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Topic Review
Intelligent Healthcare System for Predicting Cardiac Diseases
Cardiovascular diseases (CVD) are amongst the leading causes of death worldwide. Modern medical milestones are represented by 5G systems, internet services, artificial intelligence (AI), microelectronics, big data, cloud computing (CC), and smart bioengineering. These techniques are employed at every stage of sophisticated medicine. The Internet of Things (IoT)’, given its capacity to assist in solving diverse health-related problems in a highly efficient manner, has attracted the attention of scientists desirous of contributing to this domain.
  • 90
  • 19 Jan 2024
Topic Review
State-of the-Art Constraint-Based Modeling of Microbial Metabolism
Methanotrophy is the ability of an organism to capture and utilize the greenhouse gas, methane, as a source of energy-rich carbon. Over the years, significant progress has been made in understanding of mechanisms for methane utilization, mostly in bacterial systems, including the key metabolic pathways, regulation and the impact of various factors (iron, copper, calcium, lanthanum, and tungsten) on cell growth and methane bioconversion. The implementation of -omics approaches provided vast amount of heterogeneous data that require the adaptation or development of computational tools for a system-wide interrogative analysis of methanotrophy. The genome-scale mathematical modeling of its metabolism has been envisioned as one of the most productive strategies for the integration of muti-scale data to better understand methane metabolism and enable its biotechnological implementation. 
  • 140
  • 03 Jan 2024
Topic Review
Directed Cycles Evolve with Junk DNA
Cell responses are usually viewed as transitive events with fixed inputs and outputs that are regulated by feedback loops. In contrast, directed cycles (DCs) have all nodes connected, and the flow is in a single direction. Consequently, DCs can regenerate themselves and implement intransitive logic. DCs are able to couple unrelated chemical reactions to each edge. The output depends upon which node is used as input.
  • 145
  • 22 Nov 2023
Topic Review
Cell-Type Annotation
Multicellular organisms consist of cells that can be categorized by their function and morphology. Single-cell transcriptomics makes it possible to individually profile thousands of cells in multiple tissues and organisms within a single experiment. Determining and labeling cell types or states in single cell transcriptomic data is known as cell-type annotation or identification. Several methods are employed for cell-type annotation, including signature scoring, supervised learning, cell-integration-based label transfer, and semi-supervised annotation. Considering the lineage relationships among cell types, hierarchical classification methods are crucial for accurately identifying cell types and subtypes at an optimal clustering resolution. The use of well-curated reference datasets, implementation of quality control measures, and careful consideration of cluster resolutions heavily influence the reliability of cell-type annotation. The aim of cell-type annotation is to gain insights into cell heterogeneity in various biological processes and diseases, with the potential to drive improvements in therapeutic interventions.
  • 347
  • 08 Aug 2023
Topic Review
Catalytic Factors Associated with Post-Traumatic Stress Disorder
Post-traumatic stress disorder (PTSD) is a complex psychological disorder that develops following exposure to traumatic events. PTSD is influenced by catalytic factors such as dysregulated hypothalamic-pituitary-adrenal (HPA) axis, neurotransmitter imbalances, and oxidative stress. Genetic variations may act as important catalysts, impacting neurochemical signaling, synaptic plasticity, and stress response systems. Understanding the intricate gene networks and their interactions is vital for comprehending the underlying mechanisms of PTSD. 
  • 134
  • 03 Aug 2023
Topic Review
Deep Learning in Whole Slide Imaging for Cancer
The significant progress made in the field of cancer prognosis using whole slide images (WSIs) is encouraging, indicating a promising future for cancer diagnosis and management. The ability to accurately predict survival rates and recurrence risk using deep learning methods has significant implications for clinical practice and patient care. As more sophisticated models and techniques are developed, the potential to revolutionize the field of oncology is immense.
  • 204
  • 02 Aug 2023
Topic Review
Transformer Architecture and Attention Mechanisms in Genome Data
The emergence and rapid development of deep learning, specifically transformer-based architectures and attention mechanisms, have had transformative implications across several domains, including bioinformatics and genome data analysis. The analogous nature of genome sequences to language texts has enabled the application of techniques that have exhibited success in fields ranging from natural language processing to genomic data. 
  • 313
  • 26 Jul 2023
Topic Review
Application of GANs in Gene Expression Data Augmentation
A generative adversarial network (GAN) is essentially a two-player game composed of a generator and a discriminator. The generator’s role is to create synthetic data, while the discriminator’s task is to distinguish between real and generated data. During the training process, the generator strives to produce data that the discriminator cannot differentiate from the real data, whereas the discriminator continually improves its ability to distinguish real from generated data. This adversarial training regimen imbues GANs with the capability to model complex data distributions and produce high-quality synthetic data. Notably, their application to gene expression data systems is a fascinating and rapidly growing focus area.
  • 189
  • 21 Jul 2023
Topic Review
Kinases/Protein Phosphatases in Signaling Pathways Activation
Optimizing physical training regimens to increase muscle aerobic capacity requires an understanding of the internal processes that occur during exercise that initiate subsequent adaptation. During exercise, muscle cells undergo a series of metabolic events that trigger downstream signaling pathways and induce the expression of many genes in working muscle fibers. There are a number of studies that show the dependence of changes in the activity of AMP-activated protein kinase (AMPK), one of the mediators of cellular signaling pathways, on the duration and intensity of single exercises. The activity of various AMPK isoforms can change in different directions, increasing for some isoforms and decreasing for others, depending on the intensity and duration of the load.
  • 239
  • 11 Jul 2023
Topic Review
Deep Learning for Protein-Protein Interaction
Deep learning is steadily leaving its transformative imprint across multiple disciplines. Within computational biology, it is expediting progress in the understanding of Protein–Protein Interactions (PPIs), key components governing a wide array of biological functionalities.
  • 200
  • 04 Jul 2023
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