AIMC Topic: Amino Acid Sequence

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HNetGO: protein function prediction via heterogeneous network transformer.

Briefings in bioinformatics
Protein function annotation is one of the most important research topics for revealing the essence of life at molecular level in the post-genome era. Current research shows that integrating multisource data can effectively improve the performance of ...

Sequence-based prediction model of protein crystallization propensity using machine learning and two-level feature selection.

Briefings in bioinformatics
Protein crystallization is crucial for biology, but the steps involved are complex and demanding in terms of external factors and internal structure. To save on experimental costs and time, the tendency of proteins to crystallize can be initially det...

PredLLPS_PSSM: a novel predictor for liquid-liquid protein separation identification based on evolutionary information and a deep neural network.

Briefings in bioinformatics
The formation of biomolecular condensates by liquid-liquid phase separation (LLPS) has become a universal mechanism for spatiotemporal coordination of biological activities in cells and has been widely observed to directly regulate the key cellular p...

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

Bioinformatics (Oxford, England)
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...

ZetaDesign: an end-to-end deep learning method for protein sequence design and side-chain packing.

Briefings in bioinformatics
Computational protein design has been demonstrated to be the most powerful tool in the last few years among protein designing and repacking tasks. In practice, these two tasks are strongly related but often treated separately. Besides, state-of-the-a...

MMSMAPlus: a multi-view multi-scale multi-attention embedding model for protein function prediction.

Briefings in bioinformatics
Protein is the most important component in organisms and plays an indispensable role in life activities. In recent years, a large number of intelligent methods have been proposed to predict protein function. These methods obtain different types of pr...

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

Briefings in bioinformatics
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...

Masked Language Modeling for Resource Constrained Biological Natural Language Processing.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Recent advances in Natural Language Processing (NLP) have produced state of the art results on several sequence to sequence (seq2seq) tasks. Enhancements in embedders and their training methodologies have shown significant improvement on downstream t...

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

Briefings in bioinformatics
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...

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

Bioinformatics (Oxford, England)
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...