JMIR Bioinformatics and Biotechnology
Methods, web-based platforms, open data and open software tools for big data analytics, machine learning-based predictive models using genomic and imaging data, and information retrieval in biology and medicine.
Ece D. Uzun, MS, Ph.D., FAMIA, Brown University, USA
Ece D. Uzun, MS, Ph.D., FAMIA, Brown University, USA
JMIR Bioinformatics and Biotechnology, (JBB, ISSN 2563-3570) (Editor-in-chief: Ece Uzun, MS, PhD, FAMIA) is an open-access, SCOPUS, Sherpa/Romeo and DOAJ indexed journal. It focuses on bioinformatics data analyses, methods, devices, web-based platforms and open software tools for big data analytics, understanding biological/medical data, and information retrieval in biology and medicine (See Focus and Scope).
Genetic testing is essential to identify research participants for clinical trials enrolling people with Parkinson disease (PD) carrying a variant in the glucocerebrosidase (GBA) or leucine-rich repeat kinase 2 (LRRK2) genes. The limited availability of professionals trained in neurogenetics or genetic counseling is a major barrier to increased testing. Telehealth solutions to increase access to genetics education can help address issues around counselor availability and offer options to patients and family members.
While genomic variations can provide valuable information for health care and ancestry, the privacy of individual genomic data must be protected. Thus, a secure environment is desirable for a human DNA database such that the total data are queryable but not directly accessible to involved parties (eg, data hosts and hospitals) and that the query results are learned only by the user or authorized party.
COVID-19 and Middle East Respiratory Syndrome are two pandemic respiratory diseases caused by coronavirus species. The novel disease COVID-19 caused by SARS-CoV-2 was first reported in Wuhan, Hubei Province, China, in December 2019, and became a pandemic within 2-3 months, affecting social and economic platforms worldwide. Despite the rapid development of vaccines, there have been obstacles to their distribution, including a lack of fundamental resources, poor immunization, and manual vaccine replication. Several variants of the original Wuhan strain have emerged in the last 3 years, which can pose a further challenge for control and vaccine development.
A thorough understanding of the patterns of genetic subdivision in a pathogen can provide crucial information that is necessary to prevent disease spread. For SARS-CoV-2, the availability of millions of genomes makes this task analytically challenging, and traditional methods for understanding genetic subdivision often fail.
There is a great need to develop a computational approach to analyze and exploit the information contained in gene expression data. The recent utilization of nonnegative matrix factorization (NMF) in computational biology has demonstrated the capability to derive essential details from a high amount of data in particular gene expression microarrays. A common problem in NMF is finding the proper number rank (r) of factors of the degraded demonstration, but no agreement exists on which technique is most appropriate to utilize for this purpose. Thus, various techniques have been suggested to select the optimal value of rank factorization (r).
Dengue fever can progress to dengue hemorrhagic fever (DHF), a more serious and occasionally fatal form of the disease. Indicators of serious disease arise about the time the fever begins to reduce (typically 3 to 7 days following symptom onset). There are currently no effective antivirals available. Drug repurposing is an emerging drug discovery process for rapidly developing effective DHF therapies. Through network pharmacology modeling, several US Food and Drug Administration (FDA)-approved medications have already been researched for various viral outbreaks.
T helper (Th) 9 cells are a novel subset of Th cells that develop independently from Th2 cells and are characterized by the secretion of interleukin (IL)-9. Studies have suggested the involvement of Th9 cells in variable diseases such as allergic and pulmonary diseases (eg, asthma, chronic obstructive airway disease, chronic rhinosinusitis, nasal polyps, and pulmonary hypoplasia), metabolic diseases (eg, acute leukemia, myelocytic leukemia, breast cancer, lung cancer, melanoma, pancreatic cancer), neuropsychiatric disorders (eg, Alzheimer disease), autoimmune diseases (eg, Graves disease, Crohn disease, colitis, psoriasis, systemic lupus erythematosus, systemic scleroderma, rheumatoid arthritis, multiple sclerosis, inflammatory bowel disease, atopic dermatitis, eczema), and infectious diseases (eg, tuberculosis, hepatitis). However, there is a dearth of information on its involvement in other metabolic, neuropsychiatric, and infectious diseases.
Emergence of the new SARS-CoV-2 variant B.1.1.529 worried health policy makers worldwide due to a large number of mutations in its genomic sequence, especially in the spike protein region. The World Health Organization (WHO) designated this variant as a global variant of concern (VOC), which was named “Omicron.” Following Omicron’s emergence, a surge of new COVID-19 cases was reported globally, primarily in South Africa.
In recent decades, the use of artificial intelligence has been widely explored in health care. Similarly, the amount of data generated in the most varied medical processes has practically doubled every year, requiring new methods of analysis and treatment of these data. Mainly aimed at aiding in the diagnosis and prevention of diseases, this precision medicine has shown great potential in different medical disciplines. Laboratory tests, for example, almost always present their results separately as individual values. However, physicians need to analyze a set of results to propose a supposed diagnosis, which leads us to think that sets of laboratory tests may contain more information than those presented separately for each result. In this way, the processes of medical laboratories can be strongly affected by these techniques.
The emergence of SARS-CoV-2 variants with mutations associated with increased transmissibility and virulence is a public health concern in Ontario, Canada. Characterizing how the mutational patterns of the SARS-CoV-2 genome have changed over time can shed light on the driving factors, including selection for increased fitness and host immune response, that may contribute to the emergence of novel variants. Moreover, the study of SARS-CoV-2 in the microcosm of Ontario, Canada can reveal how different province-specific public health policies over time may be associated with observed mutational patterns as a model system.
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