AIMC Topic: MCF-7 Cells

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Predicting target proteins for drug candidate compounds based on drug-induced gene expression data in a chemical structure-independent manner.

BMC medical genomics
BACKGROUND: Phenotype-based high-throughput screening is a useful technique for identifying drug candidate compounds that have a desired phenotype. However, the molecular mechanisms of the hit compounds remain unknown, and substantial effort is requi...

Inferring Association between Compound and Pathway with an Improved Ensemble Learning Method.

Molecular informatics
Emergence of compound molecular data coupled to pathway information offers the possibility of using machine learning methods for compound-pathway associations' inference. To provide insights into the global relationship between compounds and their af...

Cytotoxic response of platinum-coated gold nanorods in human breast cancer cells at very low exposure levels.

Environmental toxicology
Because of unique optical behavior gold nanorods (GNRs) have attracted attention for the application in biomedical field such as bio-sensing, bio-imaging and hyperthermia. However, toxicological response of GNRs is controversial due to their differen...

Incorporating time as a third dimension in transcriptomic analysis using machine learning and explainable AI.

Computational biology and chemistry
Transcriptomic data analysis entails the measurement of RNA transcript (gene expression products) abundance in a cell or a cell population at a single point in time. In other words, transcriptomics as it is currently practiced is two-dimensional (2DT...

Droplet microfluidics integrated with machine learning reveals how adipose-derived stem cells modulate endocrine response and tumor heterogeneity in ER breast cancer.

Lab on a chip
Approximately 70% of breast cancer (BC) diagnoses are estrogen receptor positive (ER) with ∼40% of ER BC patients presenting resistance to endocrine therapy (ET). Recent studies identify the tumor microenvironment (TME) as having a key role in endoc...

Dissecting Exosomal-Tumoral-Vascular Interactions of Single Tumor Cells and Clusters Using a Tumoral-Transendothelial Migration Chip.

ACS nano
The complex interplay between tumor cells and clusters with endothelial tissues during metastasis, in particular with regard to the exosomes in mediating intercellular communication, is still not well understood. Here, we develop a tumoral-transendot...

Scientific hypothesis generation by large language models: laboratory validation in breast cancer treatment.

Journal of the Royal Society, Interface
Large language models (LLMs) have transformed artificial intelligence (AI) and achieved breakthrough performance on a wide range of tasks. In science, the most interesting application of LLMs is for hypothesis formation. A feature of LLMs, which resu...

Deep learning-based classification of breast cancer cells using transmembrane receptor dynamics.

Bioinformatics (Oxford, England)
MOTIVATION: Motions of transmembrane receptors on cancer cell surfaces can reveal biophysical features of the cancer cells, thus providing a method for characterizing cancer cell phenotypes. While conventional analysis of receptor motions in the cell...

A sequence-based deep learning approach to predict CTCF-mediated chromatin loop.

Briefings in bioinformatics
Three-dimensional (3D) architecture of the chromosomes is of crucial importance for transcription regulation and DNA replication. Various high-throughput chromosome conformation capture-based methods have revealed that CTCF-mediated chromatin loops a...

Locating transcription factor binding sites by fully convolutional neural network.

Briefings in bioinformatics
Transcription factors (TFs) play an important role in regulating gene expression, thus identification of the regions bound by them has become a fundamental step for molecular and cellular biology. In recent years, an increasing number of deep learnin...