Integrative Data Analytic Framework to Enhance Cancer Precision Medicine

Gaudelet, Thomas; Malod-Dognin, Noël; Pržulj, Nataša

doi:10.1089/nsm.2020.0015

Cited by 3 publications

(2 citation statements)

References 58 publications

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“…Another field of development of systems medicine is big data approaches and the application of artificial intelligence (AI) for highly individualized medical care [ 28 , 89 , 90 ]. This approach was intensively discussed by the participants regarding its possible consequences for health care practice.…”

Section: Discussionmentioning

confidence: 99%

A Vision of Future Healthcare: Potential Opportunities and Risks of Systems Medicine from a Citizen and Patient Perspective—Results of a Qualitative Study

Lemmen

Simić

Stock

2021

IJERPH

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Advances in (bio)medicine and technological innovations make it possible to combine high-dimensional, heterogeneous health data to better understand causes of diseases and make them usable for predictive, preventive, and precision medicine. This study aimed to determine views on and expectations of “systems medicine” from the perspective of citizens and patients in six focus group interviews, all transcribed verbatim and content analyzed. A future vision of the use of systems medicine in healthcare served as a stimulus for the discussion. The results show that although certain aspects of systems medicine were seen positive (e.g., use of smart technology, digitalization, and networking in healthcare), the perceived risks dominated. The high degree of technification was perceived as emotionally burdensome (e.g., reduction of people to their data, loss of control, dehumanization). The risk-benefit balance for the use of risk-prediction models for disease events and trajectories was rated as rather negative. There were normative and ethical concerns about unwanted data use, discrimination, and restriction of fundamental rights. These concerns and needs of citizens and patients must be addressed in policy frameworks and health policy implementation strategies to reduce negative emotions and attitudes toward systems medicine and to take advantage of its opportunities.

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Section: Discussionmentioning

confidence: 99%

A Vision of Future Healthcare: Potential Opportunities and Risks of Systems Medicine from a Citizen and Patient Perspective—Results of a Qualitative Study

Lemmen

Simić

Stock

2021

IJERPH

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“…From a gene-centric perspective, an embedding space is considered to be functionally organized if genes that participate in similar biological functions are located close in the space ( Gaudelet et al 2021 ). This organization is commonly evaluated by applying various types of clustering methods to the embedding vectors of the genes in the space, followed by functional enrichment analyses of the genes that the clustered vectors correspond to ( Malod-Dognin et al 2019 ).…”

Section: Methodsmentioning

confidence: 99%

A functional analysis of omic network embedding spaces reveals key altered functions in cancer

et al. 2023

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Motivation Advances in omics technologies have revolutionized cancer research by producing massive datasets. Common approaches to deciphering these complex data are by embedding algorithms of molecular interaction networks. These algorithms find a low-dimensional space in which similarities between the network nodes are best preserved. Currently available embedding approaches mine the gene embeddings directly to uncover new cancer-related knowledge. However, these gene-centric approaches produce incomplete knowledge, since they do not account for the functional implications of genomic alterations. We propose a new, function-centric perspective and approach, to complement the knowledge obtained from omic data. Results We introduce our Functional Mapping Matrix to explore the functional organization of different tissue-specific and species-specific embedding spaces generated by a Non-negative Matrix Tri-Factorization algorithm. Also, we use our FMM to define the optimal dimensionality of these molecular interaction network embedding spaces. For this optimal dimensionality, we compare the FMMs of the most prevalent cancers in human to FMMs of their corresponding control tissues. We find that cancer alters the positions in the embedding space of cancer-related functions, while it keeps the positions of the non-cancer-related ones. We exploit this spacial “movement” to predict novel cancer-related functions. Finally, we predict novel cancer-related genes that the currently available methods for gene-centric analyses cannot identify; we validate these predictions by literature curation and retrospective analyses of patient survival data. Availability Data and source code can be accessed at https://github.com/gaiac/FMM Supplementary information Supplementary data are available at Bioinformatics online.

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Current and future directions in network biology

Zitnik,

Li,

Wells

et al. 2024

Bioinformatics Advances

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Summary Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology, focusing on molecular/cellular networks but also on other biological network types such as biomedical knowledge graphs, patient similarity networks, brain networks, and social/contact networks relevant to disease spread. In more detail, we highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on future directions of network biology. Additionally, we discuss scientific communities, educational initiatives, and the importance of fostering diversity within the field. This article establishes a roadmap for an immediate and long-term vision for network biology. Availability and implementation Not applicable.

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Integrative Data Analytic Framework to Enhance Cancer Precision Medicine

Cited by 3 publications

References 58 publications

A Vision of Future Healthcare: Potential Opportunities and Risks of Systems Medicine from a Citizen and Patient Perspective—Results of a Qualitative Study

A Vision of Future Healthcare: Potential Opportunities and Risks of Systems Medicine from a Citizen and Patient Perspective—Results of a Qualitative Study

A functional analysis of omic network embedding spaces reveals key altered functions in cancer

Current and future directions in network biology

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