Biomarkers: The Future of Medical Science to Detect Cancer

Karley, Dugeshwar

doi:10.4172/2155-9929.1000118

Cited by 18 publications

(10 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They may also be used in next-generation sequencing, multianalytic diagnostic panels, and whole exon sequencing. [13][14][15]…”

Section: Fighting Cancer With Pmmentioning

confidence: 99%

Untitled

Vyas¹

2018

AJP

View full text Add to dashboard Cite

Precision medicine (PM) is an advanced high-model approach under which standard therapy for the right patient at an accurate time is provided, which is necessary for cancer therapy. Problems such as an individual diversity in gene, lifestyle, environment cause variability of the treatment response, and resistance to medication have been long-term challenges in oncology. Statics show that traditional clinical practices sometimes give poor health outcomes and a misuse of medical resources. In this model, a diagnostic is based on combined therapy using biomarker along with cellular, molecular, and genetic analysis. Diseases such as cancer including lung, breast, colorectal as well as leukemia, and melanomas can be controlled under this effective treatment by a physician to improve the chance of survival, reduce exposure to adverse effect, prevent harmful drug interaction, and increase overall efficacy. This revolutionized technology create a new era of medicine, in which researcher, health providers, and patient work together to improve health, treat diseases, and develop individualized care. Fighting against cancer is common to all countries. The GLOBOCAN statistics data revealed about 14.1 million newly diagnosed cancer patients, 32.6 million people living with cancer, and 8.2 million deaths from cancer worldwide. The cancer PM will hold advanced technology in other field including biotechnologies such as proteomics, nextgeneration sequencing, bioinformatics, and pharmacology to identify the actual cause for cancer and also tailor-fit personalized therapies.

show abstract

“…They may also be used in next-generation sequencing, multianalytic diagnostic panels, and whole exon sequencing. [13][14][15]…”

Section: Fighting Cancer With Pmmentioning

confidence: 99%

Untitled

Vyas¹

2018

AJP

View full text Add to dashboard Cite

show abstract

“…Molecular biomarkers can be used to refer to nonimaging biomarkers that have biophysical properties, which allow their measurements in biological samples (e.g., plasma, serum, cerebrospinal fluid, bronchoalveolar lavage, and biopsy) (Karley et al, 2011). They can be classified based on their characteristics such as molecular biomarkers (CT, PET, MRI) or imaging biomarkers.…”

Section: Types Of Biomarkersmentioning

confidence: 99%

Enzymatic Biosensors

Karunakaran¹,

Thangamuthu²,

Sethy

2015

Biosensors and Bioelectronics

View full text Add to dashboard Cite

“…Classification and diagnostic prediction of complex diseases such as cancers and neuron-degeneration diseases using genomic or proteomic data can improve the quality of pathological diagnosis and help develop personalized treatment of these diseases [1]. Although great efforts have been exerted in this field, making early and precise diagnosis of complex diseases, followed through with effectively treating remains a great challenge.…”

Section: Introductionmentioning

confidence: 99%

Diagnostic prediction of complex diseases using phase-only correlation based on virtual sample template

Fang

2013

BMC Bioinformatics

View full text Add to dashboard Cite

MotivationComplex diseases induce perturbations to interaction and regulation networks in living systems, resulting in dynamic equilibrium states that differ for different diseases and also normal states. Thus identifying gene expression patterns corresponding to different equilibrium states is of great benefit to the diagnosis and treatment of complex diseases. However, it remains a major challenge to deal with the high dimensionality and small size of available complex disease gene expression datasets currently used for discovering gene expression patterns.ResultsHere we present a phase-only correlation (POC) based classification method for recognizing the type of complex diseases. First, a virtual sample template is constructed for each subclass by averaging all samples of each subclass in a training dataset. Then the label of a test sample is determined by measuring the similarity between the test sample and each template. This novel method can detect the similarity of overall patterns emerged from the differentially expressed genes or proteins while ignoring small mismatches.ConclusionsThe experimental results obtained on seven publicly available complex disease datasets including microarray and protein array data demonstrate that the proposed POC-based disease classification method is effective and robust for diagnosing complex diseases with regard to the number of initially selected features, and its recognition accuracy is better than or comparable to other state-of-the-art machine learning methods. In addition, the proposed method does not require parameter tuning and data scaling, which can effectively reduce the occurrence of over-fitting and bias.

show abstract

Biomarkers: The Future of Medical Science to Detect Cancer

Cited by 18 publications

References 24 publications

Untitled

Untitled

Enzymatic Biosensors

Diagnostic prediction of complex diseases using phase-only correlation based on virtual sample template

Contact Info

Product

Resources

About