Text mining‑based drug discovery in cutaneous squamous cell carcinoma

Pan, Yuyan; Zhang, Yong; Liu, Jiaqi

doi:10.3892/or.2018.6746

Cited by 4 publications

(3 citation statements)

References 55 publications

(64 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…DrugQuest is a website that applies text-mining technologies and the TextQuest algorithm to mine a publicly accessible database to identify biologically significant terms and group these words based on the textual content (29). Another cancer-related study demonstrated using text-mining and pathway analysis tools is a prospective method for exploring potential drugs targeting the genes or pathways related to cutaneous squamous cell carcinoma (cSCC) (30). In this study, 121 genes relevant to cSCC were identified using a text-mining approach, and 55 drugs were revealed that could target 10 pathways through gene enrichment analysis.…”

Section: Editorial Commentarymentioning

confidence: 99%

Text-mining in cancer research may help identify effective treatments

Hsiao¹,

Lu²

2019

Transl Lung Cancer Res

View full text Add to dashboard Cite

Section: Editorial Commentarymentioning

confidence: 99%

Text-mining in cancer research may help identify effective treatments

Hsiao¹,

Lu²

2019

Transl Lung Cancer Res

View full text Add to dashboard Cite

“…e text mining methods [16,17] including information matching and other retrieval technologies are limited by the mining algorithms to detect new interactions in the literature database. However, proteins metastructure provides a new biologic way to describe the proteins with primary sequences.…”

Section: Introductionmentioning

confidence: 99%

A Sparse Feature Extraction Method with Elastic Net for Drug-Target Interaction Identification

Zhao

Huang

Pan

et al. 2021

Scientific Programming

View full text Add to dashboard Cite

The identification of drug-target interactions (DTIs) plays a crucial role in drug discovery. However, the traditional high-throughput techniques based on clinical trials are costly, cumbersome, and time-consuming for identifying DTIs. Hence, new intelligent computational methods are urgently needed to surmount these defects in predicting DTIs. In this paper, we propose a novel computational method that combines position-specific scoring matrix (PSSM), elastic net based sparse features extraction, and rotation forest (RF) classifier. Specifically, we converted each protein primary sequence into PSSM, which contains biological evolutionary information. Then we extract the hidden sparse feature descriptors in PSSM by elastic net based sparse feature extraction method (ESFE). After that, we fuse them with the features of drug, which are represented by molecular fingerprints. Finally, rotation forest classifier works on detecting the potential drug-target interactions. When performing the proposed method by the experiments of fivefold cross validation (CV) on enzyme, ion channel, G protein-coupled receptors (GPCRs), and nuclear receptor datasets, this method achieves average accuracies of 90.32%, 88.91%, 80.65%, and 79.73%, respectively. We also compared the proposed model with the state-of-the-art support vector machine (SVM) classifier and other effective methods on the same datasets. The comparison results distinctly indicate that the proposed model possesses the efficient and robust ability to predict DTIs. We expect that the new model will be able to take effects on predicting massive DTIs.

show abstract

“…In this work, our goal was to retrieve DDAs while lifting the strain on establishing or possessing prior gene-disease lists, and we concentrated herein on cancer with a particular focus on Blood Cancers (BCs). Cancer is a complex and multifactorial disease for which there is a widespread availability of related literature [ 5 ], useful for biomarker research [ 6 ], drug discovery [ 7 ], biological pathways detection [ 8 ], among others, highlighting this technique's positive contribution to a highly demanding field of study. The strength of mining the literature towards building disease networks was tapped into on multiple occasions as recently reviewed by Rodríguez-González [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

SicknessMiner: a deep-learning-driven text-mining tool to abridge disease-disease associations

et al. 2021

View full text Add to dashboard Cite

Background Blood cancers (BCs) are responsible for over 720 K yearly deaths worldwide. Their prevalence and mortality-rate uphold the relevance of research related to BCs. Despite the availability of different resources establishing Disease-Disease Associations (DDAs), the knowledge is scattered and not accessible in a straightforward way to the scientific community. Here, we propose SicknessMiner, a biomedical Text-Mining (TM) approach towards the centralization of DDAs. Our methodology encompasses Named Entity Recognition (NER) and Named Entity Normalization (NEN) steps, and the DDAs retrieved were compared to the DisGeNET resource for qualitative and quantitative comparison. Results We obtained the DDAs via co-mention using our SicknessMiner or gene- or variant-disease similarity on DisGeNET. SicknessMiner was able to retrieve around 92% of the DisGeNET results and nearly 15% of the SicknessMiner results were specific to our pipeline. Conclusions SicknessMiner is a valuable tool to extract disease-disease relationship from RAW input corpus.

show abstract

Text mining‑based drug discovery in cutaneous squamous cell carcinoma

Cited by 4 publications

References 55 publications

Text-mining in cancer research may help identify effective treatments

Text-mining in cancer research may help identify effective treatments

A Sparse Feature Extraction Method with Elastic Net for Drug-Target Interaction Identification

SicknessMiner: a deep-learning-driven text-mining tool to abridge disease-disease associations

Contact Info

Product

Resources

About