CodLncScape Provides a Self‐Enriching Framework for the Systematic Collection and Exploration of Coding LncRNAs

Advanced Science, Journal Year: 2024, Volume and Issue: 11(22)

Published: April 11, 2024

Abstract Recent studies have revealed that numerous lncRNAs can translate proteins under specific conditions, performing diverse biological functions, thus termed coding lncRNAs. Their comprehensive landscape, however, remains elusive due to this field's preliminary and dispersed nature. This study introduces codLncScape, a framework for lncRNA exploration consisting of codLncDB, codLncFlow, codLncWeb, codLncNLP. Specifically, it contains manually compiled knowledge base, encompassing 353 entries validated by experiments. Building upon codLncFlow investigates the expression characteristics these their diagnostic potential in pan‐cancer context, alongside association with spermatogenesis. Furthermore, codLncWeb emerges as platform storing, browsing, accessing concerning within various programming environments. Finally, codLncNLP serves knowledge‐mining tool enhance timely content inclusion updates codLncDB. In summary, offers well‐functioning, content‐rich ecosystem research, aiming accelerate systematic field.

Language: Английский

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PMPred-AE: a computational model for the detection and interpretation of pathological myopia based on artificial intelligence

Frontiers in Medicine, Journal Year: 2025, Volume and Issue: 12

Published: March 13, 2025

Introduction Pathological myopia (PM) is a serious visual impairment that may lead to irreversible damage or even blindness. Timely diagnosis and effective management of PM are great significance. Given the increasing number cases worldwide, there an urgent need develop automated, accurate, highly interpretable diagnostic technology. Methods We proposed computational model called PMPred-AE based on EfficientNetV2-L with attention mechanism optimization. In addition, Gradient-weighted class activation mapping (Grad-CAM) technology was used provide intuitive interpretation for model’s decision-making process. Results The experimental results demonstrated achieved excellent performance in automatically detecting PM, accuracies 98.50, 98.25, 97.25% training, validation, test datasets, respectively. can focus specific areas image when making detection decisions. Discussion developed capable reliably providing accurate detection. Grad-CAM also process model. This approach provides healthcare professionals tool AI

Language: Английский

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Conotoxins: Classification, Prediction, and Future Directions in Bioinformatics

Toxins, Journal Year: 2025, Volume and Issue: 17(2), P. 78 - 78

Published: Feb. 9, 2025

Conotoxins, a diverse family of disulfide-rich peptides derived from the venom Conus species, have gained prominence in biomedical research due to their highly specific interactions with ion channels, receptors, and neurotransmitter systems. Their pharmacological properties make them valuable molecular tools promising candidates for therapeutic development. However, traditional conotoxin classification functional characterization remain labor-intensive, necessitating increasing adoption computational approaches. In particular, machine learning (ML) techniques facilitated advancements sequence-based classification, prediction, de novo peptide design. This review explores recent progress applying ML deep (DL) research, comparing key databases, feature extraction techniques, models. Additionally, we discuss future directions, emphasizing integration multimodal data refinement predictive frameworks enhance discovery.

Language: Английский

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AtML: An Arabidopsis thaliana root cell identity recognition tool for medicinal ingredient accumulation

Methods, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 1, 2024

Arabidopsis thaliana synthesizes various medicinal compounds, and serves as a model plant for research. Single-cell transcriptomics technologies are essential understanding the developmental trajectory of roots, facilitating analysis synthesis accumulation patterns compounds in different cell subpopulations. Although methods interpreting single-cell data rapidly advancing Arabidopsis, challenges remain precisely annotating identity due to lack marker genes certain types. In this work, we trained machine learning system, AtML, using sequencing datasets from six subpopulations, comprising total 6000 cells, predict root stages identify biomarkers through complete interpretability. Performance testing an external dataset revealed that AtML achieved 96.50% accuracy 96.51% recall. Through interpretability provided by our identified 160 important genes, contributing type annotations. conclusion, efficiently stages, providing new tool elucidating mechanisms compound roots.

Language: Английский

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StackAHTPs: An explainable antihypertensive peptides identifier based on heterogeneous features and stacked learning approach

IET Systems Biology, Journal Year: 2025, Volume and Issue: 19(1)

Published: Jan. 1, 2025

Abstract Hypertension, often known as high blood pressure, is a major concern to millions of individuals globally. Recent studies have demonstrated the significant efficacy naturally derived peptides in reducing pressure. Hypertension one risks associated with cardiovascular disorders and other health problems. Naturally sourced bioactive possessing antihypertensive properties provide considerable potential viable substitutes for conventional pharmaceutical medications. Currently, thorough examination peptide (AHTPs), by using traditional wet‐lab methods highly expensive labours. Therefore, in‐silico approaches especially machine‐learning (ML) algorithms are favourable due saving time cost discovery AHTPs. In this study, novel ML‐based predictor, called StackAHTP was developed predicting accurate AHTPs from sequence only. The proposed method, utilise two types feature descriptors Pseudo‐Amino Acid Composition Dipeptide encode local global hidden information sequences. Furthermore, encoded features serially merged ranked through SHapley Additive explanations (SHAP) algorithm. Then, top fed into three different ensemble classifiers (Bagging, Boosting, Stacking) enhancing prediction performance model. StackAHTPs method achieved superior compare ML (AdaBoost, XGBoost Light Gradient Boosting (LightGBM), Bagging Boosting) on 10‐fold cross validation independent test. experimental outcomes demonstrate that our outperformed existing an accuracy 92.25% F1‐score 89.67% test non‐AHTPs. authors believe research will remarkably contribute large‐scale characterisation accelerate drug process. At https://github.com/ali‐ghulam/StackAHTPs you may find datasets used.

