Accelerating Prediction of Antiviral Peptides Using Genetic Algorithm-Based Weighted Multiperspective Descriptors with Self-Normalized Deep Networks.

The accurate prediction of antiviral peptides (AVPs) plays a crucial role in accelerating the development of peptide-based therapeutics. Despite extensive production of antiviral medications, viral diseases remain a major human health concern. AVPs have emerged as potential candidates for the development of novel antiviral drugs.

DeepAIPs-SFLA: Deep Convolutional Model for Prediction of Anti-Inflammatory Peptides Using Binary Pattern Decomposition of Novel Multiview Descriptors with an SFLA Approach.

Inflammation is a vital biological response of the human immune system to harmful stimuli, and it plays a vital role in tissue repair and pathogen elimination. However, chronic inflammation can lead to severe diseases such as arthritis, cancer, cardiovascular disorders, and autoimmune conditions. Anti-inflammatory peptides (AIPs) have emerged as promising therapeutic agents owing to their high selectivity, potency toward target cells, and minimal side effects.

JCMM Annual Review on Advances in Biotechnology for the Treatment of Haematological Malignancies: A Review of the Latest In-Patient Developments 2024-2025.

Advances in biotechnology are set to revolutionise the treatment of haematological malignancies. Current treatments have limited efficacy due to their high toxicity and poor long-term outcomes, particularly in acute myeloid leukaemia (AML). In 2024-2025, emerging targeted therapies promise improved results with reduced adverse effects.

pNPs-CapsNet: Predicting Neuropeptides Using Protein Language Models and FastText Encoding-Based Weighted Multi-View Feature Integration with Deep Capsule Neural Network.

Neuropeptides (NPs) are critical signaling molecules that are essential in numerous physiological processes and possess significant therapeutic potential. Computational prediction of NPs has emerged as a promising alternative to traditional experimental methods, often labor-intensive, time-consuming, and expensive. Recent advancements in computational peptide models provide a cost-effective approach to identifying NPs, characterized by high selectivity toward target cells and minimal side effects.

pACPs-DNN: Predicting anticancer peptides using novel peptide transformation into evolutionary and structure matrix-based images with self-attention deep learning model.

Globally, cancer remains a major health challenge due to its high mortality rates. Traditional experimental approaches and therapies are resource-intensive and often cause significant side effects. Anticancer peptides (ACPs) have emerged as alternative therapeutic agents owing to their selectivity, safety, and potential to mitigate drug resistance.

TargetCLP: clathrin proteins prediction combining transformed and evolutionary scale modeling-based multi-view features via weighted feature integration approach.

Clathrin proteins, key elements of the vesicle coat, play a crucial role in various cellular processes, including neural function, signal transduction, and endocytosis. Disruptions in clathrin protein functions have been associated with a wide range of diseases, such as Alzheimer's, neurodegeneration, viral infection, and cancer. Therefore, correctly identifying clathrin protein functions is critical to unravel the mechanism of these fatal diseases and designing drug targets.

pACP-HybDeep: predicting anticancer peptides using binary tree growth based transformer and structural feature encoding with deep-hybrid learning.

Worldwide, Cancer remains a significant health concern due to its high mortality rates. Despite numerous traditional therapies and wet-laboratory methods for treating cancer-affected cells, these approaches often face limitations, including high costs and substantial side effects. Recently the high selectivity of peptides has garnered significant attention from scientists due to their reliable targeted actions and minimal adverse effects.

Catalog of the Neuropterida of India (Insecta: Neuroptera, Megaloptera, Raphidioptera).

A catalog of the species and subspecies of Neuropterida (Insecta: Neuroptera, Megaloptera, and Raphidioptera) occurring in modern India is presented, based primarily on an extensive review of relevant literature published through mid 2024. The currently known extant fauna comprises 3 orders, 15 families, 129 genera, 368 species, and 6 subspecies (Neuroptera: 11 families, 119 genera, 332 species; Megaloptera: 2 families, 8 genera, 31 species; Raphidioptera: 2 families, 2 genera, 5 species). In addition, one extinct species is known, †Spiloconis eominuta (Neuroptera: Coniopterygidae).

DeepAIPs-Pred: Predicting Anti-Inflammatory Peptides Using Local Evolutionary Transformation Images and Structural Embedding-Based Optimal Descriptors with Self-Normalized BiTCNs.

Inflammation is a biological response to harmful stimuli, playing a crucial role in facilitating tissue repair by eradicating pathogenic microorganisms. However, when inflammation becomes chronic, it leads to numerous serious disorders, particularly in autoimmune diseases. Anti-inflammatory peptides (AIPs) have emerged as promising therapeutic agents due to their high specificity, potency, and low toxicity.

