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Cell Line

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Simultaneously Detecting Monoamine Oxidase A and B in Disease Cell/Tissue Samples Using Paper-Based Devices.

ACS applied bio materials
As enzymes in the outer membrane of the mitochondrion, monoamine oxidases (MAOs) can catalyze the oxidative deamination of monoamines in the human body. According to different substrates, MAOs can be divided into MAO-A and MAO-B. The imbalance of the...

Machine Learning Models for Predicting Cytotoxicity of Nanomaterials.

Chemical research in toxicology
The wide application of nanomaterials in consumer and medical products has raised concerns about their potential adverse effects on human health. Thus, more and more biological assessments regarding the toxicity of nanomaterials have been performed. ...

Discovering cell types using manifold learning and enhanced visualization of single-cell RNA-Seq data.

Scientific reports
Identifying relevant disease modules such as target cell types is a significant step for studying diseases. High-throughput single-cell RNA-Seq (scRNA-seq) technologies have advanced in recent years, enabling researchers to investigate cells individu...

A pan-CRISPR analysis of mammalian cell specificity identifies ultra-compact sgRNA subsets for genome-scale experiments.

Nature communications
A genetic knockout can be lethal to one human cell type while increasing growth rate in another. This context specificity confounds genetic analysis and prevents reproducible genome engineering. Genome-wide CRISPR compendia across most common human c...

PRODeepSyn: predicting anticancer synergistic drug combinations by embedding cell lines with protein-protein interaction network.

Briefings in bioinformatics
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...

EPI-Mind: Identifying Enhancer-Promoter Interactions Based on Transformer Mechanism.

Interdisciplinary sciences, computational life sciences
MOTIVATION: Enhancer-Promoter Interactions (EPIs) is an essential step in the gene regulation process. However, the detection of EPIs by traditional wet experimental techniques is time-consuming and expensive. Thus, computational methods would be ver...

Deep learning for de-convolution of Smad2 versus Smad3 binding sites.

BMC genomics
BACKGROUND: The transforming growth factor beta-1 (TGF β-1) cytokine exerts both pro-tumor and anti-tumor effects in carcinogenesis. An increasing body of literature suggests that TGF β-1 signaling outcome is partially dependent on the regulatory tar...

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)
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...

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

Endocrine, metabolic & immune disorders drug targets
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, ...

DeepSide: A Deep Learning Approach for Drug Side Effect Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
Drug failures due to unforeseen adverse effects at clinical trials pose health risks for the participants and lead to substantial financial losses. Side effect prediction algorithms have the potential to guide the drug design process. LINCS L1000 dat...