AIMC Topic: Drosophila melanogaster
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DeepEthogram, a machine learning pipeline for supervised behavior classification from raw pixels.
eLife
Sep 2, 2021
Videos of animal behavior are used to quantify researcher-defined behaviors of interest to study neural function, gene mutations, and pharmacological therapies. Behaviors of interest are often scored manually, which is time-consuming, limited to few ...
Identification of conserved transcriptome features between humans and Drosophila in the aging brain utilizing machine learning on combined data from the NIH Sequence Read Archive.
PloS one
Aug 11, 2021
Aging is universal, yet characterizing the molecular changes that occur in aging which lead to an increased risk for neurological disease remains a challenging problem. Aging affects the prefrontal cortex (PFC), which governs executive function, lear...
LiftPose3D, a deep learning-based approach for transforming two-dimensional to three-dimensional poses in laboratory animals.
Nature methods
Aug 5, 2021
Markerless three-dimensional (3D) pose estimation has become an indispensable tool for kinematic studies of laboratory animals. Most current methods recover 3D poses by multi-view triangulation of deep network-based two-dimensional (2D) pose estimate...
Deep learning connects DNA traces to transcription to reveal predictive features beyond enhancer-promoter contact.
Nature communications
Jun 8, 2021
Chromatin architecture plays an important role in gene regulation. Recent advances in super-resolution microscopy have made it possible to measure chromatin 3D structure and transcription in thousands of single cells. However, leveraging these comple...
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...
Regression plane concept for analysing continuous cellular processes with machine learning.
Nature communications
May 5, 2021
Biological processes are inherently continuous, and the chance of phenotypic discovery is significantly restricted by discretising them. Using multi-parametric active regression we introduce the Regression Plane (RP), a user-friendly discovery tool e...
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...
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...
Deep learning suggests that gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure.
Nature communications
Dec 1, 2020
Understanding the genetic regulatory code governing gene expression is an important challenge in molecular biology. However, how individual coding and non-coding regions of the gene regulatory structure interact and contribute to mRNA expression leve...
GODoc: high-throughput protein function prediction using novel k-nearest-neighbor and voting algorithms.
BMC bioinformatics
Nov 18, 2020
BACKGROUND: Biological data has grown explosively with the advance of next-generation sequencing. However, annotating protein function with wet lab experiments is time-consuming. Fortunately, computational function prediction can help wet labs formul...
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