Metabolite signature of human malignant thyroid tissue: A systematic review and meta‐analysis

Razavi, S. Adeleh; Khorsand, Babak; Salehipour, Pouya; Hedayati, Mehdi

doi:10.1002/cam4.7184

Cited by 7 publications

References 60 publications

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

Zareei,

Khorsand,

Dantism

et al. 2024

Database

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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, Peptihub houses 874 CRPs, subjected to evaluation across 10 distinct organism categories, 26 organs, and 438 cell lines. Each entry in the database is accompanied by easily accessible 3D conformations, obtained either experimentally or through predictive methodology. Users are provided with three search frameworks offering basic, advanced, and BLAST sequence search options. Furthermore, precise annotations of peptides enable users to explore CRPs based on their specific activities (anticancer, no effect, insignificant effect, carcinogen, and others) and their effectiveness (rate and IC50) under cancer conditions, specifically within individual organs. This unique property facilitates the construction of robust training and testing datasets. Additionally, PeptiHub offers 1141 features with the convenience of selecting the most pertinent features to address their specific research questions. Features include aaindex1 (in six main subcategories: alpha propensities, beta propensity, composition indices, hydrophobicity, physicochemical properties, and other properties), amino acid composition (Amino acid Composition and Dipeptide Composition), and Grouped Amino Acid Composition (Grouped amino acid composition, Grouped dipeptide composition, and Conjoint triad) categories. These utilities not only speed up machine learning-based peptide design but also facilitate peptide classification. Database URL: https://bioinformaticscollege.ir/peptihub/

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

Zareei,

Khorsand,

Dantism

et al. 2024

Database

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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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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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Metabolite signature of human malignant thyroid tissue: A systematic review and meta‐analysis

Cited by 7 publications

References 60 publications

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

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

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

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