AI Medical Compendium Topic

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Mutation

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A machine learning enhanced EMS mutagenesis probability map for efficient identification of causal mutations in Caenorhabditis elegans.

PLoS genetics
Chemical mutagenesis-driven forward genetic screens are pivotal in unveiling gene functions, yet identifying causal mutations behind phenotypes remains laborious, hindering their high-throughput application. Here, we reveal a non-uniform mutation rat...

High-Throughput and Integrated CRISPR/Cas12a-Based Molecular Diagnosis Using a Deep Learning Enabled Microfluidic System.

ACS nano
CRISPR/Cas-based molecular diagnosis demonstrates potent potential for sensitive and rapid pathogen detection, notably in SARS-CoV-2 diagnosis and mutation tracking. Yet, a major hurdle hindering widespread practical use is its restricted throughput,...

Comprehensive Potential of Artificial Intelligence for Predicting PD-L1 Expression and EGFR Mutations in Lung Cancer: A Systematic Review and Meta-Analysis.

Journal of computer assisted tomography
OBJECTIVE: To evaluate the methodological quality and the predictive performance of artificial intelligence (AI) for predicting programmed death ligand 1 (PD-L1) expression and epidermal growth factor receptors (EGFR) mutations in lung cancer (LC) ba...

Machine learning-guided multi-site combinatorial mutagenesis enhances the thermostability of pectin lyase.

International journal of biological macromolecules
Enhancing the thermostability of enzymes is crucial for industrial applications. Methods such as directed evolution are often limited by the huge sequence space and combinatorial explosion, making it difficult to obtain optimal mutants. In recent yea...

Integrating knowledge graphs into machine learning models for survival prediction and biomarker discovery in patients with non-small-cell lung cancer.

Journal of translational medicine
Accurate survival prediction for Non-Small Cell Lung Cancer (NSCLC) patients remains a significant challenge for the scientific and clinical community despite decades of advanced analytics. Addressing this challenge not only helps inform the critical...

Deep mutational scanning and machine learning for the analysis of antimicrobial-peptide features driving membrane selectivity.

Nature biomedical engineering
Many antimicrobial peptides directly disrupt bacterial membranes yet can also damage mammalian membranes. It is therefore central to their therapeutic use that rules governing the membrane selectivity of antimicrobial peptides be deciphered. However,...

A stemness-based signature with inspiring indications in discriminating the prognosis, immune response, and somatic mutation of endometrial cancer patients revealed by machine learning.

Aging
Endometrial cancer (EC) is a fatal gynecologic tumor. Bioinformatic tools are increasingly developed to screen out molecular targets related to EC. Our study aimed to identify stemness-related prognostic biomarkers for new therapeutic strategies in E...

Using deep learning to decipher the impact of telomerase promoter mutations on the dynamic metastatic morpholome.

PLoS computational biology
Melanoma showcases a complex interplay of genetic alterations and intra- and inter-cellular morphological changes during metastatic transformation. While pivotal, the role of specific mutations in dictating these changes still needs to be fully eluci...

Machine learning-guided co-optimization of fitness and diversity facilitates combinatorial library design in enzyme engineering.

Nature communications
The effective design of combinatorial libraries to balance fitness and diversity facilitates the engineering of useful enzyme functions, particularly those that are poorly characterized or unknown in biology. We introduce MODIFY, a machine learning (...