AIMC Topic: HeLa Cells

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Design and optimization of tamarind seed polysaccharide-based scaffold for tissue engineering applications using statistical modeling and machine learning, and it's in-vitro validation.

International journal of biological macromolecules
This study explores the development and optimization of a novel biomaterial scaffold for tissue engineering, composed of Tamarind seed polysaccharide (TSP), Hydroxypropyl methylcellulose (HPMC), Chitosan (CS), and Sodium alginate (ALG). Scaffold prop...

Deep Learning-Assisted Label-Free Parallel Cell Sorting with Digital Microfluidics.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Sorting specific cells from heterogeneous samples is important for research and clinical applications. In this work, a novel label-free cell sorting method is presented that integrates deep learning image recognition with microfluidic manipulation to...

Deep learning-based segmentation of subcellular organelles in high-resolution phase-contrast images.

Cell structure and function
Although quantitative analysis of biological images demands precise extraction of specific organelles or cells, it remains challenging in broad-field grayscale images, where traditional thresholding methods have been hampered due to complex image fea...

Nm-Nano: a machine learning framework for transcriptome-wide single-molecule mapping of 2´-O-methylation (Nm) sites in nanopore direct RNA sequencing datasets.

RNA biology
2´-O-methylation (Nm) is one of the most abundant modifications found in both mRNAs and noncoding RNAs. It contributes to many biological processes, such as the normal functioning of tRNA, the protection of mRNA against degradation by the decapping a...

Predicting Transcription Factor Binding Sites with Deep Learning.

International journal of molecular sciences
Prediction of binding sites for transcription factors is important to understand how the latter regulate gene expression and how this regulation can be modulated for therapeutic purposes. A consistent number of references address this issue with diff...

Refinement of paramagnetic bead-based digestion protocol for automatic sample preparation using an artificial neural network.

Talanta
Despite technological advances in the proteomics field, sample preparation still represents the main bottleneck in mass spectrometry (MS) analysis. Bead-based protein aggregation techniques have recently emerged as an efficient, reproducible, and hig...

Automated segmentation of cell organelles in volume electron microscopy using deep learning.

Microscopy research and technique
Recent advances in computing power triggered the use of artificial intelligence in image analysis in life sciences. To train these algorithms, a large enough set of certified labeled data is required. The trained neural network is then capable of pro...

Mass spectrometry-based proteomics data from thousands of HeLa control samples.

Scientific data
Here we provide a curated, large scale, label free mass spectrometry-based proteomics data set derived from HeLa cell lines for general purpose machine learning and analysis. Data access and filtering is a tedious task, which takes up considerable am...

Quantification of golgi dispersal and classification using machine learning models.

Micron (Oxford, England : 1993)
The Golgi body is a critical organelle in eukaryotic cells responsible for processing and modifying proteins and lipids. Under certain conditions, such as stress, disease, or ageing, the Golgi structure alters. Therefore, understanding the mechanisms...