AIMC Topic: Mutation

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Deceptive learning in histopathology.

Histopathology Mar 31, 2024
AIMS: Deep learning holds immense potential for histopathology, automating tasks that are simple for expert pathologists and revealing novel biology for tasks that were previously considered difficult or impossible to solve by eye alone. However, the...
Humans Adenocarcinoma of Lung Lung Neoplasms Mutation Neural Networks, Computer Deep Learning
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Self-replicating artificial neural networks give rise to universal evolutionary dynamics.

PLoS computational biology Mar 28, 2024
In evolutionary models, mutations are exogenously introduced by the modeler, rather than endogenously introduced by the replicator itself. We present a new deep-learning based computational model, the self-replicating artificial neural network (SeRAN...
Evolution, Molecular Mutation Mutation Rate Biological Evolution Neural Networks, Computer Genotype
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Flattening the curve-How to get better results with small deep-mutational-scanning datasets.

Proteins Mar 19, 2024
Proteins are used in various biotechnological applications, often requiring the optimization of protein properties by introducing specific amino-acid exchanges. Deep mutational scanning (DMS) is an effective high-throughput method for evaluating the ...
Protein Conformation Databases, Protein Mutation Algorithms Deep Learning Computational Biology Proteins Humans Machine Learning Neural Networks, Computer Software
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Identification of Genomic Signatures for Colorectal Cancer Survival Using Exploratory Data Mining.

International journal of molecular sciences Mar 12, 2024
Clinicopathological presentations are critical for establishing a postoperative treatment regimen in Colorectal Cancer (CRC), although the prognostic value is low in Stage 2 CRC. We implemented a novel exploratory algorithm based on artificial intell...
Gene Expression Regulation, Neoplastic Colorectal Neoplasms Neoplasm Recurrence, Local Mutation Humans Genomics Artificial Intelligence
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DeePNAP: A Deep Learning Method to Predict Protein-Nucleic Acid Binding Affinity from Their Sequences.

Journal of chemical information and modeling Mar 8, 2024
Predicting the protein-nucleic acid (PNA) binding affinity solely from their sequences is of paramount importance for the experimental design and analysis of PNA interactions (PNAIs). A large number of currently developed models for binding affinity ...
Deep Learning Mutation Nucleic Acids Protein Binding Proteins
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Machine learning to predict continuous protein properties from binary cell sorting data and map unseen sequence space.

Proceedings of the National Academy of Sciences of the United States of America Mar 7, 2024
Proteins are a diverse class of biomolecules responsible for wide-ranging cellular functions, from catalyzing reactions to recognizing pathogens. The ability to evolve proteins rapidly and inexpensively toward improved properties is a common objectiv...
Proteins Mutation Protein Engineering Machine Learning Gene Library
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Enhancing Generalizability in Protein-Ligand Binding Affinity Prediction with Multimodal Contrastive Learning.

Journal of chemical information and modeling Mar 5, 2024
Improving the generalization ability of scoring functions remains a major challenge in protein-ligand binding affinity prediction. Many machine learning methods are limited by their reliance on single-modal representations, hindering a comprehensive ...
Ligands Drug Discovery Neural Networks, Computer Mutation Machine Learning
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Identification and segregation of genes with improved recurrent neural network trained with optimal gene level and mutation level features.

Computer methods in biomechanics and biomedical engineering Feb 29, 2024
Even though many different approaches have been employed to address the complex mutational heterogeneity of cancer, finding driver genes is still problematic since other genomic factors cannot be fully integrated for combined analyses. This research ...
Humans Mutation Recurrent Neural Networks Neural Networks, Computer Algorithms
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Protein design using structure-based residue preferences.

Nature communications Feb 22, 2024
Recent developments in protein design rely on large neural networks with up to 100s of millions of parameters, yet it is unclear which residue dependencies are critical for determining protein function. Here, we show that amino acid preferences at in...
Amino Acids Proteins Neural Networks, Computer Mutation
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Inference of annealed protein fitness landscapes with AnnealDCA.

PLoS computational biology Feb 20, 2024
The design of proteins with specific tasks is a major challenge in molecular biology with important diagnostic and therapeutic applications. High-throughput screening methods have been developed to systematically evaluate protein activity, but only a...
Genetic Fitness Mutation Machine Learning Animals Mice
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