Design protein-protein interaction network and protein-drug interaction network for common cancer diseases: A bioinformatics approach

Hasan, Md. Rakibul; Paul, Bikash Kumar; Ahmed, Kawsar; Bhuyian, Touhid

doi:10.1016/j.imu.2020.100311

Cited by 25 publications

(14 citation statements)

References 23 publications

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“…Nevertheless, Zhou and Kosorok (2017) method is also applicable to biological networks, such as gene networks or protein-protein networks, when one wants to identify nodes or pathways affected by drug treatments or when one wants to design drug repurposing strategies, or drug combination prediction, and more in general to identify differences between networks of different patients. In this respect, we refer the reader to some significant recent studies such as ( Feng et al, 2017 ; Cheng et al, 2019 ; Hasan et al, 2020 ; Lu et al, 2020 ; Adhami et al, 2021 ; Ruiz et al, 2021 ; Somolinos et al, 2021 ). In this context, the crucial step is the treatment selection rule, or optimal treatment regime.…”

Section: Machine Learning and Structural Causal Modelsmentioning

confidence: 99%

Machine Learning for Causal Inference in Biological Networks: Perspectives of This Challenge

Lecca

2021

Front. Bioinform.

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Most machine learning-based methods predict outcomes rather than understanding causality. Machine learning methods have been proved to be efficient in finding correlations in data, but unskilful to determine causation. This issue severely limits the applicability of machine learning methods to infer the causal relationships between the entities of a biological network, and more in general of any dynamical system, such as medical intervention strategies and clinical outcomes system, that is representable as a network. From the perspective of those who want to use the results of network inference not only to understand the mechanisms underlying the dynamics, but also to understand how the network reacts to external stimuli (e. g. environmental factors, therapeutic treatments), tools that can understand the causal relationships between data are highly demanded. Given the increasing popularity of machine learning techniques in computational biology and the recent literature proposing the use of machine learning techniques for the inference of biological networks, we would like to present the challenges that mathematics and computer science research faces in generalising machine learning to an approach capable of understanding causal relationships, and the prospects that achieving this will open up for the medical application domains of systems biology, the main paradigm of which is precisely network biology at any physical scale.

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Section: Machine Learning and Structural Causal Modelsmentioning

confidence: 99%

Machine Learning for Causal Inference in Biological Networks: Perspectives of This Challenge

Lecca

2021

Front. Bioinform.

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“…The integration of these networks, both vertically and horizontally, can highlight clusters of proteins with central roles, aiding the understanding of drug action mechanisms ( Martin, Roe and Faulon, 2005 ; Dimitrakopoulos et al, 2021 ; Marín-Llaó et al, 2021 ; Tomkins and Manzoni, 2021 ). PPI networks offer prospects in many fields, such as medicine, health and also in agri-food ( Hao et al, 2019 ; Hasan et al, 2020 ; Thanasomboon et al, 2020 ; Charmpi et al, 2021 ). Vertical and horizontal integration algorithms are mainly based on propagation and alignment algorithms but are often combined with machine learning methods to predict the probability of reliability of an interaction ( Li and Ilie, 2017 ; Lee and Nam, 2018 ; Zhang et al, 2018 ; Das and Chakrabarti, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context

Robin

Bodein

Scott‐Boyer

et al. 2022

Front. Mol. Biosci.

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At the heart of the cellular machinery through the regulation of cellular functions, protein–protein interactions (PPIs) have a significant role. PPIs can be analyzed with network approaches. Construction of a PPI network requires prediction of the interactions. All PPIs form a network. Different biases such as lack of data, recurrence of information, and false interactions make the network unstable. Integrated strategies allow solving these different challenges. These approaches have shown encouraging results for the understanding of molecular mechanisms, drug action mechanisms, and identification of target genes. In order to give more importance to an interaction, it is evaluated by different confidence scores. These scores allow the filtration of the network and thus facilitate the representation of the network, essential steps to the identification and understanding of molecular mechanisms. In this review, we will discuss the main computational methods for predicting PPI, including ones confirming an interaction as well as the integration of PPIs into a network, and we will discuss visualization of these complex data.

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“…Investigation of protein-drug interactions (PDI) is critical to understand the theoretical underpinnings of agonist selectivity [ 39 , 40 ]. A medicament should first establish the drug-receptor complexes before it can interact with receptor sites or be metabolized by bioindicators [ 41 ].…”

Section: Methodsmentioning

confidence: 99%

“…A medicament should first establish the drug-receptor complexes before it can interact with receptor sites or be metabolized by bioindicators [ 41 ]. Computational approaches can be used to anticipate proteomics for a specific therapeutic agent or to react with medicines for specific target molecules or proteins [ 39 , 42 ]. NetworkAnalyst was used to predict protein-drug interactions in our study [ 43 ].…”

Section: Methodsmentioning

confidence: 99%

Bioinformatics approach to analyse COVID-19 biomarkers accountable for generation of intracranial aneurysm in COVID-19 patients

Snigdha

Akter

Amin

et al. 2023

Informatics in Medicine Unlocked

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Design protein-protein interaction network and protein-drug interaction network for common cancer diseases: A bioinformatics approach

Cited by 25 publications

References 23 publications

Machine Learning for Causal Inference in Biological Networks: Perspectives of This Challenge

Machine Learning for Causal Inference in Biological Networks: Perspectives of This Challenge

Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context

Bioinformatics approach to analyse COVID-19 biomarkers accountable for generation of intracranial aneurysm in COVID-19 patients

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