PeptiHub: a curated repository of precisely annotated cancer-related peptides with advanced utilities for peptide exploration and discovery

Zareei, Sara; Khorsand, Babak; Dantism, Alireza; Zareei, Neda; Asgharzadeh, Fereshteh; Zahraee, Shadi Shams; Mashreghi Kashan, Samane; Hekmatirad, Shirin; Amini, Shila; Ghasemi, Fatemeh; Moradnia, Maryam; Vaghf, Atena; Hemmatpour, Anahid; Hourfar, Hamdam; Niknia, Soudabeh; Johari, Ali; Salimi, Fatemeh; Fariborzi, Neda; Shojaei, Zohreh; Asiaei, Elaheh; Shabani, Hossein

doi:10.1093/database/baae092

Database

2024

DOI: 10.1093/database/baae092

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PeptiHub: a curated repository of precisely annotated cancer-related peptides with advanced utilities for peptide exploration and discovery

Sara Zareei,

Babak Khorsand,

Alireza Dantism

et al.

Abstract: Peptihub (https://bioinformaticscollege.ir/peptihub/) is a meticulously curated repository of cancer-related peptides (CRPs) that have been documented in scientific literature. A diverse collection of CRPs is included in the PeptiHub, showcasing a spectrum of effects and activities. While some peptides demonstrated significant anticancer efficacy, others exhibited no discernible impact, and some even possessed alternative non-drug functionalities, including drug carrier or carcinogenic attributes. Presently, P… Show more

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Cited by 5 publications

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Enhancing Ischemic Stroke Management: Leveraging Machine Learning Models for Predicting Patient Recovery After Alteplase Treatment

Khorsand,

Vaghf,

Salimi

et al. 2024

Preprint

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Aim: Ischemic stroke remains a leading global cause of morbidity and mortality, emphasizing the need for timely treatment strategies. This study aimed to develop a machine learning model to predict clinical outcomes in ischemic stroke patients undergoing Alteplase therapy, thereby supporting more personalized care. Methods: Data from 457 ischemic stroke patients were analyzed, including 50 demographic, clinical, laboratory, and imaging variables. Five machine learning algorithms, k-nearest neighbors (KNN), support vector machines (SVM), Naïve Bayes (NB), decision trees (DT), and random forest (RF), were evaluated for predictive accuracy. The primary evaluation metrics were sensitivity and F-measure, with an additional feature importance analysis to identify high-impact predictors. Results: The Random Forest model showed the highest predictive reliability, outperforming other algorithms in sensitivity and F-measure. Furthermore, by using only the top-ranked features identified from the feature importance analysis, the model maintained comparable performance, suggesting a streamlined yet effective predictive approach. Conclusion: Our findings highlight the potential of machine learning in optimizing ischemic stroke treatment outcomes. Random Forest, in particular, proved effective as a decision-support tool, offering clinicians valuable insights for more tailored treatment approaches. This model's use in clinical settings could significantly enhance patient outcomes by informing better treatment decisions.

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Enhancing Ischemic Stroke Management: Leveraging Machine Learning Models for Predicting Patient Recovery After Alteplase Treatment

Khorsand,

Vaghf,

Salimi

et al. 2024

Preprint

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Machine Learning-Driven Identification of Molecular Subgroups in Medulloblastoma via Gene Expression Profiling

Hourfar,

Taklifi,

Razavi

et al. 2024

Preprint

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Background: Medulloblastoma (MB) is the most prevalent malignant brain tumor in children, characterized by substantial molecular heterogeneity across its subgroups. Accurate classification is pivotal for personalized treatment strategies and prognostic assessments. Procedure: This study utilized machine learning (ML) techniques to analyze RNA sequencing data from 70 pediatric medulloblastoma samples. Five classifiers, K-nearest Neighbors (KNN), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes (NB), were employed to predict molecular subgroups based on gene expression profiles. Feature selection identified gene subsets of varying sizes (750, 75, and 25 genes) to optimize classification accuracy. Results: Initial analyses with the complete gene set lacked discriminative power. However, reduced feature sets significantly enhanced clustering and classification performance, particularly for Group 3 and Group 4 subgroups. The RF, KNN, and SVM classifiers consistently outperformed the DT and NB classifiers, achieving classification accuracies exceeding 90% in many scenarios, especially in Group 3 and Group 4. Conclusions: This study highlights the efficacy of ML algorithms in classifying medulloblastoma subgroups using gene expression data. The integration of feature selection techniques substantially improves model performance, paving the way for enhanced personalized approaches in medulloblastoma management.

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Systematics investigation of key drivers of lung adenocarcinoma: A focus on genes, pathways, and miRNAs

Navaei,

Karami,

Jahanimoghadam

et al. 2024

Preprint

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Introduction Lung cancer remain a leading cause of cancer-related death, largely due to its asymptomatic progression in early stages and the development of drug resistance. Non-small cell lung cancer (NSCLC) accounts for 80% of all lung cancer cases, with lung adenocarcinoma (LUAD) being the most prevalent subtype. Despite advancements in treatment, the 5-year survival rate for LUAD remains low. Therefore, exploring gene networks may reveal novel therapeutic targets and pave the way for improved Method A comprehensive literature review was conducted across various databases containing multi-level genomic information. From this, a robust list of LUAD-related genes was curated. These genes were used to construct a weighted network based on KEGG pathway similarity. The network was subjected to clustering, hub gene detection, and gene ontology analysis. In parallel, a protein-protein interaction (PPI) network was constructed around these genes, which was further enriched with miRNA data to develop a gene-miRNA regulatory network. Results Following our analysis, 48 genes were identified as crucial to LUAD. Many of these genes, along with their corresponding miRNAs, were found to be either upregulated or downregulated in LUAD tissues. The hub genes and miRNAs identified are believed to play key roles in the initiation and progression of LUAD. Our network analysis highlighted PIK3CA, BRAF, EGFR, ERBB2, FGFR3, MTOR, and TP53, along with KRAS, MET, and FGFR2, as potential biomarkers. Additionally, miR-17-5p and miR-27a-3p, which are notably implicated in LUAD, emerged as novel biomarker candidates. Conclusion In conclusion, we employed a combination of bioinformatics techniques and database mining to derive a refined list of genes and miRNAs with high potential for further research in LUAD. We also identified core pathways that play a critical role in LUAD pathogenesis, providing a foundation for future studies aimed at developing more targeted therapeutic approaches.

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PeptiHub: a curated repository of precisely annotated cancer-related peptides with advanced utilities for peptide exploration and discovery

Cited by 5 publications

References 44 publications

Enhancing Ischemic Stroke Management: Leveraging Machine Learning Models for Predicting Patient Recovery After Alteplase Treatment

Enhancing Ischemic Stroke Management: Leveraging Machine Learning Models for Predicting Patient Recovery After Alteplase Treatment

Machine Learning-Driven Identification of Molecular Subgroups in Medulloblastoma via Gene Expression Profiling

Systematics investigation of key drivers of lung adenocarcinoma: A focus on genes, pathways, and miRNAs

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