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 …
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 …
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 …
Machine learning-based model for accurate identification of druggable proteins using light extreme gradient boosting
The identification of druggable proteins (DPs) is significant for the development of new
drugs, personalized medicine, understanding of disease mechanisms, drug repurposing …
drugs, personalized medicine, understanding of disease mechanisms, drug repurposing …
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 …
DeepAIPs-Pred: predicting anti-inflammatory peptides using local evolutionary transformation images and structural embedding-based optimal descriptors with Self …
Inflammation is a biological response to harmful stimuli, playing a crucial role in facilitating
tissue repair by eradicating pathogenic microorganisms. However, when inflammation …
tissue repair by eradicating pathogenic microorganisms. However, when inflammation …
Exploring Integration of Multimodal Deep Learning Approaches for Enhanced Alzheimer's Disease Diagnosis: A Review of Recent Literature
Abstract Alzheimer's disease (AD), is the most common form of dementia that affects the
nervous system. In the past few years, non-invasive early AD diagnosis has become more …
nervous system. In the past few years, non-invasive early AD diagnosis has become more …
Comprehensive Analysis of Computational Models for Prediction of Anticancer Peptides Using Machine Learning and Deep Learning
Anti-cancer peptides (ACPs) represent promising candidates for cancer therapy because
they can target cancer cells selectively while leaving healthy cells unaffected. ACPs offer a …
they can target cancer cells selectively while leaving healthy cells unaffected. ACPs offer a …
A bi-layer model for identification of piwiRNA using deep neural learning
Abstract piwiRNA is a kind of non-coding RNA (ncRNA) that cannot be translated into
proteins. It helps in understanding the study of gametes generation and regulation of gene …
proteins. It helps in understanding the study of gametes generation and regulation of gene …
AE-Net: Novel Autoencoder-Based Deep Features for SQL Injection Attack Detection
Structured Query Language (SQL) injection attacks represent a critical threat to database-
driven applications and systems, exploiting vulnerabilities in input fields to inject malicious …
driven applications and systems, exploiting vulnerabilities in input fields to inject malicious …