Performance Evaluation of Structured and Semi-Structured Bioinformatics Tools : A Comparative Study

Moftah, Raja A.; Maatuk, Abdelsalam M.; White, Richard

doi:10.5121/ijsea.2018.9503

Cited by 2 publications

(3 citation statements)

References 9 publications

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“…Several works in the literature have addressed the problem of data extraction from web pages either by accessing the databases through webpages or by APIs [9][10][11][12][13]. Furthermore, big data extraction has spread to many scientific fields, such as medicine, where the volume of medical data is exponentially increasing.…”

Section: Related Workmentioning

confidence: 99%

“…However, in some cases, these data extraction tools and methods do not offer data transformation to convert the data to a structured form. Based on this, efforts are needed to deal with this problem and convert the unstructured form of data into more meaningful and structured information [10,31,39]. The development for automating the extraction of semi-structured web data is much needed.…”

Section: Related Workmentioning

confidence: 99%

“…Many tools have been constructed to extract data for machine learning purposes. These include command-line-based methods, such as application programming interface (API) [9,10] and web scraping methods, that extract information from websites [11][12][13]. Alternatively, some researchers or companies hire people to extract data manually, which costs them time and money; therefore, a method that extracts candidate information automatically is desperately needed.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Big Data Bot with a Special Reference to Bioinformatics

Al-Omari¹,

Tawalbeh²,

Akkam³

et al. 2023

Computers, Materials &Amp; Continua

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There are quintillions of data on deoxyribonucleic acid (DNA) and protein in publicly accessible data banks, and that number is expanding at an exponential rate. Many scientific fields, such as bioinformatics and drug discovery, rely on such data; nevertheless, gathering and extracting data from these resources is a tough undertaking. This data should go through several processes, including mining, data processing, analysis, and classification. This study proposes software that extracts data from big data repositories automatically and with the particular ability to repeat data extraction phases as many times as needed without human intervention. This software simulates the extraction of data from web-based (point-and-click) resources or graphical user interfaces that cannot be accessed using command-line tools. The software was evaluated by creating a novel database of 34 parameters for 1360 physicochemical properties of antimicrobial peptides (AMP) sequences (46240 hits) from various MARVIN software panels, which can be later utilized to develop novel AMPs. Furthermore, for machine learning research, the program was validated by extracting 10,000 protein tertiary structures from the Protein Data Bank. As a result, data collection from the web will become faster and less expensive, with no need for manual data extraction. The software is critical as a first step to preparing large datasets for subsequent stages of analysis, such as those using machine and deep-learning applications.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Big Data Bot with a Special Reference to Bioinformatics

Al-Omari¹,

Tawalbeh²,

Akkam³

et al. 2023

Computers, Materials &Amp; Continua

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Prostate Cancer: A Review of Genetics, Current Biomarkers and Personalised Treatments

Wilson,

Zishiri

2024

Cancer Reports

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BackgroundProstate cancer is the second leading cause of cancer deaths in men, second only to lung cancer. Despite this, diagnosis and prognosis methods remain limited, with effective treatments being few and far between. Traditionally, prostate cancer is initially tested for through a prostate serum antigen (PSA) test and a digital rectum examination (DRE), followed by confirmation through an invasive prostate biopsy. The DRE and biopsy are uncomfortable for the patient, so less invasive, accurate diagnostic tools are needed. Current diagnostic tools, along with genes that hold possible biomarker uses in diagnosis, prognosis and indications for personalised treatment plans, were reviewed in this article.Recent FindingsSeveral genes from multiple families have been identified as possible biomarkers for disease, including those from the MYC and ETS families, as well as several tumour suppressor genes, Androgen Receptor signalling genes and DNA repair genes. There have also been advances in diagnostic tools, including MRI‐targeted and liquid biopsies. Several personalised treatments have been developed over the years, including those that target metabolism‐driven prostate cancer or those that target inflammation‐driven cancer.ConclusionSeveral advances have been made in prostate cancer diagnosis and treatment, but the disease still grows year by year, leading to more and more deaths annually. This calls for even more research into this disease, allowing for better diagnosis and treatment methods and a better chance of patient survival.

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Performance Evaluation of Structured and Semi-Structured Bioinformatics Tools : A Comparative Study

Cited by 2 publications

References 9 publications

Big Data Bot with a Special Reference to Bioinformatics

Big Data Bot with a Special Reference to Bioinformatics

Prostate Cancer: A Review of Genetics, Current Biomarkers and Personalised Treatments

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