A random forest model for predicting exosomal proteins using evolutionary information and motifs

Arora, Akanksha; Patiyal, Sumeet; Sharma, Neelam; Naorem, Leimarembi Devi; Kaur, Dashleen; Raghava, Gajendra P. S.

doi:10.1002/pmic.202300231

Cited by 7 publications

(1 citation statement)

References 47 publications

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“…( Breiman, 2001 ; Wu et al, 2002 ; Geurts et al, 2006 ; Stoltzfus, 2011 ; Bulac and Bulac, 2016 ; Chen and Guestrin, 2016 ). These methods have previously been used in many studies ( Aggarwal et al, 2023 ; Arora et al, 2023 ; Kaur et al, 2023 ; Srivastava et al, 2023 ).…”

Section: Methodsmentioning

confidence: 99%

A deep learning method for classification of HNSCC and HPV patients using single-cell transcriptomics

Jarwal,

Dhall,

Arora

et al. 2024

Front. Mol. Biosci.

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BackgroundHead and Neck Squamous Cell Carcinoma (HNSCC) is the seventh most highly prevalent cancer type worldwide. Early detection of HNSCC is one of the important challenges in managing the treatment of the cancer patients. Existing techniques for detecting HNSCC are costly, expensive, and invasive in nature.MethodsIn this study, we aimed to address this issue by developing classification models using machine learning and deep learning techniques, focusing on single-cell transcriptomics to distinguish between HNSCC and normal samples. Furthermore, we built models to classify HNSCC samples into HPV-positive (HPV+) and HPV-negative (HPV−) categories. In this study, we have used GSE181919 dataset, we have extracted 20 primary cancer (HNSCC) samples, and 9 normal tissues samples. The primary cancer samples contained 13 HPV− and 7 HPV+ samples. The models developed in this study have been trained on 80% of the dataset and validated on the remaining 20%. To develop an efficient model, we performed feature selection using mRMR method to shortlist a small number of genes from a plethora of genes. We also performed Gene Ontology (GO) enrichment analysis on the 100 shortlisted genes.ResultsArtificial Neural Network based model trained on 100 genes outperformed the other classifiers with an AUROC of 0.91 for HNSCC classification for the validation set. The same algorithm achieved an AUROC of 0.83 for the classification of HPV+ and HPV− patients on the validation set. In GO enrichment analysis, it was found that most genes were involved in binding and catalytic activities.ConclusionA software package has been developed in Python which allows users to identify HNSCC in patients along with their HPV status. It is available at https://webs.iiitd.edu.in/raghava/hnscpred/.

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Section: Methodsmentioning

confidence: 99%

A deep learning method for classification of HNSCC and HPV patients using single-cell transcriptomics

Jarwal,

Dhall,

Arora

et al. 2024

Front. Mol. Biosci.

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Prediction of peptide hormones using an ensemble of machine learning and similarity‐based methods

Kaur,

Arora,

Vigneshwar

et al. 2024

Proteomics

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Peptide hormones serve as genome‐encoded signal transduction molecules that play essential roles in multicellular organisms, and their dysregulation can lead to various health problems. In this study, we propose a method for predicting hormonal peptides with high accuracy. The dataset used for training, testing, and evaluating our models consisted of 1174 hormonal and 1174 non‐hormonal peptide sequences. Initially, we developed similarity‐based methods utilizing BLAST and MERCI software. Although these similarity‐based methods provided a high probability of correct prediction, they had limitations, such as no hits or prediction of limited sequences. To overcome these limitations, we further developed machine and deep learning‐based models. Our logistic regression‐based model achieved a maximum AUROC of 0.93 with an accuracy of 86% on an independent/validation dataset. To harness the power of similarity‐based and machine learning‐based models, we developed an ensemble method that achieved an AUROC of 0.96 with an accuracy of 89.79% and a Matthews correlation coefficient (MCC) of 0.8 on the validation set. To facilitate researchers in predicting and designing hormone peptides, we developed a web‐based server called HOPPred. This server offers a unique feature that allows the identification of hormone‐associated motifs within hormone peptides. The server can be accessed at: https://webs.iiitd.edu.in/raghava/hoppred/.

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Prediction of Anti‐Freezing Proteins From Their Evolutionary Profile

Kumar,

Choudhury,

Bajiya

et al. 2024

Proteomics

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Prediction of antifreeze proteins (AFPs) holds significant importance due to their diverse applications in healthcare. An inherent limitation of current AFP prediction methods is their reliance on unreviewed proteins for evaluation. This study evaluates, proposed and existing methods on an independent dataset containing 80 AFPs and 73 non‐AFPs obtained from Uniport, which have been already reviewed by experts. Initially, we constructed machine learning models for AFP prediction using selected composition‐based protein features and achieved a peak AUROC of 0.90 with an MCC of 0.69 on the independent dataset. Subsequently, we observed a notable enhancement in model performance, with the AUROC increasing from 0.90 to 0.93 upon incorporating evolutionary information instead of relying solely on the primary sequence of proteins. Furthermore, we explored hybrid models integrating our machine learning approaches with BLAST‐based similarity and motif‐based methods. However, the performance of these hybrid models either matched or was inferior to that of our best machine‐learning model. Our best model based on evolutionary information outperforms all existing methods on independent/validation dataset. To facilitate users, a user‐friendly web server with a standalone package named “AFPropred” was developed (https://webs.iiitd.edu.in/raghava/afpropred).

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A random forest model for predicting exosomal proteins using evolutionary information and motifs

Cited by 7 publications

References 47 publications

A deep learning method for classification of HNSCC and HPV patients using single-cell transcriptomics

A deep learning method for classification of HNSCC and HPV patients using single-cell transcriptomics

Prediction of peptide hormones using an ensemble of machine learning and similarity‐based methods

Prediction of Anti‐Freezing Proteins From Their Evolutionary Profile

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