Biomarkers and Their Role in Detection of Biomolecules

Taj, Ayesha; Rehman, Abdul; Bajwa, Sadia Z.

doi:10.1002/9783527345137.ch4

Cited by 7 publications

(3 citation statements)

References 90 publications

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“…Therefore, these biomolecules are indicative parameters or biomarkers of different types of diseases. 136 Mostly these biomarkers are found in body fluids such as blood, urine, and tissues and are potent tools for the identification and treatment of many life-threatening diseases. Hence, the precise detection of these biomolecules is necessary for protecting our bodies from various diseases.…”

Section: Biomoleculesmentioning

confidence: 99%

Bubble-propelled micro/nanomotors: a robust platform for the detection of environmental pollutants and biosensing

et al. 2023

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Section: Biomoleculesmentioning

confidence: 99%

Bubble-propelled micro/nanomotors: a robust platform for the detection of environmental pollutants and biosensing

et al. 2023

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“…27,44 Since biomolecules are involved in many key biological processes and therapeutic intervention can influence their expression, they have become popular targets for biomarker discovery. 45 Numerous MSI studies have focused on increasing both the sensitivity and specificity of measurements for biomolecules. [46][47][48][49] Distinguishing how a disease state or therapeutic intervention impacts biomolecule expression and localization can give us further insight into both the pharmacokinetics 50 and pharmacodynamics.…”

Section: Introductionmentioning

confidence: 99%

Quantitative mass spectrometry imaging: therapeutics & biomolecules

Holbrook,

Kemper,

Hummon

2024

Chem. Commun.

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“…Biomarkers are the indicators that provide essential information about the presence of disease (Taj, Rehman, & Bajwa, 2020). For example, body temperature is a biomarker of fever, and blood pressure is a biomarker of hypertension or hypotension.…”

Section: Introductionmentioning

confidence: 99%

Finding Biomarkers from a High-Dimensional Imbalanced Dataset Using the Hybrid Method of Random Undersampling and Lasso

Rochayani

Sa'adah

Astuti

2020

ComTech

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The research conducted undersampling and gene selection as a starting point for cancer classification in gene expression datasets with a high-dimensional and imbalanced class. It investigated whether implementing undersampling before gene selection gave better results than without implementing undersampling. The used undersampling method was Random Undersampling (RUS), and for gene selection, it was Lasso. Then, the selected genes based on theory were validated. To explore the effectiveness of applying RUS before gene selection, the researchers used two gene expression datasets. Both of the datasets consisted of two classes, 1.545 observations and 10.935 genes, but had a different imbalance ratio. The results show that the proposed gene selection methods, namely Lasso and RUS + Lasso, can produce several important biomarkers, and the obtained model has high accuracy. However, the model is complicated since it involves too many genes. It also finds that undersampling is not affected when it is implemented in a less imbalanced class. Meanwhile, when the dataset is highly imbalanced, undersampling can remove a lot of information from the majority class. Nevertheless, the effectiveness of undersampling remains unclear. Simulation studies can be carried out in the next research to investigate when undersampling should be implemented.

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Biomarkers and Their Role in Detection of Biomolecules

Cited by 7 publications

References 90 publications

Bubble-propelled micro/nanomotors: a robust platform for the detection of environmental pollutants and biosensing

Bubble-propelled micro/nanomotors: a robust platform for the detection of environmental pollutants and biosensing

Quantitative mass spectrometry imaging: therapeutics & biomolecules

Finding Biomarkers from a High-Dimensional Imbalanced Dataset Using the Hybrid Method of Random Undersampling and Lasso

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