Recent progress in the discovery and design of antimicrobial peptides using traditional machine learning and deep learning
Antimicrobial resistance has become a critical global health problem due to the abuse of
conventional antibiotics and the rise of multi-drug-resistant microbes. Antimicrobial peptides …
conventional antibiotics and the rise of multi-drug-resistant microbes. Antimicrobial peptides …
Deep learning tools to accelerate antibiotic discovery
Introduction As machine learning (ML) and artificial intelligence (AI) expand to many
segments of our society, they are increasingly being used for drug discovery. Recent deep …
segments of our society, they are increasingly being used for drug discovery. Recent deep …
AIPs-SnTCN: Predicting anti-inflammatory peptides using fastText and transformer encoder-based hybrid word embedding with self-normalized temporal …
Inflammation is a biologically resistant response to harmful stimuli, such as infection,
damaged cells, toxic chemicals, or tissue injuries. Its purpose is to eradicate pathogenic …
damaged cells, toxic chemicals, or tissue injuries. Its purpose is to eradicate pathogenic …
cACP-DeepGram: classification of anticancer peptides via deep neural network and skip-gram-based word embedding model
Cancer is a Toxic health concern worldwide, it happens when cellular modifications cause
the irregular growth and division of human cells. Several traditional approaches such as …
the irregular growth and division of human cells. Several traditional approaches such as …
iAFPs-EnC-GA: identifying antifungal peptides using sequential and evolutionary descriptors based multi-information fusion and ensemble learning approach
Fungal infections have become a serious health concern for human beings worldwide.
Fungal infections usually occur when the invading fungus appear on a particular part of the …
Fungal infections usually occur when the invading fungus appear on a particular part of the …
iAtbP-Hyb-EnC: Prediction of antitubercular peptides via heterogeneous feature representation and genetic algorithm based ensemble learning model
Tuberculosis (TB) is a worldwide illness caused by the bacteria Mycobacterium tuberculosis.
Owing to the high prevalence of multidrug-resistant tuberculosis, numerous traditional …
Owing to the high prevalence of multidrug-resistant tuberculosis, numerous traditional …
StackedEnC-AOP: prediction of antioxidant proteins using transform evolutionary and sequential features based multi-scale vector with stacked ensemble learning
Background Antioxidant proteins are involved in several biological processes and can
protect DNA and cells from the damage of free radicals. These proteins regulate the body's …
protect DNA and cells from the damage of free radicals. These proteins regulate the body's …
AFP-CMBPred: Computational identification of antifreeze proteins by extending consensus sequences into multi-blocks evolutionary information
In extremely cold environments, living organisms like plants, animals, fishes, and microbes
can die due to the intracellular ice formation in their bodies. To sustain life in such cold …
can die due to the intracellular ice formation in their bodies. To sustain life in such cold …
Deep-AntiFP: Prediction of antifungal peptides using distanct multi-informative features incorporating with deep neural networks
World widely, Fungal infections have become a serious issue for human beings. Fungal
infections normally happen once invading fungus appear on a specific area of the body and …
infections normally happen once invading fungus appear on a specific area of the body and …
iHBP-DeepPSSM: Identifying hormone binding proteins using PsePSSM based evolutionary features and deep learning approach
Hormone binding proteins (HBPs) are soluble carrier proteins that can non-covalently and
selectively interact with the human hormone. HBPs plays a significant role in human life, but …
selectively interact with the human hormone. HBPs plays a significant role in human life, but …