Literature DB >> 24969134

Cancer modeling and network biology: accelerating toward personalized medicine.

Ali Masoudi-Nejad1, Edwin Wang2.   

Abstract

The complexity of cancer progression can manifests itself on at least three scales that can be described using mathematical models, namely microscopic, mesoscopic and macroscopic scales. Multiscale cancer models have proven to be advantageous in this context because they can simultaneously incorporate the many different characteristics and scales of complex diseases such as cancer. This has driven the expansion of more predictive data-driven models, coupled to experimental and clinical data. These models are defining the foundations that facilitate the forthcoming design of patient specific cancer therapy. This should be considered as a great leap toward the era of personalized medicine. Consequently, further improvements in mathematical modeling of cancer will lead to the design of more sophisticated cancer therapy approaches.
Copyright © 2014 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Cancer modeling; Multiscale model; Network reconstruction; Personalized medicine

Mesh:

Year:  2014        PMID: 24969134     DOI: 10.1016/j.semcancer.2014.06.005

Source DB:  PubMed          Journal:  Semin Cancer Biol        ISSN: 1044-579X            Impact factor:   15.707


  9 in total

Review 1.  Innovations in Genomics and Big Data Analytics for Personalized Medicine and Health Care: A Review.

Authors:  Mubashir Hassan; Faryal Mehwish Awan; Anam Naz; Enrique J deAndrés-Galiana; Oscar Alvarez; Ana Cernea; Lucas Fernández-Brillet; Juan Luis Fernández-Martínez; Andrzej Kloczkowski
Journal:  Int J Mol Sci       Date:  2022-04-22       Impact factor: 6.208

2.  FMLNCSIM: fuzzy measure-based lncRNA functional similarity calculation model.

Authors:  Xing Chen; Yu-An Huang; Xue-Song Wang; Zhu-Hong You; Keith C C Chan
Journal:  Oncotarget       Date:  2016-07-19

3.  Mechanistic modeling quantifies the influence of tumor growth kinetics on the response to anti-angiogenic treatment.

Authors:  Thomas D Gaddy; Qianhui Wu; Alyssa D Arnheim; Stacey D Finley
Journal:  PLoS Comput Biol       Date:  2017-12-21       Impact factor: 4.475

4.  Inferring clonal structure in HTLV-1-infected individuals: towards bridging the gap between analysis and visualization.

Authors:  Amir Farmanbar; Sanaz Firouzi; Wojciech Makałowski; Masako Iwanaga; Kaoru Uchimaru; Atae Utsunomiya; Toshiki Watanabe; Kenta Nakai
Journal:  Hum Genomics       Date:  2017-07-11       Impact factor: 4.639

5.  In silico mouse study identifies tumour growth kinetics as biomarkers for the outcome of anti-angiogenic treatment.

Authors:  Qianhui Wu; Alyssa D Arnheim; Stacey D Finley
Journal:  J R Soc Interface       Date:  2018-08       Impact factor: 4.118

Review 6.  Deep sea as a source of novel-anticancer drugs: update on discovery and preclinical/clinical evaluation in a systems medicine perspective.

Authors:  Patrizia Russo; Alessandra Del Bufalo; Massimo Fini
Journal:  EXCLI J       Date:  2015-02-10       Impact factor: 4.068

7.  A Generic Individual-Based Spatially Explicit Model as a Novel Tool for Investigating Insect-Plant Interactions: A Case Study of the Behavioural Ecology of Frugivorous Tephritidae.

Authors:  Ming Wang; Bronwen Cribb; Anthony R Clarke; Jim Hanan
Journal:  PLoS One       Date:  2016-03-21       Impact factor: 3.240

8.  IRWRLDA: improved random walk with restart for lncRNA-disease association prediction.

Authors:  Xing Chen; Zhu-Hong You; Gui-Ying Yan; Dun-Wei Gong
Journal:  Oncotarget       Date:  2016-09-06

Review 9.  Analyzing of Molecular Networks for Human Diseases and Drug Discovery.

Authors:  Tong Hao; Qian Wang; Lingxuan Zhao; Dan Wu; Edwin Wang; Jinsheng Sun
Journal:  Curr Top Med Chem       Date:  2018       Impact factor: 3.295

  9 in total

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