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.

Drug repurposing for hyperlipidemia associated disorders: An integrative network biology and machine learning approach.

Hyperlipidemia causes diseases like cardiovascular disease, cancer, Type II Diabetes and Alzheimer's disease. Drugs that specifically target HL associated diseases are required for treatment. 34 KEGG pathways targeted by lipid lowering drugs were used to construct a directed protein-protein interaction network and driver nodes were determined using CytoCtrlAnalyser plugin of Cytoscape 3.

Deciphering the Novel Target Genes Involved in the Epigenetics of Hepatocellular Carcinoma Using Graph Theory Approach.

Background: Even after decades of research, cancer, by and large, remains a challenge and is one of the major causes of death worldwide. For a very long time, it was believed that cancer is simply an outcome of changes at the genetic level but today, it has become a well-established fact that both genetics and epigenetics work together resulting in the transformation of normal cells to cancerous cells.

Objective: In the present scenario, researchers are focusing on targeting epigenetic machinery.

Network analysis of host-pathogen protein interactions in microbe induced cardiovascular diseases.

Large-scale visualization and analysis of HPIs involved in microbial CVDs can provide crucial insights into the mechanisms of pathogenicity. The comparison of CVD associated HPIs with the entire set of HPIs can identify the pathways specific to CVDs. Therefore, topological properties of HPI networks in CVDs and all pathogens was studied using Cytoscape3.

Systems Biology: A Powerful Tool for Drug Development.

The conventional way of characterizing a disease consists of correlating clinical symptoms with pathological findings. Although this approach for many years has assisted clinicians in establishing syndromic patterns for pathophenotypes, it has major limitations as it does not consider preclinical disease states and is unable to individualize medicine. Moreover, the complexity of disease biology is the major challenge in the development of effective and safe medicines.

Modeling, dynamics and phosphoinositide binding of the pleckstrin homology domain of two novel PLCs: η1 and η2.

PH domains mediate interactions involved in cell signaling, intracellular membrane transport regulation and cytoskeleton organization. Some PH domains bind phosphoinositides with different affinity and specificity. The two novel PLCη (1 and 2) possess an N-terminal PH domain (PHη1 and PHη2 respectively) that has been implicated in membrane association and induction of PLC activity.

Novel Lipidomic Biomarkers in Hyperlipidemia and Cardiovascular Diseases: An Integrative Biology Analysis.

Lipidomics is a new frontier of omics research and offers much promise for new-generation biomarkers for common complex phenotypes such as hyperlipidemia (HL) and cardiovascular diseases (CVDs). HL is a disorder characterized by increased levels of blood lipids and is a well-established risk factor for CVD. Traditional clinical markers for prognosis of hyperlipidemic individuals are inadequate to forecast or diagnose cardiac events.

Hyperlipidemia, Disease Associations, and Top 10 Potential Drug Targets: A Network View.

The prevalence of acquired hyperlipidemia has increased due to sedentary life style and lipid-rich diet. In this work, a lipid-protein-protein interaction network (LPPIN) for acquired hyperlipidemia was prepared by incorporating differentially expressed genes in obese fatty liver as seed nodes, protein interactions from PathwayLinker, and lipid interactions from STITCH4.0.

Modeling of human M1 aminopeptidases for in silico screening of potential Plasmodium falciparum alanine aminopeptidase (PfA-M1) specific inhibitors.

Plasmodium falciparum alanine M1-aminopeptidase (PfA-M1) is a validated target for anti-malarial drug development. Presence of significant similarity between PfA-M1 and human M1-aminopeptidases, particularly within regions of enzyme active site leads to problem of non-specificity and off-target binding for known aminopeptidase inhibitors. Molecular docking based in silico screening approach for off-target binding has high potential but requires 3D-structure of all human M1-aminopeptidaes.