Peptides

Ionmob: a Python package for prediction of peptide collisional cross-section values.

Bioinformatics (Oxford, England) Sep 2, 2023

MOTIVATION: Including ion mobility separation (IMS) into mass spectrometry proteomics experiments is useful to improve coverage and throughput. Many IMS devices enable linking experimentally derived mobility of an ion to its collisional cross-section...

Machine Learning Peptides Amino Acid Sequence Ions Mass Spectrometry Proteomics

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MITNet: a fusion transformer and convolutional neural network architecture approach for T-cell epitope prediction.

Briefings in bioinformatics Jul 20, 2023

Classifying epitopes is essential since they can be applied in various fields, including therapeutics, diagnostics and peptide-based vaccines. To determine the epitope or peptide against an antibody, epitope mapping with peptides is the most extensiv...

Neural Networks, Computer Peptides Receptors, Antigen, T-Cell Epitopes, T-Lymphocyte Amino Acid Sequence

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TCRmodel2: high-resolution modeling of T cell receptor recognition using deep learning.

Nucleic acids research Jul 5, 2023

The cellular immune system, which is a critical component of human immunity, uses T cell receptors (TCRs) to recognize antigenic proteins in the form of peptides presented by major histocompatibility complex (MHC) proteins. Accurate definition of the...

Peptides Antigens Receptors, Antigen, T-Cell Deep Learning Humans Computer Simulation

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Deep learning-based multi-functional therapeutic peptides prediction with a multi-label focal dice loss function.

Bioinformatics (Oxford, England) Jun 1, 2023

MOTIVATION: With the great number of peptide sequences produced in the postgenomic era, it is highly desirable to identify the various functions of therapeutic peptides quickly. Furthermore, it is a great challenge to predict accurate multi-functiona...

Deep Learning Peptides Humans Software Neural Networks, Computer

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UniDL4BioPep: a universal deep learning architecture for binary classification in peptide bioactivity.

Briefings in bioinformatics May 19, 2023

Identification of potent peptides through model prediction can reduce benchwork in wet experiments. However, the conventional process of model buildings can be complex and time consuming due to challenges such as peptide representation, feature selec...

Software Deep Learning Neural Networks, Computer Amino Acid Sequence Peptides

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PreTP-2L: identification of therapeutic peptides and their types using two-layer ensemble learning framework.

Bioinformatics (Oxford, England) Apr 3, 2023

MOTIVATION: Therapeutic peptides play an important role in immune regulation. Recently various therapeutic peptides have been used in the field of medical research, and have great potential in the design of therapeutic schedules. Therefore, it is ess...

Amino Acid Sequence Peptides Machine Learning

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ExamPle: explainable deep learning framework for the prediction of plant small secreted peptides.

Bioinformatics (Oxford, England) Mar 1, 2023

MOTIVATION: Plant Small Secreted Peptides (SSPs) play an important role in plant growth, development, and plant-microbe interactions. Therefore, the identification of SSPs is essential for revealing the functional mechanisms. Over the last few decade...

Peptides Machine Learning Amino Acids Deep Learning

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Prediction of anticancer peptides based on an ensemble model of deep learning and machine learning using ordinal positional encoding.

Briefings in bioinformatics Jan 19, 2023

Anticancer peptides (ACPs) are the types of peptides that have been demonstrated to have anticancer activities. Using ACPs to prevent cancer could be a viable alternative to conventional cancer treatments because they are safer and display higher sel...

Algorithms Peptides Machine Learning Deep Learning Neural Networks, Computer

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HelixGAN a deep-learning methodology for conditional de novo design of α-helix structures.

Bioinformatics (Oxford, England) Jan 1, 2023

MOTIVATION: Protein and peptide engineering has become an essential field in biomedicine with therapeutics, diagnostics and synthetic biology applications. Helices are both abundant structural feature in proteins and comprise a major portion of bioac...

Protein Structure, Secondary Deep Learning Proteins Peptides Protein Conformation, alpha-Helical

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Deep learning of protein sequence design of protein-protein interactions.

Bioinformatics (Oxford, England) Jan 1, 2023

MOTIVATION: As more data of experimentally determined protein structures are becoming available, data-driven models to describe protein sequence-structure relationships become more feasible. Within this space, the amino acid sequence design of protei...

Amino Acid Sequence Deep Learning Software Proteins Peptides Algorithms

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AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Ionmob: a Python package for prediction of peptide collisional cross-section values.

MITNet: a fusion transformer and convolutional neural network architecture approach for T-cell epitope prediction.

TCRmodel2: high-resolution modeling of T cell receptor recognition using deep learning.

Deep learning-based multi-functional therapeutic peptides prediction with a multi-label focal dice loss function.

UniDL4BioPep: a universal deep learning architecture for binary classification in peptide bioactivity.

PreTP-2L: identification of therapeutic peptides and their types using two-layer ensemble learning framework.

ExamPle: explainable deep learning framework for the prediction of plant small secreted peptides.

Prediction of anticancer peptides based on an ensemble model of deep learning and machine learning using ordinal positional encoding.

HelixGAN a deep-learning methodology for conditional de novo design of α-helix structures.

Deep learning of protein sequence design of protein-protein interactions.

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