AIMC Topic: Mutation

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Informed training set design enables efficient machine learning-assisted directed protein evolution.

Cell systems Aug 19, 2021
Directed evolution of proteins often involves a greedy optimization in which the mutation in the highest-fitness variant identified in each round of single-site mutagenesis is fixed. The efficiency of such a single-step greedy walk depends on the ord...
Mutation Machine Learning Mutagenesis Proteins
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Comparative analysis of machine learning approaches to classify tumor mutation burden in lung adenocarcinoma using histopathology images.

Scientific reports Aug 16, 2021
Both histologic subtypes and tumor mutation burden (TMB) represent important biomarkers in lung cancer, with implications for patient prognosis and treatment decisions. Typically, TMB is evaluated by comprehensive genomic profiling but this requires ...
Carcinoma, Non-Small-Cell Lung Age Factors Female Area Under Curve Aged Mutation Smoking Aged, 80 and over Coloring Agents ROC Curve Adenocarcinoma of Lung Eosine Yellowish-(YS) Sex Factors Lung Neoplasms Middle Aged Hematoxylin Staining and Labeling Deep Learning Adult Datasets as Topic Male Humans
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Predicting mutant outcome by combining deep mutational scanning and machine learning.

Proteins Jul 31, 2021
Deep mutational scanning provides unprecedented wealth of quantitative data regarding the functional outcome of mutations in proteins. A single experiment may measure properties (eg, structural stability) of numerous protein variants. Leveraging the ...
High-Throughput Nucleotide Sequencing DNA Mutational Analysis Machine Learning Algorithms Protein Interaction Maps Computational Biology Proteins Models, Statistical Mutation
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In silico saturation mutagenesis of cancer genes.

Nature Jul 28, 2021
Despite the existence of good catalogues of cancer genes, identifying the specific mutations of those genes that drive tumorigenesis across tumour types is still a largely unsolved problem. As a result, most mutations identified in cancer genes acros...
Humans Machine Learning Reproducibility of Results Neoplasms Computer Simulation Probability Precision Medicine Mutagenesis Models, Genetic Organ Specificity Mutation Oncogenes Cell Transformation, Neoplastic
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Gene Mutation Classification through Text Evidence Facilitating Cancer Tumour Detection.

Journal of healthcare engineering Jul 27, 2021
A cancer tumour consists of thousands of genetic mutations. Even after advancement in technology, the task of distinguishing genetic mutations, which act as driver for the growth of tumour with passengers (Neutral Genetic Mutations), is still being d...
Humans Neural Networks, Computer Machine Learning Neoplasms Natural Language Processing Mutation
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The impact of site-specific digital histology signatures on deep learning model accuracy and bias.

Nature communications Jul 20, 2021
The Cancer Genome Atlas (TCGA) is one of the largest biorepositories of digital histology. Deep learning (DL) models have been trained on TCGA to predict numerous features directly from histology, including survival, gene expression patterns, and dri...
Specimen Handling Gene Expression Profiling Deep Learning Image Processing, Computer-Assisted Data Accuracy DNA Mutational Analysis Neoplasm Staging Biomarkers, Tumor Mutation Risk Assessment Neoplasms Humans
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Predicting HIV drug resistance using weighted machine learning method at target protein sequence-level.

Molecular diversity Jul 9, 2021
Acquired immune deficiency syndrome (AIDS) is a fatal disease caused by human immunodeficiency virus (HIV). Although 23 different drugs have been available, the treatment of AIDS remains challenging because the virus mutates very quickly which can le...
Algorithms Databases, Factual Reproducibility of Results Anti-HIV Agents Humans Machine Learning Mutation HIV Amino Acid Sequence Drug Resistance, Viral Models, Theoretical Support Vector Machine Dose-Response Relationship, Drug Viral Proteins
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Gene Position Index Mutation Detection Algorithm Based on Feedback Fast Learning Neural Network.

Computational intelligence and neuroscience Jul 6, 2021
In the detection of genome variation, the research on the internal correlation of reference genome is deepening; the detection of variation in genome sequence has become the focus of research, and it has also become an effective path to find new gene...
Neural Networks, Computer Software Algorithms Feedback Mutation Sequence Analysis, DNA Gene Order
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Characterizing the Clinical Features and Atrophy Patterns of -Related Frontotemporal Dementia With Disease Progression Modeling.

Neurology Jun 22, 2021
BACKGROUND AND OBJECTIVE: Mutations in the gene cause frontotemporal dementia (FTD). Most previous studies investigating the neuroanatomical signature of mutations have grouped all different mutations together and shown an association with focal at...
tau Proteins Female Aged Atrophy Adult Mutation Male Middle Aged Frontotemporal Dementia Humans Image Interpretation, Computer-Assisted Disease Progression Machine Learning Neuroimaging Magnetic Resonance Imaging Brain
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PANDA: Predicting the change in proteins binding affinity upon mutations by finding a signal in primary structures.

Journal of bioinformatics and computational biology Jun 11, 2021
Accurately determining a change in protein binding affinity upon mutations is important to find novel therapeutics and to assist mutagenesis studies. Determination of change in binding affinity upon mutations requires sophisticated, expensive, and ti...
Amino Acid Sequence Protein Binding Proteins Mutation Machine Learning
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