AIMC Topic: Cell Lineage
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Using deep learning for quantification of cellularity and cell lineages in bone marrow biopsies and comparison to normal age-related variation.
Pathology
Nov 10, 2021
Cellularity estimation forms an important aspect of the visual examination of bone marrow biopsies. In clinical practice, cellularity is estimated by eye under a microscope, which is rapid, but subjective and subject to inter- and intraobserver varia...
Establishment of a morphological atlas of the Caenorhabditis elegans embryo using deep-learning-based 4D segmentation.
Nature communications
Dec 7, 2020
The invariant development and transparent body of the nematode Caenorhabditis elegans enables complete delineation of cell lineages throughout development. Despite extensive studies of cell division, cell migration and cell fate differentiation, cell...
Prognostic Significance of Immune Cell Populations Identified by Machine Learning in Colorectal Cancer Using Routine Hematoxylin and Eosin-Stained Sections.
Clinical cancer research : an official journal of the American Association for Cancer Research
May 21, 2020
PURPOSE: Although high T-cell density is a well-established favorable prognostic factor in colorectal cancer, the prognostic significance of tumor-associated plasma cells, neutrophils, and eosinophils is less well-defined.
Deciphering epigenomic code for cell differentiation using deep learning.
BMC genomics
Sep 12, 2019
BACKGROUND: Although DNA sequence plays a crucial role in establishing the unique epigenome of a cell type, little is known about the sequence determinants that lead to the unique epigenomes of different cell types produced during cell differentiatio...
Robotic platform for microinjection into single cells in brain tissue.
EMBO reports
Aug 30, 2019
Microinjection into single cells in brain tissue is a powerful technique to study and manipulate neural stem cells. However, such microinjection requires expertise and is a low-throughput process. We developed the "Autoinjector", a robot that utilize...
Automatic recognition of different types of acute leukaemia in peripheral blood by image analysis.
Journal of clinical pathology
Jun 29, 2019
AIMS: Morphological differentiation among different blast cell lineages is a difficult task and there is a lack of automated analysers able to recognise these abnormal cells. This study aims to develop a machine learning approach to predict the diagn...
Tracing cell lineages in videos of lens-free microscopy.
Medical image analysis
Jun 5, 2018
In vitro experiments with cultured cells are essential for studying their growth and migration pattern and thus, for gaining a better understanding of cancer progression and its treatment. Recent progress in lens-free microscopy (LFM) has rendered it...
Prospective identification of hematopoietic lineage choice by deep learning.
Nature methods
Feb 20, 2017
Differentiation alters molecular properties of stem and progenitor cells, leading to changes in their shape and movement characteristics. We present a deep neural network that prospectively predicts lineage choice in differentiating primary hematopoi...
Basic fibroblast growth factor is critical to reprogramming buffalo (Bubalus bubalis) primordial germ cells into embryonic germ stem cell-like cells.
Theriogenology
Dec 29, 2016
Primordial germ cells (PGCs) are destined to form gametes in vivo, and they can be reprogrammed into pluripotent embryonic germ (EG) cells in vitro. Buffalo PGC have been reported to be reprogrammed into EG-like cells, but the identities of the major...
Predicting transcription factor site occupancy using DNA sequence intrinsic and cell-type specific chromatin features.
BMC bioinformatics
Jan 11, 2016
BACKGROUND: Understanding the mechanisms by which transcription factors (TF) are recruited to their physiological target sites is crucial for understanding gene regulation. DNA sequence intrinsic features such as predicted binding affinity are often ...
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