AI Medical Compendium Topic

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HybridSucc: A Hybrid-learning Architecture for General and Species-specific Succinylation Site Prediction.

Genomics, proteomics & bioinformatics
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...

Boosting phosphorylation site prediction with sequence feature-based machine learning.

Proteins
Protein phosphorylation is one of the essential posttranslation modifications playing a vital role in the regulation of many fundamental cellular processes. We propose a LightGBM-based computational approach that uses evolutionary, geometric, sequenc...

Mapping Atlantic rainforest degradation and regeneration history with indicator species using convolutional network.

PloS one
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
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
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...

Artificial intelligence reveals environmental constraints on colour diversity in insects.

Nature communications
Explaining colour variation among animals at broad geographic scales remains challenging. Here we demonstrate how deep learning-a form of artificial intelligence-can reveal subtle but robust patterns of colour feature variation along an ecological gr...

scGen predicts single-cell perturbation responses.

Nature methods
Accurately modeling cellular response to perturbations is a central goal of computational biology. While such modeling has been based on statistical, mechanistic and machine learning models in specific settings, no generalization of predictions to ph...

DeepMRMP: A new predictor for multiple types of RNA modification sites using deep learning.

Mathematical biosciences and engineering : MBE
RNA modification plays an indispensable role in the regulation of organisms. RNA modification site prediction offers an insight into diverse cellular processing. Regarding different types of RNA modification site prediction, it is difficult to tell t...

Insights and approaches using deep learning to classify wildlife.

Scientific reports
The implementation of intelligent software to identify and classify objects and individuals in visual fields is a technology of growing importance to operatives in many fields, including wildlife conservation and management. To non-experts, the metho...

Prediction and functional analysis of prokaryote lysine acetylation site by incorporating six types of features into Chou's general PseAAC.

Journal of theoretical biology
Lysine acetylation is one of the most important types of protein post-translational modifications (PTM) that are widely involved in cellular regulatory processes. To fully understand the regulatory mechanism of acetylation, identification of acetylat...