AIMC Topic: Mutation
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Editorial for "A Deep Learning Model Based on MRI and Clinical Factors Facilitates Noninvasive Prediction of KRAS Mutation in Rectal Cancer".
Journal of magnetic resonance imaging : JMRI
May 16, 2022
The future of artificial intelligence in digital pathology - results of a survey across stakeholder groups.
Histopathology
May 11, 2022
AIMS: Artificial intelligence (AI) provides a powerful tool to extract information from digitised histopathology whole slide images. During the last 5 years, academic and commercial actors have developed new technical solutions for a diverse set of t...
CancerVar: An artificial intelligence-empowered platform for clinical interpretation of somatic mutations in cancer.
Science advances
May 6, 2022
Several knowledgebases are manually curated to support clinical interpretations of thousands of hotspot somatic mutations in cancer. However, discrepancies or even conflicting interpretations are observed among these databases. Furthermore, many prev...
Classification of non-coding variants with high pathogenic impact.
PLoS genetics
Apr 29, 2022
Whole genome sequencing is increasingly used to diagnose medical conditions of genetic origin. While both coding and non-coding DNA variants contribute to a wide range of diseases, most patients who receive a WGS-based diagnosis today harbour a prote...
Artificial intelligence to identify genetic alterations in conventional histopathology.
The Journal of pathology
Apr 21, 2022
Precision oncology relies on the identification of targetable molecular alterations in tumor tissues. In many tumor types, a limited set of molecular tests is currently part of standard diagnostic workflows. However, universal testing for all targeta...
MAVE-NN: learning genotype-phenotype maps from multiplex assays of variant effect.
Genome biology
Apr 15, 2022
Multiplex assays of variant effect (MAVEs) are a family of methods that includes deep mutational scanning experiments on proteins and massively parallel reporter assays on gene regulatory sequences. Despite their increasing popularity, a general stra...
Robots as models of evolving systems.
Proceedings of the National Academy of Sciences of the United States of America
Mar 17, 2022
Experimental robobiological physics can bring insights into biological evolution. We present a development of hybrid analog/digital autonomous robots with mutable diploid dominant/recessive 6-byte genomes. The robots are capable of death, rebirth, an...
DrABC: deep learning accurately predicts germline pathogenic mutation status in breast cancer patients based on phenotype data.
Genome medicine
Feb 25, 2022
BACKGROUND: Identifying breast cancer patients with DNA repair pathway-related germline pathogenic variants (GPVs) is important for effectively employing systemic treatment strategies and risk-reducing interventions. However, current criteria and ris...
Machine Learning Predictability of Clinical Next Generation Sequencing for Hematologic Malignancies to Guide High-Value Precision Medicine.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Feb 21, 2022
Advancing diagnostic testing capabilities such as clinical next generation sequencing methods offer the potential to diagnose, risk stratify, and guide specialized treatment, but must be balanced against the escalating costs of healthcare to identify...
Accurate and rapid prediction of tuberculosis drug resistance from genome sequence data using traditional machine learning algorithms and CNN.
Scientific reports
Feb 14, 2022
Effective and timely antibiotic treatment depends on accurate and rapid in silico antimicrobial-resistant (AMR) predictions. Existing statistical rule-based Mycobacterium tuberculosis (MTB) drug resistance prediction methods using bacterial genomic s...
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