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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 …
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 …
ToxinPred 3.0: An improved method for predicting the toxicity of peptides
Toxicity emerges as a prominent challenge in the design of therapeutic peptides, causing
the failure of numerous peptides during clinical trials. In 2013, our group developed …
the failure of numerous peptides during clinical trials. In 2013, our group developed …
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 …
Optimizing gene selection and cancer classification with hybrid sine cosine and cuckoo search algorithm
Gene expression datasets offer a wide range of information about various biological
processes. However, it is difficult to find the important genes among the high-dimensional …
processes. However, it is difficult to find the important genes among the high-dimensional …
PAtbP-EnC: Identifying anti-tubercular peptides using multi-feature representation and genetic algorithm-based deep ensemble model
Mycobacterium tuberculosis, a highly perilous pathogen in humans, serves as the causative
agent of tuberculosis (TB), affecting nearly 33% of the global population. With the increasing …
agent of tuberculosis (TB), affecting nearly 33% of the global population. With the increasing …
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 …
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 …
Raman spectroscopy and AI applications in cancer grading. An overview
Raman spectroscopy (RS) is a label-free molecular vibrational spectroscopy technique that
is able to identify the molecular fingerprint of various samples making use of the inelastic …
is able to identify the molecular fingerprint of various samples making use of the inelastic …