An ensemble method for prediction of phage-based therapy against bacterial infections

Aggarwal, Suchet; Dhall, Anjali; Patiyal, Sumeet; Choudhury, Shubham; Arora, Akanksha; Raghava, Gajendra P. S.

doi:10.3389/fmicb.2023.1148579

Cited by 13 publications

(6 citation statements)

References 36 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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“…Meanwhile, these methods are also used to characterize viruses from metagenomes such as forest soil metagenome [ 76 ] and gut metagenome [ 77 ]. Predicting PHI also shows potential in phage therapy, which has been utilized in phage therapy against bacterial infection [ 78 ]. As a promising candidate to combat antimicrobial resistance, phage therapy has been known for its potential clinical utility in lysing bacteria [ 79 ].…”

Section: Prediction Performance and Application Scopementioning

confidence: 99%

Advances in phage–host interaction prediction: in silico method enhances the development of phage therapies

Nie,

Qiu,

Wei

et al. 2024

Briefings in Bioinformatics

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Phages can specifically recognize and kill bacteria, which lead to important application value of bacteriophage in bacterial identification and typing, livestock aquaculture and treatment of human bacterial infection. Considering the variety of human-infected bacteria and the continuous discovery of numerous pathogenic bacteria, screening suitable therapeutic phages that are capable of infecting pathogens from massive phage databases has been a principal step in phage therapy design. Experimental methods to identify phage–host interaction (PHI) are time-consuming and expensive; high-throughput computational method to predict PHI is therefore a potential substitute. Here, we systemically review bioinformatic methods for predicting PHI, introduce reference databases and in silico models applied in these methods and highlight the strengths and challenges of current tools. Finally, we discuss the application scope and future research direction of computational prediction methods, which contribute to the performance improvement of prediction models and the development of personalized phage therapy.

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“…in previous studies [29][30][31][32]. We used a standalone tool called Pfeature to compute numerous features for the proteins, including evolutionary information-based features and composition-based features [33].…”

Section: Feature Generationmentioning

confidence: 99%

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

et al. 2023

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Non‐invasive diagnostics and therapies are crucial to prevent patients from undergoing painful procedures. Exosomal proteins can serve as important biomarkers for such advancements. In this study, we attempted to build a model to predict exosomal proteins. All models are trained, tested, and evaluated on a non‐redundant dataset comprising 2831 exosomal and 2831 non‐exosomal proteins, where no two proteins have more than 40% similarity. Initially, the standard similarity‐based method Basic Local Alignment Search Tool (BLAST) was used to predict exosomal proteins, which failed due to low‐level similarity in the dataset. To overcome this challenge, machine learning (ML) based models were developed using compositional and evolutionary features of proteins achieving an area under the receiver operating characteristics (AUROC) of 0.73. Our analysis also indicated that exosomal proteins have a variety of sequence‐based motifs which can be used to predict exosomal proteins. Hence, we developed a hybrid method combining motif‐based and ML‐based approaches for predicting exosomal proteins, achieving a maximum AUROC of 0.85 and MCC of 0.56 on an independent dataset. This hybrid model performs better than presently available methods when assessed on an independent dataset. A web server and a standalone software ExoProPred (https://webs.iiitd.edu.in/raghava/exopropred/) have been created to help scientists predict and discover exosomal proteins and find functional motifs present in them.

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An ensemble method for prediction of phage-based therapy against bacterial infections

Cited by 13 publications

References 36 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

Advances in phage–host interaction prediction: in silico method enhances the development of phage therapies

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

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