scholarly journals Computational systems biology for omics data analysis

2019 ◽  
Vol 11 (8) ◽  
pp. 631-632 ◽  
Author(s):  
Luonan Chen
Keyword(s):  
Data Analysis ◽  
Systems Biology ◽  
Omics Data ◽  
Computational Systems Biology ◽  
Computational Systems ◽  
Omics Data Analysis

scholarly journals Metabolic Reprogramming of Fibroblasts as Therapeutic Target in Rheumatoid Arthritis and Cancer: Deciphering Key Mechanisms Using Computational Systems Biology Approaches

Cancers ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 35
Author(s):  
Sahar Aghakhani ◽  
Naouel Zerrouk ◽  
Anna Niarakis
Keyword(s):  
Rheumatoid Arthritis ◽  
Extracellular Matrix ◽  
Systems Biology ◽  
Healing Process ◽  
Metabolic Reprogramming ◽  
Critical Event ◽  
Wound Healing Process ◽  
Computational Systems Biology ◽  
Structural Framework ◽  
Computational Systems

Fibroblasts, the most abundant cells in the connective tissue, are key modulators of the extracellular matrix (ECM) composition. These spindle-shaped cells are capable of synthesizing various extracellular matrix proteins and collagen. They also provide the structural framework (stroma) for tissues and play a pivotal role in the wound healing process. While they are maintainers of the ECM turnover and regulate several physiological processes, they can also undergo transformations responding to certain stimuli and display aggressive phenotypes that contribute to disease pathophysiology. In this review, we focus on the metabolic pathways of glucose and highlight metabolic reprogramming as a critical event that contributes to the transition of fibroblasts from quiescent to activated and aggressive cells. We also cover the emerging evidence that allows us to draw parallels between fibroblasts in autoimmune disorders and more specifically in rheumatoid arthritis and cancer. We link the metabolic changes of fibroblasts to the toxic environment created by the disease condition and discuss how targeting of metabolic reprogramming could be employed in the treatment of such diseases. Lastly, we discuss Systems Biology approaches, and more specifically, computational modeling, as a means to elucidate pathogenetic mechanisms and accelerate the identification of novel therapeutic targets.

Using machine learning approaches for multi-omics data analysis: A review

2021 ◽  
Vol 49 ◽  
pp. 107739
Author(s):  
Parminder S. Reel ◽  
Smarti Reel ◽  
Ewan Pearson ◽  
Emanuele Trucco ◽  
Emily Jefferson
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Omics Data ◽  
Learning Approaches ◽  
Omics Data Analysis

scholarly journals Computational prediction of inter-species relationships through omics data analysis and machine learning

2018 ◽  
Vol 19 (S14) ◽  
Author(s):  
Diogo Manuel Carvalho Leite ◽  
Xavier Brochet ◽  
Grégory Resch ◽  
Yok-Ai Que ◽  
Aitana Neves ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Computational Prediction ◽  
Omics Data ◽  
Species Relationships ◽  
Omics Data Analysis

scholarly journals From data to knowledge: The future of multi-omics data analysis for the rhizosphere

Rhizosphere ◽  
2017 ◽  
Vol 3 ◽  
pp. 222-229 ◽  
Author(s):  
Richard Allen White ◽  
Mark I. Borkum ◽  
Albert Rivas-Ubach ◽  
Aivett Bilbao ◽  
Jason P. Wendler ◽  
...  
Keyword(s):  
Data Analysis ◽  
Omics Data ◽  
The Future ◽  
Omics Data Analysis
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