Language: Английский

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MultiCycPermea: accurate and interpretable prediction of cyclic peptide permeability using a multimodal image-sequence model

BMC Biology, Journal Year: 2025, Volume and Issue: 23(1)

Published: Feb. 27, 2025

Cyclic peptides, known for their high binding affinity and low toxicity, show potential as innovative drugs targeting "undruggable" proteins. However, therapeutic efficacy is often hindered by poor membrane permeability. Over the past decade, FDA has approved an average of one macrocyclic peptide drug per year, with romidepsin being only intracellular site. Biological experiments to measure permeability are time-consuming labor-intensive. Rapid assessment cyclic crucial development. In this work, we proposed a novel deep learning model, dubbed MultiCycPermea, predicting MultiCycPermea extracts features from both image information (2D structural information) sequence (1D peptides. Additionally, substructure-constrained feature alignment module align two types features. made leap in predictive accuracy. in-distribution setting CycPeptMPDB dataset, reduced mean squared error (MSE) approximately 44.83% compared latest model Multi_CycGT (0.29 vs 0.16). By leveraging visual analysis tools, can reveal relationship between modification structures permeability, providing insights improve provides effective tool that accurately predicts offering valuable improving This work paves new path application artificial intelligence assisting design membrane-permeable

Language: Английский

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Construction of a prognostic risk model for clear cell renal cell carcinomas based on centrosome amplification-related genes

Molecular Genetics and Genomics, Journal Year: 2025, Volume and Issue: 300(1)

Published: March 13, 2025

Clear cell renal carcinoma (ccRCC) is the urological malignancy with highest incidence, centrosome amplification-associated genes (CARGs) have been suggested to be associated carcinogenesis, but their roles in ccRCC are still incompletely understood. This study utilizes bioinformatics explore role of CARGs pathogenesis and establish a prognostic model for related CARGs. Based on publicly available datasets, 2312 differentially expressed (DEGs) were identified (control vs. ccRCC). Disease samples classified into high low scoring groups based CARG scores analysed differences obtain 345 DEGs (S-DEGs). 137 candidate obtained by taking intersection S-DEGs. Six (PCP4, SLN, PI3, PROX1, VAT1L, KLK2) then screened univariate Cox, LASSO, multifactorial Cox regression. These exhibit degree enrichment ribosome-associated pathways. Both risk score age independent factors, Nomogram constructed them had good predictive performance (AUC > 0.7). In addition, immunological analyses 6 different immune cells 23 checkpoints between high- low-risk groups, whereas mutational frequent VHL mutations both groups. Finally, 93 potentially sensitive drugs identified. conclusion, this six as established value. findings provide insights prediction ccRCC, optimisation clinical management development targeted therapeutic strategies.

Language: Английский

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Prediction of lncRNA-miRNA interaction based on sequence and structural information of potential binding site

International Journal of Biological Macromolecules, Journal Year: 2025, Volume and Issue: unknown, P. 142255 - 142255

Published: March 1, 2025

Language: Английский

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Alternative splicing dynamics during gastrulation in mouse embryo

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 30, 2025

Alternative splicing (AS) plays an essential role in development, differentiation and carcinogenesis. However, the mechanisms underlying regulation during mouse embryo gastrulation remain unclear. Based on spatial-temporal transcriptome epigenome data, we detected dynamics of AS revealed its regulatory across primary germ layers gastrulation, spanning developmental stages from E6.5 to E7.5. Subsequently, dynamic expression factors (SFs) was characterized, while patterns functions layer-specific SFs were identified. The results indicate that differential alternative events (DASEs) exhibit changes are significantly abundant late stage gastrulation. Similarly, demonstrate stage-specific expression, with elevated levels observed middle Epigenetic signals associated sites significant enrichment undergo throughout Overall, this study offers a systematic analysis identifies events, characterizes epigenetic signals. These findings enhance understanding formation three mammalian focus pre-mRNA AS.

Language: Английский

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Transcriptome Analyses Reveal the Important miRNAs Involved in Immune Response of Gastric Cancer

IET Systems Biology, Journal Year: 2025, Volume and Issue: 19(1)

Published: Jan. 1, 2025

ABSTRACT MicroRNAs (miRNAs) are crucial factors in gene regulation, and their dysregulation plays important roles the immunity of gastric cancer (GC). However, finding specific effective miRNA markers is still a great challenge for GC immunotherapy. In this study, we computed analysed miRNA‐seq, RNA‐seq clinical data patients from TCGA database. With comparison tumour normal tissues GC, identified 2056 upregulated 2311 downregulated protein‐coding genes. Based on miRNet database, more than 2600 miRNAs interact with these Several key miRNAs, including hsa‐mir‐34a, hsa‐mir‐182 hsa‐mir‐23b, were to potentially play regulatory expression most genes GC. bioinformation approaches, expressions hsa‐mir‐34a closely linked stage, high hsa‐mir‐23b was correlated poor survival Moreover, three involved immune cell infiltration (such as activated memory CD4 T cells resting mast cells), particularly hsa‐mir‐23b. GSEA suggested that changes may possibly activate/inhibit immune‐related signal pathways, such chemokine signalling pathway CXCR4 pathway. These results will provide possible or targets combined immunotherapy

Language: Английский

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