Recent advances in machine learning-based models for prediction of antiviral peptides
Viruses have killed and infected millions of people across the world. It causes several
chronic diseases like COVID-19, HIV, and hepatitis. To cope with such diseases and virus …
chronic diseases like COVID-19, HIV, and hepatitis. To cope with such diseases and virus …
Comparative analysis of the existing methods for prediction of antifreeze proteins
Antifreeze proteins (AFPs) are found in different living organisms like plants, insects, and
fish. AFPs avoid the formation of ice crystals in these organisms and make them able to …
fish. AFPs avoid the formation of ice crystals in these organisms and make them able to …
Deepstacked-AVPs: predicting antiviral peptides using tri-segment evolutionary profile and word embedding based multi-perspective features with deep stacking …
Background Viral infections have been the main health issue in the last decade. Antiviral
peptides (AVPs) are a subclass of antimicrobial peptides (AMPs) with substantial potential to …
peptides (AVPs) are a subclass of antimicrobial peptides (AMPs) with substantial potential to …
AFP-SPTS: an accurate prediction of antifreeze proteins using sequential and pseudo-tri-slicing evolutionary features with an extremely randomized tree
The development of intracellular ice in the bodies of cold-blooded living organisms may
cause them to die. These species yield antifreeze proteins (AFPs) to live in subzero …
cause them to die. These species yield antifreeze proteins (AFPs) to live in subzero …
BiCaps-DBP: Predicting DNA-binding proteins from protein sequences using Bi-LSTM and a 1D-capsule network
MKN Mursalim, TLER Mengko, R Hertadi… - Computers in Biology …, 2023 - Elsevier
Predicting DNA-binding proteins (DBPs) based solely on primary sequences is one of the
most challenging problems in genome annotation. DBPs play a crucial role in various …
most challenging problems in genome annotation. DBPs play a crucial role in various …
DBP-DeepCNN: prediction of DNA-binding proteins using wavelet-based denoising and deep learning
DNA-binding proteins (DBPs) are highly concerned with several types of cancers (lung,
breast, and liver), other fatal diseases (AIDS/HIV, asthma), and are used in the designing of …
breast, and liver), other fatal diseases (AIDS/HIV, asthma), and are used in the designing of …
Target-DBPPred: an intelligent model for prediction of DNA-binding proteins using discrete wavelet transform based compression and light eXtreme gradient boosting
DNA-protein interaction is a critical biological process that performs influential activities,
including DNA transcription and recombination. DBPs (DNA-binding proteins) are closely …
including DNA transcription and recombination. DBPs (DNA-binding proteins) are closely …
Prediction of antifreeze proteins using machine learning
Living organisms including fishes, microbes, and animals can live in extremely cold weather.
To stay alive in cold environments, these species generate antifreeze proteins (AFPs), also …
To stay alive in cold environments, these species generate antifreeze proteins (AFPs), also …
Deep-AGP: Prediction of angiogenic protein by integrating two-dimensional convolutional neural network with discrete cosine transform
Angiogenic proteins (AGPs) play a primary role in the formation of new blood vessels from
pre-existing ones. AGPs have diverse applications in cancer, including serving as …
pre-existing ones. AGPs have diverse applications in cancer, including serving as …
Deep-GHBP: improving prediction of Growth Hormone-binding proteins using deep learning model
Growth hormone-binding proteins (GHBPs) are carrier proteins that interact with other
growth hormone proteins in a selective and non-covalent fashion. GHBPs perform significant …
growth hormone proteins in a selective and non-covalent fashion. GHBPs perform significant …