HENA, heterogeneous network-based data set for Alzheimer’s disease

Sügis, Elena; Dauvillier, Jérôme; Leontjeva, Anna; Adler, Priit; Hindie, Valérie; Moncion, Thomas; Collura, Vincent; Daudin, Rachel; Loe-Mie, Yann; Hérault, Yann; Lambert, Jean‐Charles; Hermjakob, Henning; Pupko, Tal; Rain, Jean-Christophe; Xénarios, Ioannis; Vilo, Jaak; Simonneau, Michel; Peterson, Hedi

doi:10.1038/s41597-019-0152-0

Cited by 40 publications

(23 citation statements)

References 81 publications

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“…While we had mainly studied common diseases (vascular heart diseases in particular) and trained deep learning models, this resource could not directly be used for COVID-2019, given substantial differences between them and the virus epidemic. However, machine learning-based network approaches have improved our capacity to analyse big data at the systems level 32 , and we could build, in the future, a viral-disease-dedicated analysis pipeline using virus-related data for training.…”

Section: Discussionmentioning

confidence: 99%

Network Bioinformatics Analysis Provides Insight into Drug Repurposing for COVID-2019

Li¹,

Yu²,

Zhang³

et al. 2020

Preprint

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The COVID-2019 disease caused by the SARS-CoV-2 virus (aka 2019-nCoV) has raised significant health concerns in China and worldwide. While novel drug discovery and vaccine studies are long, repurposing old drugs against the COVID-2019 epidemic can help identify treatments, with known preclinical, pharmacokinetic, pharmacodynamic, and toxicity profiles, which can rapidly enter Phase 3 or 4 or can be used directly in clinical settings. In this study, we presented a novel network based drug repurposing platform to identify potential drugs for the treatment of COVID-2019. We first analysed the genome sequence of SARS-CoV-2 and identified SARS as the closest disease, based on genome similarity between both causal viruses, followed by MERS and other human coronavirus diseases. Using our AutoSeed pipeline (text mining and database searches), we obtained 34 COVID-2019-related genes. Taking those genes as seeds, we automatically built a molecular network for which our module detection and drug prioritization algorithms identified 24 disease-related human pathways, five modules and finally suggested 78 drugs to repurpose. Following manual filtering based on clinical knowledge, we re-prioritized 30 potential repurposable drugs against COVID-2019 (including pseudoephedrine, andrographolide, chloroquine, abacavir, and thalidomide) . We hope that this data can provide critical insights into SARS-CoV-2 biology and help design rapid clinical trials of treatments against COVID-2019.

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Section: Discussionmentioning

confidence: 99%

Network Bioinformatics Analysis Provides Insight into Drug Repurposing for COVID-2019

Li¹,

Yu²,

Zhang³

et al. 2020

Preprint

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“…For the PPI network, we try different confidence threshold values for filtering IntAct. 0.45 threshold value is commonly used in the literature to filter out the interactions with low confidence [Sügis et al, 2019, Porras et al, 2020]. Additionally, to observe the effect of using a lower threshold than the current one, we also tried filtering IntAct with a threshold value of 0.25.…”

Section: Resultsmentioning

confidence: 99%

A Network-centric Framework for the Evaluation of Mutual Exclusivity Tests on Cancer Drivers

Ahmed

Erten

Houdjedj

et al. 2021

Preprint

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One of the key concepts employed in cancer driver gene identification is that of mutual exclusivity (ME); a driver mutation is less likely to occur in case of an earlier mutation that has common functionality in the same molecular pathway. Several ME tests have been proposed recently, however the current protocols to evaluate ME tests have two main limitations. Firstly the evaluations are mostly with respect to simulated data and secondly the evaluation metrics lack a network-centric view. The latter is especially crucial as the notion of common functionality can be achieved through searching for interaction patterns in relevant networks. We propose a network-centric framework to evaluate the pairwise significances found by statistical ME tests. It has three main components. The first component consists of metrics employed in the network-centric ME evaluations. Such metrics are designed so that network knowledge and the reference set of known cancer genes are incorporated in ME evaluations under a careful definition of proper control groups. The other two components are designed as further mechanisms to avoid confounders inherent in ME detection on top of the network-centric view. To this end, our second objective is to dissect the side effects caused by mutation load artifacts where mutations driving tumor subtypes with low mutation load might be incorrectly diagnosed as mutually exclusive. Finally, as part of the third main component, the confounding issue stemming from the use of nonspecific interaction networks generated as combinations of interactions from different tissues is resolved through the creation and use of tissue-specific networks in the proposed framework. The data, the source code and useful scripts are available at: https://github.com/abu-compbio/NetCentric.

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“…Along with the characterization of amyloid fibrils, ML approaches have been utilized for identifying potential drug targets. HENA, 414 a heterogeneous network‐based data set for AD, integrates distinct data types (i.e., PPI, gene coexpression, epistasis, genome‐wide association study, gene expression in different brain regions, and positive selection data) through GCN to predict AD‐associated genes.…”

Section: Ai/ml Applications In Cns Drug Discoverymentioning

confidence: 99%

Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions

Vatansever

Schlessinger

Wacker

et al. 2020

Medicinal Research Reviews

182

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Neurological disorders significantly outnumber diseases in other therapeutic areas. However, developing drugs for central nervous system (CNS) disorders remains the most challenging area in drug discovery, accompanied with the long timelines and high attrition rates. With the rapid growth of biomedical data enabled by advanced experimental technologies, artificial intelligence (AI) and machine learning (ML) have emerged as an indispensable tool to draw meaningful insights and improve decision making in drug discovery. Thanks to the advancements in AI and ML algorithms, now the AI/ML‐driven solutions have an unprecedented potential to accelerate the process of CNS drug discovery with better success rate. In this review, we comprehensively summarize AI/ML‐powered pharmaceutical discovery efforts and their implementations in the CNS area. After introducing the AI/ML models as well as the conceptualization and data preparation, we outline the applications of AI/ML technologies to several key procedures in drug discovery, including target identification, compound screening, hit/lead generation and optimization, drug response and synergy prediction, de novo drug design, and drug repurposing. We review the current state‐of‐the‐art of AI/ML‐guided CNS drug discovery, focusing on blood–brain barrier permeability prediction and implementation into therapeutic discovery for neurological diseases. Finally, we discuss the major challenges and limitations of current approaches and possible future directions that may provide resolutions to these difficulties.

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HENA, heterogeneous network-based data set for Alzheimer’s disease

Cited by 40 publications

References 81 publications

Network Bioinformatics Analysis Provides Insight into Drug Repurposing for COVID-2019

Network Bioinformatics Analysis Provides Insight into Drug Repurposing for COVID-2019

A Network-centric Framework for the Evaluation of Mutual Exclusivity Tests on Cancer Drivers

Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions

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