Molecular mechanistic associations of human diseases

Stegmaier, Philip; Krull, Mathias; Voss, Nico; Kel, Alexander; Wingender, Edgar

doi:10.1186/1752-0509-4-124

Cited by 14 publications

(11 citation statements)

References 67 publications

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“…Diabetes is a multigenic disease tightly related to central obesity, atherosclerosis and cardiovascular disease, a connection also revealed by network representations (Ghazalpour et al, 2004; Lusis & Weiss, 2010; Stegmaier et al, 2010). Here we summarize network-related methods to predict novel drug target candidates in diabetes and related metabolic diseases.…”

Section: Four Examples Of Network Description and Analysis In Drugmentioning

confidence: 93%

“…Low clustering coefficient was successfully applied as a discriminatory feature in the prediction of disease-related genes (Sharma et al, 2010a). • Disease-related genes tend to form overlapping disease modules in protein-protein interaction networks showing even a 10-fold increase of physical interactions relative to random expectation (Gandhi et al, 2006;Goh et al, 2007;Oti & Brunner, 2007;Feldman et al, 2008;Jiang et al, 2008;Stegmaier et al, 2010;Bauer-Mehren et al, 2011;Loscalzo & Barabasi, 2011;Xia et al, 2011). Overlaps of disease modules are also characteristic to comorbidity networks (Rzhetsky et al, 2007;Hidalgo et al, 2009).…”

Section: The Human Disease Networkmentioning

confidence: 99%

“…In Section 5 we will summarize the drug-design related applications of these molecular networks in case of four disease families: infections, cancer, diabetes and neurodenegerative diseases. In the next section Goh et al, 2007;Feldman et al, 2008;Bauer-Mehren et al, 2010;Stegmaier et al,…”

Section: The Human Disease Networkmentioning

confidence: 99%

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Structure and dynamics of molecular networks: A novel paradigm of drug discovery

Csermely

Korcsmáros

Kiss

et al. 2013

Pharmacology & Therapeutics

810

661

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Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only gives a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The “central hit strategy” selectively targets central node/edges of the flexible networks of infectious agents or cancer cells to kill them. The “network influence strategy” works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.

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Section: Four Examples Of Network Description and Analysis In Drugmentioning

confidence: 93%

Section: The Human Disease Networkmentioning

confidence: 99%

See 1 more Smart Citation

Structure and dynamics of molecular networks: A novel paradigm of drug discovery

Csermely

Korcsmáros

Kiss

et al. 2013

Pharmacology & Therapeutics

810

661

View full text Add to dashboard Cite

show abstract

“…Historically, diseases have been classified according to different criteria, such as pathology, anatomy and prognosis. More recently, new nosological criteria have been set forth based on molecular biology and genetics, such as disease genes, genetic associations and pathways [ 14 – 18 ]. Here, instead, I have explored the creation of a drug-centric disease taxonomy by classifying diseases according to the drugs they adopt.…”

Section: Resultsmentioning

confidence: 99%

A Drug-Centric View of Drug Development: How Drugs Spread from Disease to Disease

Rodriguez‐Esteban

2016

PLoS Comput Biol

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Drugs are often seen as ancillary to the purpose of fighting diseases. Here an alternative view is proposed in which they occupy a spearheading role. In this view, drugs are technologies with an inherent therapeutic potential. Once created, they can spread from disease to disease independently of the drug creator’s original intentions. Through the analysis of extensive literature and clinical trial records, it can be observed that successful drugs follow a life cycle in which they are studied at an increasing rate, and for the treatment of an increasing number of diseases, leading to clinical advancement. Such initial growth, following a power law on average, has a degree of momentum, but eventually decelerates, leading to stagnation and decay. A network model can describe the propagation of drugs from disease to disease in which diseases communicate with each other by receiving and sending drugs. Within this model, some diseases appear more prone to influence other diseases than be influenced, and vice versa. Diseases can also be organized into a drug-centric disease taxonomy based on the drugs that each adopts. This taxonomy reflects not only biological similarities across diseases, but also the level of differentiation of existing therapies. In sum, this study shows that drugs can become contagious technologies playing a driving role in the fight against disease. By better understanding such dynamics, pharmaceutical developers may be able to manage drug projects more effectively.

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“…Causal molecular mechanisms present a unifying principle for disease classification, analysis of clinical disorder associations, as well as prediction of disease genes, diagnostic markers, and therapeutic targets. A novel approach published (Stegmaier et al, 2010) built of 1000 causal gene-disease networks is now updated and available in the HumanPSD TM database (Wingender et al, 2007). The important features identified using the ML model can serve as biomarkers of survival/prognosis in GBM.…”

Section: Identification Of Biomarkersmentioning

confidence: 99%

Machine Learning based Survival Group Prediction in Glioblastoma<strong> </strong>

Kalya¹,

Kel²,

Leha³

et al. 2022

Preprint

Self Cite

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Glioblastoma (GBM) is a very aggressive malignant brain tumor with the vast majority of patients surviving less than 12 months (Short-term survivors [STS]). Only around 2% of patients survive more than 36 months (Long-term survivors [LTS]). Studying these extreme survival groups might help in better understanding GBM biology. This work aims at exploring application of machine learning methods in predicting survival groups(STS, LTS). We used age and gene expression profiles belonging to 249 samples from publicly available datasets. 10 Machine learning methods have been implemented and compared for their performances. Hyperparameter tuned random forest model performed best with accuracy of 80% (AUC of 74% and F1_score of 85%). The performance of this model is validated on external test data of 16 samples. The model predicted the true survival group for 15 samples achieving an accuracy of 93.75%. This classification model is deployed as a web tool GlioSurvML. The top 1500 features which retained classification efficiency (Accuracy of 80%, AUC of 74%) were studied for enriched pathways and disease-causal biomarker associations using the HumanPSDTM database. We identified 199 genes as possible biomarkers of GBM and/or similar diseases (like Glioma, astrocytoma, and others). 57 of these genes are shown to be differentially expressed across survival groups and/or have impact on survival. This work demonstrates the application of machine learning methods in predicting survival groups of GBM.

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Molecular mechanistic associations of human diseases

Cited by 14 publications

References 67 publications

Structure and dynamics of molecular networks: A novel paradigm of drug discovery

Structure and dynamics of molecular networks: A novel paradigm of drug discovery

A Drug-Centric View of Drug Development: How Drugs Spread from Disease to Disease

Machine Learning based Survival Group Prediction in Glioblastoma<strong> </strong>

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