AIMC Topic: Mutation

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Insight into deep learning for glioma IDH medical image analysis: A systematic review.

Medicine
BACKGROUND: Deep learning techniques explain the enormous potential of medical image analysis, particularly in digital pathology. Concurrently, molecular markers have gained increasing significance over the past decade in the context of glioma patien...

Discovering genotype-phenotype relationships with machine learning and the Visual Physiology Opsin Database (VPOD).

GigaScience
BACKGROUND: Predicting phenotypes from genetic variation is foundational for fields as diverse as bioengineering and global change biology, highlighting the importance of efficient methods to predict gene functions. Linking genetic changes to phenoty...

Machine Learning-Based Pathomics Model to Predict the Prognosis in Clear Cell Renal Cell Carcinoma.

Technology in cancer research & treatment
Clear cell renal cell carcinoma (ccRCC) is a highly lethal urinary malignancy with poor overall survival (OS) rates. Integrating computer vision and machine learning in pathomics analysis offers potential for enhancing classification, prognosis, and ...

ETLD: an encoder-transformation layer-decoder architecture for protein contact and mutation effects prediction.

Briefings in bioinformatics
The latent features extracted from the multiple sequence alignments (MSAs) of homologous protein families are useful for identifying residue-residue contacts, predicting mutation effects, shaping protein evolution, etc. Over the past three decades, a...

Deep learning-based activity recognition and fine motor identification using 2D skeletons of cynomolgus monkeys.

Zoological research
Video-based action recognition is becoming a vital tool in clinical research and neuroscientific study for disorder detection and prediction. However, action recognition currently used in non-human primate (NHP) research relies heavily on intense man...

Learning protein fitness landscapes with deep mutational scanning data from multiple sources.

Cell systems
One of the key points of machine learning-assisted directed evolution (MLDE) is the accurate learning of the fitness landscape, a conceptual mapping from sequence variants to the desired function. Here, we describe a multi-protein training scheme tha...

DDMut: predicting effects of mutations on protein stability using deep learning.

Nucleic acids research
Understanding the effects of mutations on protein stability is crucial for variant interpretation and prioritisation, protein engineering, and biotechnology. Despite significant efforts, community assessments of predictive tools have highlighted ongo...

DeepAlloDriver: a deep learning-based strategy to predict cancer driver mutations.

Nucleic acids research
Driver mutations can contribute to the initial processes of cancer, and their identification is crucial for understanding tumorigenesis as well as for molecular drug discovery and development. Allostery regulates protein function away from the functi...

Mutate and observe: utilizing deep neural networks to investigate the impact of mutations on translation initiation.

Bioinformatics (Oxford, England)
MOTIVATION: The primary regulatory step for protein synthesis is translation initiation, which makes it one of the fundamental steps in the central dogma of molecular biology. In recent years, a number of approaches relying on deep neural networks (D...