A side effect resource to capture phenotypic effects of drugs

Kuhn, Michael; Campillos, Mónica; Letunić, Ivica; Jensen, Lars Juhl; Bork, Peer

doi:10.1038/msb.2009.98

Cited by 768 publications

(682 citation statements)

References 15 publications

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“…A detailed understanding of how symptoms relate to underlying molecular processes is therefore central for our efforts towards more effective and individualized treatments. First attempts in this direction have been proposed recently in drug design, using for example phenotype screening or the similarities of side-effects 49 , which are also most often observed and reported as clinical symptoms 50 . Our comprehensive symptom-based disease relationships may provide valuable input for such approaches.…”

Section: Discussionmentioning

confidence: 99%

Human symptoms–disease network

Zhou¹,

Menche²,

Barabási³

et al. 2014

Nat Commun

571

484

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In the post-genomic era, the elucidation of the relationship between the molecular origins of diseases and their resulting phenotypes is a crucial task for medical research. Here, we use a large-scale biomedical literature database to construct a symptom-based human disease network and investigate the connection between clinical manifestations of diseases and their underlying molecular interactions. We find that the symptom-based similarity of two diseases correlates strongly with the number of shared genetic associations and the extent to which their associated proteins interact. Moreover, the diversity of the clinical manifestations of a disease can be related to the connectivity patterns of the underlying protein interaction network. The comprehensive, high-quality map of disease-symptom relations can further be used as a resource helping to address important questions in the field of systems medicine, for example, the identification of unexpected associations between diseases, disease etiology research or drug design.

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Section: Discussionmentioning

confidence: 99%

Human symptoms–disease network

Zhou¹,

Menche²,

Barabási³

et al. 2014

Nat Commun

571

484

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“…Overall, we considered five drug-drug similarities and three gene-gene similarities from different biological and chemical sources. The drug-drug similarity measures were computed using chemical, registered and predicted drug side effects (Kuhn et al, 2010) of the drug, drug response gene expression profiles, and the Anatomical, Therapeutic and Chemical (ATC) classification system. The gene-gene similarity measures used are based on sequence, closeness in a protein-protein interaction network, and semantic Gene 134 PERLMAN ET AL.…”

Section: Similarity Measuresmentioning

confidence: 99%

“…(4) Side-effect based: Drug side effects were obtained from SIDER (Kuhn et al, 2010), an online database containing drug side effects associations extracted from package inserts using text mining methods. Recently, we developed an algorithmic framework to predict side effects for drugs by combining side effect information on known drugs with their chemical properties (Atias and Sharan, 2010).…”

Section: Similarity-based Drug-target Predictionmentioning

confidence: 99%

Combining Drug and Gene Similarity Measures for Drug-Target Elucidation

Perlman¹,

Gottlieb

Atias

et al. 2011

Journal of Computational Biology

172

131

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Understanding drugs and their modes of action is a fundamental challenge in systems medicine. Key to addressing this challenge is the elucidation of drug targets, an important step in the search for new drugs or novel targets for existing drugs. Incorporating multiple biological information sources is of essence for improving the accuracy of drug target prediction. In this article, we introduce a novel framework-Similarity-based Inference of drug-TARgets (SITAR)-for incorporating multiple drug-drug and gene-gene similarity measures for drug target prediction. The framework consists of a new scoring scheme for drug-gene associations based on a given pair of drug-drug and gene-gene similarity measures, combined with a logistic regression component that integrates the scores of multiple measures to yield the final association score. We apply our framework to predict targets for hundreds of drugs using both commonly used and novel drug-drug and gene-gene similarity measures and compare our results to existing state of the art methods, markedly outperforming them. We then employ our framework to make novel target predictions for hundreds of drugs; we validate these predictions via curated databases that were not used in the learning stage. Our framework provides an extensible platform for incorporating additional emerging similarity measures among drugs and genes. Supplementary Material is available at www.liebertonline .com/cmb.

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“…The hive driver receives the tasks(Queries) [13] from user and send to Hadoop architecture.The Hadoop architecture uses name node, data node, job tracker and task tracker for receiving and dividing the work what Hive sends to Hadoop The Patient or doctor can log in using the web user interface [14]. The admin can add multiple disease and their symptoms to train the machine.…”

Section: International Journal For Research In Applied Science and Engimentioning

confidence: 99%

Analysis of Multi-Disease & Prediction of Suitable Drug for Healthcare Application using Bigdata

Patidar¹

2017

IJRASET

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The Healthcare technologies have grown immensely in various domains. These technologies have also made the health care data huge making it difficult to process. Several precautions should be taken using pharmaceutical drugs, for both healthcare professionals, who prescribe and administer drugs, and for drug consumers. To achieve this goal, we integrate the massive data using Hadoop and perform a wide range of medical and healthcare functions to infer knowledge that assist in diagnosis of disease and provide the best drug. User can post the query through system. We also arrange appointment to the Best Doctor for the consultation based on user feedbacks.

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A side effect resource to capture phenotypic effects of drugs

Cited by 768 publications

References 15 publications

Human symptoms–disease network

Human symptoms–disease network

Combining Drug and Gene Similarity Measures for Drug-Target Elucidation

Analysis of Multi-Disease & Prediction of Suitable Drug for Healthcare Application using Bigdata

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