scholarly journals Construction of the multi-layer omics data generation / analysis platform that contributes to the precision medicine of COVID-19

2021 ◽  
Vol 94 (0) ◽  
pp. 1-S01-2
Author(s):  
Yumiko Imai
Keyword(s):  
Precision Medicine ◽  
Omics Data ◽  
Data Generation ◽  
Analysis Platform

MOBCdb: a comprehensive database integrating multi-omics data on breast cancer for precision medicine

2018 ◽  
Vol 169 (3) ◽  
pp. 625-632 ◽  
Author(s):  
Bingbing Xie ◽  
Zifeng Yuan ◽  
Yadong Yang ◽  
Zhidan Sun ◽  
Shuigeng Zhou ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Precision Medicine ◽  
Omics Data ◽  
Comprehensive Database

Precision Medicine Approach in Prostate Cancer

2020 ◽  
Vol 26 (31) ◽  
pp. 3783-3798
Author(s):  
Majid Assadi ◽  
Narges Jokar ◽  
Mojtaba Ghasemi ◽  
Iraj Nabipour ◽  
Ali Gholamrezanezhad ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Precision Medicine ◽  
Metabolic Pathways ◽  
Locally Advanced ◽  
Omics Data ◽  
Prostate Cancers ◽  
New Treatments ◽  
Treatment Procedures ◽  
The Impact

Prostate cancer is the most prevalent type of cancer and the second cause of death in men worldwide. Various diagnostic and treatment procedures are available for this type of malignancy, but High-grade or locally advanced prostate cancers showed the potential to develop to lethal phase that can be causing dead. Therefore, new approaches are needed to prolong patients’ survival and to improve their quality of life. Precision medicine is a novel emerging field that plays an essential role in identifying new sub-classifications of diseases and in providing guidance in treatment that is based on individual multi-omics data. Multi-omics approaches include the use of genomics, transcriptomics, proteomics, metabolomics, epigenomics and phenomics data to unravel the complexity of a disease-associated biological network, to predict prognostic biomarkers, and to identify new targeted drugs for individual cancer patients. We review the impact of multi-omics data in the framework of systems biology in the era of precision medicine, emphasising the combination of molecular imaging modalities with highthroughput techniques and the new treatments that target metabolic pathways involved in prostate cancer.

A Comparative Analysis of Biological Data Integration Systems Famous for Data Exploitation and Knowledge Discovery

2021 ◽  
Vol 15 ◽  
Author(s):  
Omer Irshad ◽  
Muhammad Usman Ghani Khan
Keyword(s):  
Comparative Analysis ◽  
Data Integration ◽  
Biological Data ◽  
Omics Data ◽  
Data Generation ◽  
Biological Databases ◽  
Future Data ◽  
Design Characteristics ◽  
Biological Data Integration ◽  
Omics Data Integration

: Integrating heterogeneous biological databases for unveiling the new intra-molecular and inter-molecular attributes, behaviors, and relationships in the human cellular system has always been a focused research area of computational biology. In this context, a lot of biological data integration systems have been deployed in the last couple of decades. One of the prime and common objectives of all these systems is to better facilitate the end-users for exploring, exploiting, and analyzing the integrated biological data for knowledge extraction. With the advent of especially highthroughput data generation technologies, biological data is growing and dispersing continuously, exponentially, heterogeneously, and geographically. Due to this, biological data integration systems are too facing data integration and data organization-related current and future challenges. The objective of this review is to quantitatively evaluate and compare some of the recent warehouse-based multi-omics data integration systems to check their compliance with the current and future data integration needs. For this, we identified some of the major data integration design characteristics that should be in the multi-omics data integration model to comprehensively address the current and future data integration challenges. Based on these design characteristics and the evaluation criteria, we evaluated some of the recent data warehouse systems and showed categorical and comparative analysis results. Results show that most of the systems exhibit no or partial compliance with the required data integration design characteristics. So, these systems need design improvements to adequately address the current and future data integration challenges while keeping their service level commitments in place.

scholarly journals Knowledge Network Embedding of Transcriptomic Data from Spaceflown Mice Uncovers Signs and Symptoms Associated with Terrestrial Diseases

Life ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 42
Author(s):  
Charlotte A. Nelson ◽  
Ana Uriarte Acuna ◽  
Amber M. Paul ◽  
Ryan T. Scott ◽  
Atul J. Butte ◽  
...  
Keyword(s):  
Precision Medicine ◽  
Signs And Symptoms ◽  
Knowledge Network ◽  
Knowledge Graph ◽  
Space Travel ◽  
Omics Data ◽  
Physiological Changes ◽  
Network Embedding ◽  
Transcriptomic Data ◽  
Heterogeneous Knowledge

