AIMC Topic: Cell Line

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Cellular level robotic surgery: Nanodissection of intermediate filaments in live keratinocytes.

Nanomedicine : nanotechnology, biology, and medicine
We present the nanosurgery on the cytoskeleton of live cells using AFM based nanorobotics to achieve adhesiolysis and mimic the effect of pathophysiological modulation of intercellular adhesion. Nanosurgery successfully severs the intermediate filame...

Robotic adherent cell injection for characterizing cell-cell communication.

IEEE transactions on bio-medical engineering
Compared to robotic injection of suspended cells (e.g., embryos and oocytes), fewer attempts were made to automate the injection of adherent cells (e.g., cancer cells and cardiomyocytes) due to their smaller size, highly irregular morphology, small t...

Machine learning-assisted design of immunomodulatory lipid nanoparticles for delivery of mRNA to repolarize hyperactivated microglia.

Drug delivery
Regulating inflammatory microglia presents a promising strategy for treating neurodegenerative and autoimmune disorders, yet effective therapeutic agents delivery to these cells remains a challenge. This study investigates modified lipid nanoparticle...

BOLA3 as a key protein for the treatment of diabetic skeletal muscle atrophy.

International immunopharmacology
OBJECTIVE: Skeletal muscle is crucial for glucose metabolism, but diabetes impairs this function, leading to muscle atrophy. Although the mutation of BOLA Family Member 3 (BOLA3) resulted in disease Multiple Mitochondrial Dysfunctions Syndrome, the r...

MIRACN: a residual convolutional neural network for predicting cell line specific functional regulatory variants.

Briefings in bioinformatics
In post-genome-wide association study era, interpretation of noncoding variants remains a significant challenge due to their complexity and the limited understanding of their functions. Here, we developed MIRACN, a novel residual convolutional neural...

Improving drug response prediction via integrating gene relationships with deep learning.

Briefings in bioinformatics
Predicting the drug response of cancer cell lines is crucial for advancing personalized cancer treatment, yet remains challenging due to tumor heterogeneity and individual diversity. In this study, we present a deep learning-based framework named Dee...

Unveiling human origins of replication using deep learning: accurate prediction and comprehensive analysis.

Briefings in bioinformatics
Accurate identification of replication origins (ORIs) is crucial for a comprehensive investigation into the progression of human cell growth and cancer therapy. Here, we proposed a computational approach Ori-FinderH, which can efficiently and precise...

DeepICSH: a complex deep learning framework for identifying cell-specific silencers and their strength from the human genome.

Briefings in bioinformatics
Silencers are noncoding DNA sequence fragments located on the genome that suppress gene expression. The variation of silencers in specific cells is closely related to gene expression and cancer development. Computational approaches that exclusively r...

MSDRP: a deep learning model based on multisource data for predicting drug response.

Bioinformatics (Oxford, England)
MOTIVATION: Cancer heterogeneity drastically affects cancer therapeutic outcomes. Predicting drug response in vitro is expected to help formulate personalized therapy regimens. In recent years, several computational models based on machine learning a...

Automatic recognition of protein subcellular location patterns in single cells from immunofluorescence images based on deep learning.

Briefings in bioinformatics
With the improvement of single-cell measurement techniques, there is a growing awareness that individual differences exist among cells, and protein expression distribution can vary across cells in the same tissue or cell line. Pinpointing the protein...