Computational insights into irinotecan's interaction with UBE2I in ovarian and endometrial cancers.

Endometrial and Ovarian cancers are two highly prevalent and fatal reproductive diseases with poor prognoses among women. Elevated estrogen levels in Ovarian Cancer (OC) stimulate the endometrium, causing Endometrial Cancer (EC). Although numerous studies have reported the crucial genes and pathways in this cancer, the pathogenesis of this disease remains unclear.

Expression analysis and mapping of Viral-Host Protein interactions of Poxviridae suggests a lead candidate molecule targeting Mpox.

Background: Monkeypox (Mpox) is an important human pathogen without etiological treatment. A viral-host interactome study may advance our understanding of molecular pathogenesis and lead to the discovery of suitable therapeutic targets.

Methods: GEO Expression datasets characterizing mRNA profile changes in different host responses to poxviruses were analyzed for shared pathway identification, and then, the Protein-protein interaction (PPI) maps were built.

Non-coding RNAs in human health and disease: potential function as biomarkers and therapeutic targets.

Human diseases have been a critical threat from the beginning of human history. Knowing the origin, course of action and treatment of any disease state is essential. A microscopic approach to the molecular field is a more coherent and accurate way to explore the mechanism, progression, and therapy with the introduction and evolution of technology than a macroscopic approach.

The influence of machine learning technologies in gut microbiome research and cancer studies - A review.

Gut microbial profiles induce cancer growth and impact treatment effectiveness, tolerance, and safety. There is still more to discover about the relationship between diseases and the microbiota and its clinical consequences. Even though much of the study is still in its early phases, the 'omics' technologies were widely used for microbiome analysis due to the increased size of datasets available in public databases.

Predicting cross-tissue hormone-gene relations using balanced word embeddings.

Motivation: Inter-organ/inter-tissue communication is central to multi-cellular organisms including humans, and mapping inter-tissue interactions can advance system-level whole-body modeling efforts. Large volumes of biomedical literature have fostered studies that map within-tissue or tissue-agnostic interactions, but literature-mining studies that infer inter-tissue relations, such as between hormones and genes are solely missing.

Results: We present a first study to predict from biomedical literature the hormone-gene associations mediating inter-tissue signaling in the human body.

The crosstalk of the human microbiome in breast and colon cancer: A metabolomics analysis.

The human microbiome's role in colon and breast cancer is described in this review. Understanding how the human microbiome and metabolomics interact with breast and colon cancer is the chief area of this study. First, the role of the gut and distal microbiome in breast and colon cancer is investigated, and the direct relationship between microbial dysbiosis and breast and colon cancer is highlighted.

Identification of marker genes in Alzheimer's disease using a machine-learning model.

Alzheimer's Disease (AD) is one of the most common causes of dementia, mostly affecting the elderly population. Currently, there is no proper diagnostic tool or method available for the detection of AD. The present study used two distinct data sets of AD genes, which could be potential biomarkers in the diagnosis.

Host transcriptome-guided drug repurposing for COVID-19 treatment: a meta-analysis based approach.

Background: Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been declared a pandemic by the World Health Organization, and the identification of effective therapeutic strategy is a need of the hour to combat SARS-CoV-2 infection. In this scenario, the drug repurposing approach is widely used for the rapid identification of potential drugs against SARS-CoV-2, considering viral and host factors.

Methods: We adopted a host transcriptome-based drug repurposing strategy utilizing the publicly available high throughput gene expression data on SARS-CoV-2 and other respiratory infection viruses.