There has long been an interest in understanding how the hazards from spaceflight may trigger or exacerbate human diseases. With the goal of advancing our knowledge on physiological changes during space travel, NASA GeneLab provides an open-source repository of multi-omics data from real and simulated spaceflight studies. Alone, this data enables identification of biological changes during spaceflight, but cannot infer how that may impact an astronaut at the phenotypic level. To bridge this gap, Scalable Precision Medicine Oriented Knowledge Engine (SPOKE), a heterogeneous knowledge graph connecting biological and clinical data from over 30 databases, was used in combination with GeneLab transcriptomic data from six studies. This integration identified critical symptoms and physiological changes incurred during spaceflight.

scholarly journals Practicing precision medicine with intelligently integrative clinical and multi-omics data analysis

2020 ◽  
Vol 14 (1) ◽  
Author(s):  
Zeeshan Ahmed
Keyword(s):  
Machine Learning ◽  
Precision Medicine ◽  
Ethical Issues ◽  
Predictive Biomarkers ◽  
Heterogeneous Data ◽  
Prognostic Models ◽  
Omics Data ◽  
Clinical Predictors ◽  
Personalized Care ◽  
Complex Disorders

Abstract Precision medicine aims to empower clinicians to predict the most appropriate course of action for patients with complex diseases like cancer, diabetes, cardiomyopathy, and COVID-19. With a progressive interpretation of the clinical, molecular, and genomic factors at play in diseases, more effective and personalized medical treatments are anticipated for many disorders. Understanding patient’s metabolomics and genetic make-up in conjunction with clinical data will significantly lead to determining predisposition, diagnostic, prognostic, and predictive biomarkers and paths ultimately providing optimal and personalized care for diverse, and targeted chronic and acute diseases. In clinical settings, we need to timely model clinical and multi-omics data to find statistical patterns across millions of features to identify underlying biologic pathways, modifiable risk factors, and actionable information that support early detection and prevention of complex disorders, and development of new therapies for better patient care. It is important to calculate quantitative phenotype measurements, evaluate variants in unique genes and interpret using ACMG guidelines, find frequency of pathogenic and likely pathogenic variants without disease indicators, and observe autosomal recessive carriers with a phenotype manifestation in metabolome. Next, ensuring security to reconcile noise, we need to build and train machine-learning prognostic models to meaningfully process multisource heterogeneous data to identify high-risk rare variants and make medically relevant predictions. The goal, today, is to facilitate implementation of mainstream precision medicine to improve the traditional symptom-driven practice of medicine, and allow earlier interventions using predictive diagnostics and tailoring better-personalized treatments. We strongly recommend automated implementation of cutting-edge technologies, utilizing machine learning (ML) and artificial intelligence (AI) approaches for the multimodal data aggregation, multifactor examination, development of knowledgebase of clinical predictors for decision support, and best strategies for dealing with relevant ethical issues.

scholarly journals Leveraging Multilayered “Omics” Data for Atopic Dermatitis: A Road Map to Precision Medicine

2018 ◽  
Vol 9 ◽  
Author(s):  
Debajyoti Ghosh ◽  
Jonathan A. Bernstein ◽  
Gurjit K. Khurana Hershey ◽  
Marc E. Rothenberg ◽  
Tesfaye B. Mersha
Keyword(s):  
Atopic Dermatitis ◽  
Precision Medicine ◽  
Omics Data ◽  
Road Map

scholarly journals CatbNet: A Multi Network Analyzer for Comparing and Analyzing the Topology of Biological Networks

2019 ◽  
Vol 20 (1) ◽  
pp. 69-75 ◽  
Author(s):  
Ehsan Pournoor ◽  
Naser Elmi ◽  
Ali Masoudi-Nejad
Keyword(s):  
Biological Networks ◽  
Network Data ◽  
Omics Data ◽  
Network Analyzer ◽  
Cellular Interactions ◽  
Data Generation ◽  
Network Comparison ◽  
Holistic Approaches ◽  
Topological Features ◽  
Generation Network

Background: Complexity and dynamicity of biological events is a reason to use comprehensive and holistic approaches to deal with their difficulty. Currently with advances in omics data generation, network-based approaches are used frequently in different areas of computational biology and bioinformatics to solve problems in a systematic way. Also, there are many applications and tools for network data analysis and manipulation which their goal is to facilitate the way of improving our understandings of inter/intra cellular interactions. Methods: In this article, we introduce CatbNet, a multi network analyzer application which is prepared for network comparison objectives. Result and Conclusion: CatbNet uses many topological features of networks to compare their structure and foundations. One of the most prominent properties of this application is classified network analysis in which groups of networks are compared with each other.

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