Computational Techniques and Tools for Omics Data Analysis: State-of-the-Art, Challenges, and Future Directions

Kaur, Parampreet; Singh, Ashima; Chana, Inderveer

doi:10.1007/s11831-021-09547-0

Cited by 44 publications

(20 citation statements)

References 121 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using mRNA expression data, microRNA expression data, and DNA methylation data, it identifies important biomarkers in various cancers [ 97 ]. Kaur et al published a comprehensive review of CAs that can handle multi-Omic data [ 98 ].…”

Section: Computational Algorithms For Data Integrationmentioning

confidence: 99%

Prostate cancer in omics era

Gholami

Haghparast²,

Alipourfard

et al. 2022

Cancer Cell Int

View full text Add to dashboard Cite

Recent advances in omics technology have prompted extraordinary attempts to define the molecular changes underlying the onset and progression of a variety of complex human diseases, including cancer. Since the advent of sequencing technology, cancer biology has become increasingly reliant on the generation and integration of data generated at these levels. The availability of multi-omic data has transformed medicine and biology by enabling integrated systems-level approaches. Multivariate signatures are expected to play a role in cancer detection, screening, patient classification, assessment of treatment response, and biomarker identification. This review reports current findings and highlights a number of studies that are both novel and groundbreaking in their application of multi Omics to prostate cancer.

show abstract

Section: Computational Algorithms For Data Integrationmentioning

confidence: 99%

Prostate cancer in omics era

Gholami

Haghparast²,

Alipourfard

et al. 2022

Cancer Cell Int

View full text Add to dashboard Cite

show abstract

“…Caruso et al 34 accuracy, and sensitivity of CT scans were 56%, 72%, and 97% respectively, meaning that doctors would be able to thoroughly identify the infection in patients using CT. Lin Li et al 35 used AI to identify radiological findings in chest CT scans from six Chinese hospitals. 36 The development of a DL model called CovNet was used to extract the graphical features from 3D CT scans. The dataset comprises 4356 CT scans of the chest from 3322 patients.…”

Section: Deep Learning (Dl)mentioning

confidence: 99%

“…The patients who were affected and conducted a chest CT scan with vascular signs and extreme CT motion artifacts were included in the analysis. The precision, accuracy, and sensitivity of CT scans were 56%, 72%, and 97% respectively, meaning that doctors would be able to thoroughly identify the infection in patients using CT. Lin Li et al 35 used AI to identify radiological findings in chest CT scans from six Chinese hospitals 36 . The development of a DL model called CovNet was used to extract the graphical features from 3D CT scans.…”

Section: Related Workmentioning

confidence: 99%

Software system to predict the infection in COVID‐19 patients using deep learning and web of things

et al. 2021

Self Cite

View full text Add to dashboard Cite

Since the end of 2019, computed tomography (CT) images have been used as an important substitute for the time‐consuming Reverse Transcriptase polymerase chain reaction (RT‐PCR) test; a new coronavirus 2019 (COVID‐19) disease has been detected and has quickly spread through many countries across the world. Medical imaging such as computed tomography provides great potential due to growing skepticism toward the sensitivity of RT‐PCR as a screening tool. For this purpose, automated image segmentation is highly desired for a clinical decision aid and disease monitoring. However, there is limited publicly accessible COVID‐19 image knowledge, leading to the overfitting of conventional approaches. To address this issue, the present paper focuses on data augmentation techniques to create synthetic data. Further, a framework has been proposed using WoT and traditional U‐Net with EfficientNet B0 to segment the COVID Radiopedia and Medseg datasets automatically. The framework achieves an F ‐score of 0.96, which is best among state‐of‐the‐art methods. The performance of the proposed framework also computed using Sensitivity, Specificity, and Dice‐coefficient, achieves 84.5%, 93.9%, and 65.0%, respectively. Finally, the proposed work is validated using three quality of service (QoS) parameters such as server latency, response time, and network latency which improves the performance by 8%, 7%, and 10%, respectively.

show abstract

“…1 ) [22] . Whole-genome sequencing, in particular, can identify single-nucleotide polymorphisms (SNPs), copy number variants (CNVs), and structural variations at the genotype level [23] . At the intermediate phenotype (or endophenotype) level [16] , [24] , [25] , magnetic resonance imaging (MRI) (structural, diffusional, and functional) can reflect information about grey matter in the brain, white matter integrity, and brain functional activity with high spatial resolution; electroencephalograms (EEG) can record the neuronal electrical activity with high temporal resolution; RNA-seq can provide information on gene expression levels and discover alternative splicing, gene fusion, and novel isoforms (transcriptomic level); DNA methylation microarrays can detect CpG, CHH, and CHG sites; miRNA-seq captures expression of micro-RNAs and thus the regulation of mRNA translations and noncoding RNA expression levels; protein arrays and mass spectrometers are helpful for detecting the concentration of proteins and metabolites in cerebrospinal fluid (CSF) which can offers insight into the physiological state of the brain; and positron emission tomography/single photon emission computed tomography (PET/SPECT) can allow noninvasive evaluation of functional imaging biomarkers and physiological changes.…”

Section: Introductionmentioning

confidence: 99%