AIMC Topic: Cell Line

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Evaluation of Image Classification for Quantifying Mitochondrial Morphology Using Deep Learning.

Endocrine, metabolic & immune disorders drug targets Jan 1, 2023
BACKGROUND: Mitochondrial morphology reversibly changes between fission and fusion. As these changes (mitochondrial dynamics) reflect the cellular condition, they are one of the simplest indicators of cell state and predictors of cell fate. However, ...
Deep Learning Cell Line Mitochondria
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Predicting cell line-specific synergistic drug combinations through a relational graph convolutional network with attention mechanism.

Briefings in bioinformatics Nov 19, 2022
Identifying synergistic drug combinations (SDCs) is a great challenge due to the combinatorial complexity and the fact that SDC is cell line specific. The existing computational methods either did not consider the cell line specificity of SDC, or did...
Neural Networks, Computer Drug Combinations Cell Line
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CLNN-loop: a deep learning model to predict CTCF-mediated chromatin loops in the different cell lines and CTCF-binding sites (CBS) pair types.

Bioinformatics (Oxford, England) Sep 30, 2022
MOTIVATION: Three-dimensional (3D) genome organization is of vital importance in gene regulation and disease mechanisms. Previous studies have shown that CTCF-mediated chromatin loops are crucial to studying the 3D structure of cells. Although variou...
Binding Sites Chromatin CCCTC-Binding Factor Deep Learning Cell Line
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CycleDNN - A Novel Deep Neural Network Model for CETSA Feature Prediction cross Cell Lines.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2022
Cellular Thermal Shift Assay (CETSA) has been widely used in drug discovery, cancer cell biology, immunology, etc. One of the barriers for CETSA applications is that CETSA experiments have to be conducted on various cell lines, which is extremely tim...
Proteins Neural Networks, Computer Research Design Drug Discovery Cell Line
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PRODeepSyn: predicting anticancer synergistic drug combinations by embedding cell lines with protein-protein interaction network.

Briefings in bioinformatics Mar 10, 2022
Although drug combinations in cancer treatment appear to be a promising therapeutic strategy with respect to monotherapy, it is arduous to discover new synergistic drug combinations due to the combinatorial explosion. Deep learning technology holds i...
Drug Combinations Cell Line Neural Networks, Computer Machine Learning Protein Interaction Maps
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How much can deep learning improve prediction of the responses to drugs in cancer cell lines?

Briefings in bioinformatics Jan 17, 2022
The drug response prediction problem arises from personalized medicine and drug discovery. Deep neural networks have been applied to the multi-omics data being available for over 1000 cancer cell lines and tissues for better drug response prediction....
Neural Networks, Computer Precision Medicine Neoplasms Humans Deep Learning Cell Line
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A self-attention model for inferring cooperativity between regulatory features.

Nucleic acids research Jul 21, 2021
Deep learning has demonstrated its predictive power in modeling complex biological phenomena such as gene expression. The value of these models hinges not only on their accuracy, but also on the ability to extract biologically relevant information fr...
Promoter Regions, Genetic Nucleotide Motifs Regulatory Elements, Transcriptional Transcription Factors Cell Line Arabidopsis Chromatin Immunoprecipitation Sequencing Humans Deep Learning Genomics
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Biohybrid soft robots with self-stimulating skeletons.

Science robotics Apr 21, 2021
Bioinspired hybrid soft robots that combine living and synthetic components are an emerging field in the development of advanced actuators and other robotic platforms (i.e., swimmers, crawlers, and walkers). The integration of biological components o...
Swimming Tissue Scaffolds Biomimetic Materials Mechanical Phenomena Finite Element Analysis Cell Line Smart Materials Muscle, Skeletal Motion Animals Printing, Three-Dimensional Biomimetics Mice Robotics Artificial Organs Equipment Design
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CODEX, a neural network approach to explore signaling dynamics landscapes.

Molecular systems biology Apr 1, 2021
Current studies of cell signaling dynamics that use live cell fluorescent biosensors routinely yield thousands of single-cell, heterogeneous, multi-dimensional trajectories. Typically, the extraction of relevant information from time series data reli...
Drosophila Radiation, Ionizing Extracellular Signal-Regulated MAP Kinases Databases as Topic Movement Dose-Response Relationship, Radiation Intercellular Signaling Peptides and Proteins Tumor Suppressor Protein p53 Signal Transduction Machine Learning Cell Line Algorithms Fluorescent Dyes Animals Humans Proto-Oncogene Proteins c-akt Neural Networks, Computer Transforming Growth Factor beta Light
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SilencerDB: a comprehensive database of silencers.

Nucleic acids research Jan 8, 2021
Gene regulatory elements, including promoters, enhancers, silencers, etc., control transcriptional programs in a spatiotemporal manner. Though these elements are known to be able to induce either positive or negative transcriptional control, the comm...
Transcription, Genetic Sus scrofa Animals Databases, Nucleic Acid Chickens Drosophila melanogaster Silencer Elements, Transcriptional Internet User-Computer Interface Molecular Sequence Annotation Rats Buffaloes Humans Mice Cell Line Machine Learning Organ Specificity
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