AIMC Topic: Antibodies

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Low-data interpretable deep learning prediction of antibody viscosity using a biophysically meaningful representation.

Scientific reports Feb 20, 2023
Deep learning, aided by the availability of big data sets, has led to substantial advances across many disciplines. However, many scientific problems of practical interest lack sufficiently large datasets amenable to deep learning. Prediction of anti...
Antibodies Viscosity Immunoglobulin Variable Region Deep Learning Big Data
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Smart Graphene-Based Electrochemical Nanobiosensor for Clinical Diagnosis: Review.

Sensors (Basel, Switzerland) Feb 16, 2023
The technological improvement in the field of physics, chemistry, electronics, nanotechnology, biology, and molecular biology has contributed to the development of various electrochemical biosensors with a broad range of applications in healthcare se...
Graphite Electricity Electric Conductivity Artificial Intelligence Antibodies
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Computational and artificial intelligence-based methods for antibody development.

Trends in pharmacological sciences Jan 18, 2023
Due to their high target specificity and binding affinity, therapeutic antibodies are currently the largest class of biotherapeutics. The traditional largely empirical antibody development process is, while mature and robust, cumbersome and has signi...
Humans Artificial Intelligence Antibodies Complementarity Determining Regions Machine Learning
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AbAgIntPre: A deep learning method for predicting antibody-antigen interactions based on sequence information.

Frontiers in immunology Dec 22, 2022
INTRODUCTION: Antibody-mediated immunity is an essential part of the immune system in vertebrates. The ability to specifically bind to antigens allows antibodies to be widely used in the therapy of cancers and other critical diseases. A key step in a...
Amino Acid Sequence Antigens Deep Learning Antibodies Neural Networks, Computer Animals
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NanoNet: Rapid and accurate end-to-end nanobody modeling by deep learning.

Frontiers in immunology Aug 12, 2022
Antibodies are a rapidly growing class of therapeutics. Recently, single domain camelid VHH antibodies, and their recognition nanobody domain (Nb) appeared as a cost-effective highly stable alternative to full-length antibodies. There is a growing ne...
Single-Domain Antibodies Complementarity Determining Regions Antibodies Amino Acid Sequence Deep Learning
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Simultaneous phenotyping of five Rh red blood cell antigens on a paper-based analytical device combined with deep learning for rapid and accurate interpretation.

Analytica chimica acta Apr 5, 2022
Both the ABO and Rhesus (Rh) blood groups play crucial roles in blood transfusion medicine. Herein, we report a simple and low-cost paper-based analytical device (PAD) for phenotyping red blood cell (RBC) antigens. Using this Rh typing format, 5 Rh a...
Deep Learning Antibodies Rh-Hr Blood-Group System Erythrocytes Antigens Blood Group Antigens
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Leveraging Sequential and Spatial Neighbors Information by Using CNNs Linked With GCNs for Paratope Prediction.

IEEE/ACM transactions on computational biology and bioinformatics Feb 3, 2022
Antibodies consisting of variable and constant regions, are a special type of proteins playing a vital role in immune system of the vertebrate. They have the remarkable ability to bind a large range of diverse antigens with extraordinary affinity and...
Neural Networks, Computer Algorithms Binding Sites, Antibody Proteins Antibodies
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Machine learning analyses of antibody somatic mutations predict immunoglobulin light chain toxicity.

Nature communications Jun 10, 2021
In systemic light chain amyloidosis (AL), pathogenic monoclonal immunoglobulin light chains (LC) form toxic aggregates and amyloid fibrils in target organs. Prompt diagnosis is crucial to avoid permanent organ damage, but delayed diagnosis is common ...
Humans Algorithms Animals Machine Learning Antibodies Models, Molecular Recombinant Proteins Amino Acid Sequence Gene Expression Immunoglobulin Light Chains Databases, Genetic Mutation Caenorhabditis elegans Immunoglobulin Light-chain Amyloidosis
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Protein design and variant prediction using autoregressive generative models.

Nature communications Apr 23, 2021
The ability to design functional sequences and predict effects of variation is central to protein engineering and biotherapeutics. State-of-art computational methods rely on models that leverage evolutionary information but are inadequate for importa...
Protein Engineering Humans Neural Networks, Computer Amino Acid Sequence Genotype Mutation Antigens Algorithms Computational Biology Antibodies Phenotype Proteins
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Using the antibody-antigen binding interface to train image-based deep neural networks for antibody-epitope classification.

PLoS computational biology Mar 29, 2021
High-throughput B-cell sequencing has opened up new avenues for investigating complex mechanisms underlying our adaptive immune response. These technological advances drive data generation and the need to mine and analyze the information contained in...
Antibodies, Viral Virus Diseases Deep Learning Humans Binding Sites, Antibody Computational Biology Image Processing, Computer-Assisted Antibodies Models, Molecular Neural Networks, Computer Epitopes
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