Role of Bioinformatics in cancer research and drug development

Beg, Anam; Parveen, Rabea

doi:10.1016/b978-0-323-89824-9.00011-2

Cited by 11 publications

(2 citation statements)

References 35 publications

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“…Computational intelligence together with artificial intelligence plays an important role in precision therapies and cancer surveillance. Last but not the least bioinformatics advancement in Machine learning helps in integrative gene network construction to analyze cancer, predict drug response and together with deep learning it boosts anticancer drug development [33] Thousands of mRNA transcripts can be measured in the single experiment with the help of Gene-expression profiling using DNA microarrays. Results from such studies confirmed that cancers are not a single disease instead it's a group of molecularly distinct neoplastic disorders.…”

Section: Recent Advancement In Ovarian Cancer-bioinformatics Approachesmentioning

confidence: 99%

Review of Bioinformatics Tools and Techniques to Accelerate Ovarian Cancer Research

Beg,

Parveen

2022

Int J Bioinfor Intell Comput

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Since the history of humans there was no definitive cure for cancer. The rapid development in the field of bioinformatics has resulted in acceleration of advancement of cancer research. As computing and IT technology improves over time the use and importance of bioinformatics will also rise. The bulk of biological data created by biomedical researchers has increased over the years, and it has become difficult to store and analyze that data. Faster computer processors and advancement in quantum computing will solve the conventional problem of slow data processing and will make the use of bioinformatics even attractive for scientists and researchers across the globe. The success of potential drug candidates and vaccines were identified and credit goes to bioinformatics gene simulation sequencing, simulation and fast data processing. The results were development of a vaccine in record time all thanks to bioinformatics approaches. This paper explores the contribution that bioinformatics has been able to make in the field of ovarian cancer and how the use of DNA sequencing and simulation helped in developing targeted drugs such as PARP inhibitors. It also elucidates the impact bioinformatics can make in developing effective therapies in times to come. Genome sequencing has paved the way in understanding the disease, possible treatment options analyze mutations and further predict the drug target. In this review we will highlight different aspects of bioinformatics tools and techniques that have accelerated the ovarian cancer research.

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Section: Recent Advancement In Ovarian Cancer-bioinformatics Approachesmentioning

confidence: 99%

Review of Bioinformatics Tools and Techniques to Accelerate Ovarian Cancer Research

Beg,

Parveen

2022

Int J Bioinfor Intell Comput

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“…A crucial factor throughout the poor prognosis and increased mortality is the lack of early and efficient diagnosis procedures [ 7 , 8 ]. It is therefore creating more efficient diagnosing and treatment strategies and more efforts must be made to find and better understand novel biomarkers and specific OC targets [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Unravelling driver genes as potential therapeutic targets in ovarian cancer via integrated bioinformatics approach

Beg,

Parveen,

Fouad

et al. 2024

J Ovarian Res

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Target-driven cancer therapy is a notable advancement in precision oncology that has been accompanied by substantial medical accomplishments. Ovarian cancer is a highly frequent neoplasm in women and exhibits significant genomic and clinical heterogeneity. In a previous publication, we presented an extensive bioinformatics study aimed at identifying specific biomarkers associated with ovarian cancer. The findings of the network analysis indicate the presence of a cluster of nine dysregulated hub genes that exhibited significance in the underlying biological processes and contributed to the initiation of ovarian cancer. Here in this research article, we are proceeding our previous research by taking all hub genes into consideration for further analysis. GEPIA2 was used to identify patterns in the expression of critical genes. The KM plotter analysis indicated that the out of all genes 5 genes are statistically significant. The cBioPortal platform was further used to investigate the frequency of genetic mutations across the board and how they affected the survival of the patients. Maximum mutation was reported by ELAVL2. In order to discover viable therapeutic candidates after competitive inhibition of ELAVL2 with small molecular drug complex, high throughput screening and docking studies were used. Five compounds were identified. Overall, our results suggest that the ELAV-like protein 2-ZINC03830554 complex was relatively stable during the molecular dynamic simulation. The five compounds that have been found can also be further examined as potential therapeutic possibilities. The combined findings suggest that ELAVL2, together with their genetic changes, can be investigated in therapeutic interventions for precision oncology, leveraging early diagnostics and target-driven therapy.

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Gene Expression Signature: An Influential Access to Drug Discovery in Ovarian Cancer

Beg

Parveen

2022

Computational Intelligence in Oncology

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Role of Bioinformatics in cancer research and drug development

Cited by 11 publications

References 35 publications

Review of Bioinformatics Tools and Techniques to Accelerate Ovarian Cancer Research

Review of Bioinformatics Tools and Techniques to Accelerate Ovarian Cancer Research

Unravelling driver genes as potential therapeutic targets in ovarian cancer via integrated bioinformatics approach

Gene Expression Signature: An Influential Access to Drug Discovery in Ovarian Cancer

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