AIMC Topic: Computational Biology

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Machine learning builds full-QM precision protein force fields in seconds.

Briefings in bioinformatics
Full-quantum mechanics (QM) calculations are extraordinarily precise but difficult to apply to large systems, such as biomolecules. Motivated by the massive demand for efficient calculations for large systems at the full-QM level and by the significa...

An artificial neural network model based on DNA damage response genes to predict outcomes of lower-grade glioma patients.

Briefings in bioinformatics
Although the prognosis of lower-grade glioma (LGG) patients is better than others, outcomes are highly heterogeneous. Isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion status can identify patient subsets with different prognosis. However,...

A machine learning framework to predict antibiotic resistance traits and yet unknown genes underlying resistance to specific antibiotics in bacterial strains.

Briefings in bioinformatics
Recently, the frequency of observing bacterial strains without known genetic components underlying phenotypic resistance to antibiotics has increased. There are several strains of bacteria lacking known resistance genes; however, they demonstrate res...

nhKcr: a new bioinformatics tool for predicting crotonylation sites on human nonhistone proteins based on deep learning.

Briefings in bioinformatics
Lysine crotonylation (Kcr) is a newly discovered type of protein post-translational modification and has been reported to be involved in various pathophysiological processes. High-resolution mass spectrometry is the primary approach for identificatio...

NeuroPred-FRL: an interpretable prediction model for identifying neuropeptide using feature representation learning.

Briefings in bioinformatics
Neuropeptides (NPs) are the most versatile neurotransmitters in the immune systems that regulate various central anxious hormones. An efficient and effective bioinformatics tool for rapid and accurate large-scale identification of NPs is critical in ...

Multi-view Multichannel Attention Graph Convolutional Network for miRNA-disease association prediction.

Briefings in bioinformatics
MOTIVATION: In recent years, a growing number of studies have proved that microRNAs (miRNAs) play significant roles in the development of human complex diseases. Discovering the associations between miRNAs and diseases has become an important part of...

EMNUSS: a deep learning framework for secondary structure annotation in cryo-EM maps.

Briefings in bioinformatics
Cryo-electron microscopy (cryo-EM) has become one of important experimental methods in structure determination. However, despite the rapid growth in the number of deposited cryo-EM maps motivated by advances in microscopy instruments and image proces...

SSI-DDI: substructure-substructure interactions for drug-drug interaction prediction.

Briefings in bioinformatics
A major concern with co-administration of different drugs is the high risk of interference between their mechanisms of action, known as adverse drug-drug interactions (DDIs), which can cause serious injuries to the organism. Although several computat...

Deciphering enhancer sequence using thermodynamics-based models and convolutional neural networks.

Nucleic acids research
Deciphering the sequence-function relationship encoded in enhancers holds the key to interpreting non-coding variants and understanding mechanisms of transcriptomic variation. Several quantitative models exist for predicting enhancer function and und...

Avoiding a replication crisis in deep-learning-based bioimage analysis.

Nature methods
Deep learning algorithms are powerful tools to analyse, restore and transform bioimaging data, increasingly used in life sciences research. These approaches now outperform most other algorithms for a broad range of image analysis tasks. In particular...