AIMC Topic: Cell Line
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A Strictly Unsupervised Deep Learning Method for HEp-2 Cell Image Classification.
Sensors (Basel, Switzerland)
May 9, 2020
Classifying the images that portray the Human Epithelial cells of type 2 (HEp-2) represents one of the most important steps in the diagnosis procedure of autoimmune diseases. Performing this classification manually represents an extremely complicated...
Analysis of Drug Effects on iPSC Cardiomyocytes with Machine Learning.
Annals of biomedical engineering
May 4, 2020
Patient-specific induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) offer an attractive experimental platform to investigate cardiac diseases and therapeutic outcome. In this study, iPSC-CMs were utilized to study their calcium transient...
Robust classification of cell cycle phase and biological feature extraction by image-based deep learning.
Molecular biology of the cell
Apr 22, 2020
Across the cell cycle, the subcellular organization undergoes major spatiotemporal changes that could in principle contain biological features that could potentially represent cell cycle phase. We applied convolutional neural network-based classifier...
A Multi-Omics Interpretable Machine Learning Model Reveals Modes of Action of Small Molecules.
Scientific reports
Jan 22, 2020
High-throughput screening and gene signature analyses frequently identify lead therapeutic compounds with unknown modes of action (MoAs), and the resulting uncertainties can lead to the failure of clinical trials. We developed an approach for uncover...
Biomimetic smoking robot for in vitro inhalation exposure compatible with microfluidic organ chips.
Nature protocols
Jan 10, 2020
Exposure of lung tissues to cigarette smoke is a major cause of human disease and death worldwide. Unfortunately, adequate model systems that can reliably recapitulate disease biogenesis in vitro, including exposure of the human lung airway to fresh ...
Accurate and rapid background estimation in single-molecule localization microscopy using the deep neural network BGnet.
Proceedings of the National Academy of Sciences of the United States of America
Dec 23, 2019
Background fluorescence, especially when it exhibits undesired spatial features, is a primary factor for reduced image quality in optical microscopy. Structured background is particularly detrimental when analyzing single-molecule images for 3-dimens...
Cell Line Classification Using Electric Cell-Substrate Impedance Sensing (ECIS).
The international journal of biostatistics
Dec 5, 2019
We present new methods for cell line classification using multivariate time series bioimpedance data obtained from electric cell-substrate impedance sensing (ECIS) technology. The ECIS technology, which monitors the attachment and spreading of mammal...
CDSeq: A novel complete deconvolution method for dissecting heterogeneous samples using gene expression data.
PLoS computational biology
Dec 2, 2019
Quantifying cell-type proportions and their corresponding gene expression profiles in tissue samples would enhance understanding of the contributions of individual cell types to the physiological states of the tissue. Current approaches that address ...
Fast fit-free analysis of fluorescence lifetime imaging via deep learning.
Proceedings of the National Academy of Sciences of the United States of America
Nov 12, 2019
Fluorescence lifetime imaging (FLI) provides unique quantitative information in biomedical and molecular biology studies but relies on complex data-fitting techniques to derive the quantities of interest. Herein, we propose a fit-free approach in FLI...
AIKYATAN: mapping distal regulatory elements using convolutional learning on GPU.
BMC bioinformatics
Oct 7, 2019
BACKGROUND: The data deluge can leverage sophisticated ML techniques for functionally annotating the regulatory non-coding genome. The challenge lies in selecting the appropriate classifier for the specific functional annotation problem, within the b...
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