AIMC Topic: Computational Biology

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A bi-layer model for identification of piwiRNA using deep neural learning.

Journal of biomolecular structure & dynamics
piwiRNA is a kind of non-coding RNA (ncRNA) that cannot be translated into proteins. It helps in understanding the study of gametes generation and regulation of gene expression over both transcriptional and post-transcriptional levels. piwiRNA has th...

Predicting transcriptional outcomes of novel multigene perturbations with GEARS.

Nature biotechnology
Understanding cellular responses to genetic perturbation is central to numerous biomedical applications, from identifying genetic interactions involved in cancer to developing methods for regenerative medicine. However, the combinatorial explosion in...

The current role and evolution of X-ray crystallography in drug discovery and development.

Expert opinion on drug discovery
INTRODUCTION: Macromolecular X-ray crystallography and cryo-EM are currently the primary techniques used to determine the three-dimensional structures of proteins, nucleic acids, and viruses. Structural information has been critical to drug discovery...

Unraveling the Molecular Puzzle: Exploring Gene Networks across Diverse EMT Status of Cell Lines.

International journal of molecular sciences
Understanding complex disease mechanisms requires a comprehensive understanding of the gene regulatory networks, as complex diseases are often characterized by the dysregulation and dysfunction of molecular networks, rather than abnormalities in sing...

The seductive allure of spatial biology: accelerating new discoveries in the life sciences.

Immunology and cell biology
Spatial biology is a rapidly developing field which enables the visualization of protein and transcriptomic data while preserving tissue context and architecture. Initially used in discovery, there is growing promise for translational and diagnostic ...

Estimating protein complex model accuracy based on ultrafast shape recognition and deep learning in CASP15.

Proteins
This article reports and analyzes the results of protein complex model accuracy estimation by our methods (DeepUMQA3 and GraphGPSM) in the 15th Critical Assessment of techniques for protein Structure Prediction (CASP15). The new deep learning-based m...

Cross-protein transfer learning substantially improves disease variant prediction.

Genome biology
BACKGROUND: Genetic variation in the human genome is a major determinant of individual disease risk, but the vast majority of missense variants have unknown etiological effects. Here, we present a robust learning framework for leveraging saturation m...

Computational Methods in Immunology and Vaccinology: Design and Development of Antibodies and Immunogens.

Journal of chemical theory and computation
The design of new biomolecules able to harness immune mechanisms for the treatment of diseases is a prime challenge for computational and simulative approaches. For instance, in recent years, antibodies have emerged as an important class of therapeut...

AIVariant: a deep learning-based somatic variant detector for highly contaminated tumor samples.

Experimental & molecular medicine
The detection of somatic DNA variants in tumor samples with low tumor purity or sequencing depth remains a daunting challenge despite numerous attempts to address this problem. In this study, we constructed a substantially extended set of actual posi...