology Machine Learning Systems Biology
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Quick Annotator: an open-source digital pathology based rapid image annotation tool.

The journal of pathology. Clinical research Jul 19, 2021
Image-based biomarker discovery typically requires accurate segmentation of histologic structures (e.g. cell nuclei, tubules, and epithelial regions) in digital pathology whole slide images (WSIs). Unfortunately, annotating each structure of interest...
Workflow Microscopy High-Throughput Screening Assays Pathology Image Interpretation, Computer-Assisted Cell Nucleus Humans Epithelial Cells Automation, Laboratory Predictive Value of Tests Colorectal Neoplasms Reproducibility of Results Biopsy Deep Learning Time Factors
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