AIMC Topic: Caenorhabditis elegans
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Cross-Predicting Essential Genes between Two Model Eukaryotic Species Using Machine Learning.
International journal of molecular sciences
May 11, 2021
Experimental studies of and have contributed substantially to our understanding of molecular and cellular processes in metazoans at large. Since the publication of their genomes, functional genomic investigations have identified genes that are esse...
WormPose: Image synthesis and convolutional networks for pose estimation in C. elegans.
PLoS computational biology
Apr 27, 2021
An important model system for understanding genes, neurons and behavior, the nematode worm C. elegans naturally moves through a variety of complex postures, for which estimation from video data is challenging. We introduce an open-source Python packa...
Modeling transcriptional regulation of model species with deep learning.
Genome research
Apr 22, 2021
To enable large-scale analyses of transcription regulation in model species, we developed DeepArk, a set of deep learning models of the -regulatory activities for four widely studied species: , , , and DeepArk accurately predicts the presence of tho...
3DeeCellTracker, a deep learning-based pipeline for segmenting and tracking cells in 3D time lapse images.
eLife
Mar 30, 2021
Despite recent improvements in microscope technologies, segmenting and tracking cells in three-dimensional time-lapse images (3D + T images) to extract their dynamic positions and activities remains a considerable bottleneck in the field. We develope...
Deep learning the collisional cross sections of the peptide universe from a million experimental values.
Nature communications
Feb 19, 2021
The size and shape of peptide ions in the gas phase are an under-explored dimension for mass spectrometry-based proteomics. To investigate the nature and utility of the peptide collisional cross section (CCS) space, we measure more than a million dat...
Real-time volumetric reconstruction of biological dynamics with light-field microscopy and deep learning.
Nature methods
Feb 11, 2021
Light-field microscopy has emerged as a technique of choice for high-speed volumetric imaging of fast biological processes. However, artifacts, nonuniform resolution and a slow reconstruction speed have limited its full capabilities for in toto extra...
SSnet: A Deep Learning Approach for Protein-Ligand Interaction Prediction.
International journal of molecular sciences
Jan 30, 2021
Computational prediction of Protein-Ligand Interaction (PLI) is an important step in the modern drug discovery pipeline as it mitigates the cost, time, and resources required to screen novel therapeutics. Deep Neural Networks (DNN) have recently show...
Improving skeleton algorithm for helping Caenorhabditis elegans trackers.
Scientific reports
Dec 17, 2020
One of the main problems when monitoring Caenorhabditis elegans nematodes (C. elegans) is tracking their poses by automatic computer vision systems. This is a challenge given the marked flexibility that their bodies present and the different poses th...
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...
Identifying longevity associated genes by integrating gene expression and curated annotations.
PLoS computational biology
Nov 30, 2020
Aging is a complex process with poorly understood genetic mechanisms. Recent studies have sought to classify genes as pro-longevity or anti-longevity using a variety of machine learning algorithms. However, it is not clear which types of features are...
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