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 oxidative stress and perform a significant role in many antioxidant-based drugs. The current invitro-based medications are costly, time-consuming, and unable to efficiently screen and identify the targeted motif of antioxidant proteins.

DeepAVP-TPPred: identification of antiviral peptides using transformed image-based localized descriptors and binary tree growth algorithm.

Motivation: Despite the extensive manufacturing of antiviral drugs and vaccination, viral infections continue to be a major human ailment. Antiviral peptides (AVPs) have emerged as potential candidates in the pursuit of novel antiviral drugs. These peptides show vigorous antiviral activity against a diverse range of viruses by targeting different phases of the viral life cycle.

iAFPs-Mv-BiTCN: Predicting antifungal peptides using self-attention transformer embedding and transform evolutionary based multi-view features with bidirectional temporal convolutional networks.

Globally, fungal infections have become a major health concern in humans. Fungal diseases generally occur due to the invading fungus appearing on a specific portion of the body and becoming hard for the human immune system to resist. The recent emergence of COVID-19 has intensely increased different nosocomial fungal infections.

Deepstacked-AVPs: predicting antiviral peptides using tri-segment evolutionary profile and word embedding based multi-perspective features with deep stacking model.

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 protect the human body against various viral diseases. However, there has been significant production of antiviral vaccines and medications.

AIPs-SnTCN: Predicting Anti-Inflammatory Peptides Using fastText and Transformer Encoder-Based Hybrid Word Embedding with Self-Normalized Temporal Convolutional Networks.

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 micro-organisms or irritants and facilitate tissue repair. Prolonged inflammation can result in chronic inflammatory diseases.

NUC-7738 regulates β-catenin signalling resulting in reduced proliferation and self-renewal of AML cells.

Acute myeloid leukemia (AML) stem cells are required for the initiation and maintenance of the disease. Activation of the Wnt/β-catenin pathway is required for the survival and development of AML leukaemia stem cells (LSCs) and therefore, targeting β-catenin is a potential therapeutic strategy. NUC-7738, a phosphoramidate transformation of 3'-deoxyadenosine (3'-dA) monophosphate, is specifically designed to generate the active anti-cancer metabolite 3'-deoxyadenosine triphosphate (3'-dATP) intracellularly, bypassing key limitations of breakdown, transport, and activation.

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 therapies and wet laboratory-based methods have been applied to treat cancer cells. However, these methods are considered less effective due to their high cost and diverse side effects.

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 environments, some cold-blooded species produced Antifreeze proteins (AFPs), also called ice-binding proteins. AFPs are not only limited to the medical field but also have diverse significance in the area of biotechnology, agriculture, and the food industry.

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 strategies for developing novel alternative therapies have been presented. The effectiveness and dependability of these procedures are not always consistent.

First record of the myrmicine ant genus Syllophopsis Santschi, 1915 (Hymenoptera: Formicidae) from India with description of a new species.

Here we describe and illustrate Syllophopsis peetersi sp. nov. from Silent Valley National Park, a biodiversity hotspot region of the Western Ghats of India.

The Ants (Hymenoptera, Formicidae) of Sri Lanka: a taxonomic research summary and updated checklist.

An updated checklist of the ants (Hymenoptera: Formicidae) of Sri Lanka is presented. These include representatives of eleven of the 17 known extant subfamilies with 341 valid ant species in 79 genera. Mayr, 1879 is reported as a new species country record for Sri Lanka.

DP-BINDER: machine learning model for prediction of DNA-binding proteins by fusing evolutionary and physicochemical information.

DNA-binding proteins (DBPs) participate in various biological processes including DNA replication, recombination, and repair. In the human genome, about 6-7% of these proteins are utilized for genes encoding. DBPs shape the DNA into a compact structure known chromatin while some of these proteins regulate the chromosome packaging and transcription process.

A new species of the ant genus Roger, 1861 (Hymenoptera: Formicidae) from India.

Background: There are no well defined species groups based on the worker morphology from the Oriental region Arimoto (2017). forms a complex species group with closely related species having little morphological changes Wilson (1958), Sarnat and Economo (2012). From the Oriental region, there are currently 9 species belonging to the group.

iMethyl-STTNC: Identification of N-methyladenosine sites by extending the idea of SAAC into Chou's PseAAC to formulate RNA sequences.

N- methyladenosine (mA) is a vital post-transcriptional modification, which adds another layer of epigenetic regulation at RNA level. It chemically modifies mRNA that effects protein expression. RNA sequence contains many genetic code motifs (GAC).