AIMC Topic: Species Specificity
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HybridSucc: A Hybrid-learning Architecture for General and Species-specific Succinylation Site Prediction.
Genomics, proteomics & bioinformatics
Aug 28, 2020
As an important protein acylation modification, lysine succinylation (Ksucc) is involved in diverse biological processes, and participates in human tumorigenesis. Here, we collected 26,243 non-redundant known Ksucc sites from 13 species as the benchm...
Experimentally revealed stochastic preferences for multicomponent choice options.
Journal of experimental psychology. Animal learning and cognition
Jul 27, 2020
Realistic, everyday rewards contain multiple components. An apple has taste and size. However, we choose in single dimensions, simply preferring some apples to others. How can such single-dimensional preference relationships refer to multicomponent c...
Cross-species regulatory sequence activity prediction.
PLoS computational biology
Jul 20, 2020
Machine learning algorithms trained to predict the regulatory activity of nucleic acid sequences have revealed principles of gene regulation and guided genetic variation analysis. While the human genome has been extensively annotated and studied, mod...
Genome, Human
Machine Learning
Mice
Models, Genetic
Genomics
Mutation
Species Specificity
Humans
Software
Databases, Genetic
Quantitative Trait Loci
Computational Biology
Hepatocytes
Algorithms
Neural Networks, Computer
Sequence Analysis, DNA
Epigenomics
Genetic Variation
Animals
Models, Statistical
Gene Expression Regulation
A supervised learning framework for chromatin loop detection in genome-wide contact maps.
Nature communications
Jul 9, 2020
Accurately predicting chromatin loops from genome-wide interaction matrices such as Hi-C data is critical to deepening our understanding of proper gene regulation. Current approaches are mainly focused on searching for statistically enriched dots on ...
The proteome landscape of the kingdoms of life.
Nature
Jun 17, 2020
Proteins carry out the vast majority of functions in all biological domains, but for technological reasons their large-scale investigation has lagged behind the study of genomes. Since the first essentially complete eukaryotic proteome was reported, ...
Accurate prediction of species-specific 2-hydroxyisobutyrylation sites based on machine learning frameworks.
Analytical biochemistry
May 28, 2020
Lysine 2-hydroxyisobutyrylation (K) is a newly discovered post-translational modification (PTM) across eukaryotes and prokaryotes in recent years, which plays a significant role in diverse cellular functions. Accurate prediction of K sites is a first...
Deep learning based prediction of species-specific protein S-glutathionylation sites.
Biochimica et biophysica acta. Proteins and proteomics
Mar 29, 2020
As a widespread and reversible post-translational modification of proteins, S-glutathionylation specifically generates the mixed disulfides between cysteine residues and glutathione, which regulates various biological processes including oxidative st...
Mapping Atlantic rainforest degradation and regeneration history with indicator species using convolutional network.
PloS one
Feb 28, 2020
The Atlantic rainforest of Brazil is one of the global terrestrial hotspots of biodiversity. Despite having undergone large scale deforestation, forest cover has shown signs of increases in the last decades. Here, to understand the degradation and re...
Towards a global understanding of the drivers of marine and terrestrial biodiversity.
PloS one
Feb 5, 2020
Understanding the distribution of life's variety has driven naturalists and scientists for centuries, yet this has been constrained both by the available data and the models needed for their analysis. Here we compiled data for over 67,000 marine and ...
DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput.
Nature methods
Nov 25, 2019
We present an easy-to-use integrated software suite, DIA-NN, that exploits deep neural networks and new quantification and signal correction strategies for the processing of data-independent acquisition (DIA) proteomics experiments. DIA-NN improves t...
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