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Review Article
From Data to Diagnosis Exploring AWS Cloud Solutions in Multi-Omics Breast Cancer Biomarker Research
Gnanam Subramanian*,
Kavitha Ramamoorthy
Issue:
Volume 12, Issue 1, December 2024
Pages:
1-11
Received:
27 March 2024
Accepted:
12 April 2024
Published:
15 August 2024
Abstract: Breast cancer presents a profound global health challenge, compounded by unique intricacies within the Indian demographic, necessitating bespoke research methodologies. This abstract delineates the profound impact of Amazon Web Services (AWS) Cloud Solutions on advancing multi-omics breast cancer biomarker research, with a particular focus on Indian patient cohorts. It initiates with an exposition of the inherent challenges encountered during the transition from raw data acquisition to clinical diagnosis, emphasizing the indispensable role of cloud-based infrastructures in expediting this complex trajectory. Harnessing the comprehensive capabilities of AWS, this study elucidates how cloud solutions facilitate the seamless integration and analysis of multifaceted omics datasets, encompassing genomics, transcriptomics, proteomics, and metabolomics. Central to this endeavor is a meticulous exploration of region-specific molecular markers germane to breast cancer within the Indian populace, illuminating their diagnostic and therapeutic ramifications. By capitalizing on AWS Cloud's scalability and computational acumen, this research underscores notable efficiency enhancements in processing voluminous datasets and distilling salient patterns therein. Furthermore, the discourse extends to the broader ramifications of these technological advancements within the precision medicine landscape, emphasizing the potential for tailored therapeutic interventions. This research heralds a paradigmatic shift in the application of cloud-based infrastructures to unravel the intricate tapestry of breast cancer, transcending geographical confines. Through its provision of insights poised to augment diagnostic precision and therapeutic efficacy on a global scale, this study marks a seminal stride towards fully harnessing the potential of precision oncology in combating breast malignancies.
Abstract: Breast cancer presents a profound global health challenge, compounded by unique intricacies within the Indian demographic, necessitating bespoke research methodologies. This abstract delineates the profound impact of Amazon Web Services (AWS) Cloud Solutions on advancing multi-omics breast cancer biomarker research, with a particular focus on India...
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Research Article
Application of Fractal Dimension for Cardiac Arrhythmias Classification
Ben Ali Sabrine*,
Aguili Taoufik
Issue:
Volume 12, Issue 1, December 2024
Pages:
12-17
Received:
29 May 2024
Accepted:
9 July 2024
Published:
11 September 2024
Abstract: Fractal analysis is crucial for understanding complex, irregular patterns found in nature, finance, and various scientific fields. It helps to reveal self-similarity, where structures repeat at different scales, providing insights into chaotic systems like weather patterns, stock markets, and biological growth. By applying fractal analysis, researchers can model phenomena that traditional geometric methods cannot easily describe, enabling better predictions and deeper comprehension of dynamic systems. The Fractals are a fascinating mathematical tool for modeling the roughness of nature and understanding structure of such complex objects. They are considered a tool for understanding the world. In general, fractal objects are characterized by the fractal dimension. The application of fractal geometry to the analysis of ECG time series data is examined in this paper. A method based on the assessment of the Fractal Dimension (FD) of ECG recordings is suggested for the identification of cardiac diseases. In this work, and in order to exploit the fractal dimension to analyze fractal signals, the notion of fractal dimension is defined by presenting methods for calculating this dimension such as Higuchi algorithm, Katz method, regularization, box-counting etc… Each of them has its own advantages and disadvantages. This study has shown that the electrocardiogram (ECG) is a fractal signal. This allows to classify heartbeats founded on the concept of fractals. The main aim is to develop a digital technique to analyze ECG signals in order to make an accurate diagnosis of cardiovascular diseases.
Abstract: Fractal analysis is crucial for understanding complex, irregular patterns found in nature, finance, and various scientific fields. It helps to reveal self-similarity, where structures repeat at different scales, providing insights into chaotic systems like weather patterns, stock markets, and biological growth. By applying fractal analysis, researc...
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Research Article
A Study on Novel Amino Acid Pair Features for Protein Evolutionary Classifications
Issue:
Volume 12, Issue 1, December 2024
Pages:
18-31
Received:
14 August 2024
Accepted:
7 September 2024
Published:
23 September 2024
DOI:
10.11648/j.cbb.20241201.13
Downloads:
Views:
Abstract: Protein evolutionary classification from amino acid sequence is one of the hot research topics in computational biology and bioinformatics. The amino acid composition and arrangement in a protein sequence embed the hints to its evolutionary origins. The feature extraction from an amino acid sequence to a numerical vector is still a challenging problem. Traditional feature methods extract protein sequence information either from individual amino acids or kmers aspects, which have general performance with limitations in classification accuracy. To further improve the accuracy in protein evolutionary classifications, six new features defined on separated amino acid pairs are proposed for protein evolutionary classification analysis, where composition and arrangement as well as physical properties are considered for the different combinations of separated amino acid pairs. Different from general consideration of amino acid pairs, the new features account for the features of separated amino acid pairs with spatial intervals in the sequence, which may deeper reflect the spatial relationships and characters between the amino acid in pairs. In test of the performances of the new features, five standard protein evolutionary classification examples are employed, where the new features proposed are compared with classical protein sequence features such as averaged property factors (APF), natural vector (NV) and pseudo amino acid composition (PseAAC) as well as kmer versions of these features. The area under precision-recall curve (AUPRC) analysis shows that the new features are efficient in evolutionary classifications, which outperform traditional protein sequence features that are based on individual amino acids and kmers. Parameter analysis on the novel separated amino acid pair features and kmer features show that the features of some medium or longer length of amino acid pair intervals and kmers may achieve higher classification accuracy in evolutionary classifications. From this analysis, the newly proposed separated amino acid pairs with spacial intervals are proved to be efficient units in extracting protein sequences features, which may interpret richer evolutionary information of protein sequences than individual amino acids and kmers.
Abstract: Protein evolutionary classification from amino acid sequence is one of the hot research topics in computational biology and bioinformatics. The amino acid composition and arrangement in a protein sequence embed the hints to its evolutionary origins. The feature extraction from an amino acid sequence to a numerical vector is still a challenging prob...
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,
Xin ying Tan
