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Protein Interaction Mapping

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Computational probing protein-protein interactions targeting small molecules.

Bioinformatics (Oxford, England)
MOTIVATION: With the booming of interactome studies, a lot of interactions can be measured in a high throughput way and large scale datasets are available. It is becoming apparent that many different types of interactions can be potential drug target...

IAS: Interaction Specific GO Term Associations for Predicting Protein-Protein Interaction Networks.

IEEE/ACM transactions on computational biology and bioinformatics
Proteins carry out their function in a cell through interactions with other proteins. A large scale protein-protein interaction (PPI) network of an organism provides static yet an essential structure of interactions, which is valuable clue for unders...

An Unsupervised Graph Based Continuous Word Representation Method for Biomedical Text Mining.

IEEE/ACM transactions on computational biology and bioinformatics
In biomedical text mining tasks, distributed word representation has succeeded in capturing semantic regularities, but most of them are shallow-window based models, which are not sufficient for expressing the meaning of words. To represent words usin...

Network fingerprint: a knowledge-based characterization of biomedical networks.

Scientific reports
It can be difficult for biomedical researchers to understand complex molecular networks due to their unfamiliarity with the mathematical concepts employed. To represent molecular networks with clear meanings and familiar forms for biomedical research...

A roadmap to multifactor dimensionality reduction methods.

Briefings in bioinformatics
Complex diseases are defined to be determined by multiple genetic and environmental factors alone as well as in interactions. To analyze interactions in genetic data, many statistical methods have been suggested, with most of them relying on statisti...

TENET: topological feature-based target characterization in signalling networks.

Bioinformatics (Oxford, England)
MOTIVATION: Target characterization for a biochemical network is a heuristic evaluation process that produces a characterization model that may aid in predicting the suitability of each molecule for drug targeting. These approaches are typically used...

Machine learning assisted design of highly active peptides for drug discovery.

PLoS computational biology
The discovery of peptides possessing high biological activity is very challenging due to the enormous diversity for which only a minority have the desired properties. To lower cost and reduce the time to obtain promising peptides, machine learning ap...

Exploring the relationship between hub proteins and drug targets based on GO and intrinsic disorder.

Computational biology and chemistry
Protein-protein interactions (PPIs) play essential roles in many biological processes. In protein-protein interaction networks, hubs involve in numbers of PPIs and may constitute an important source of drug targets. The intrinsic disorder proteins (I...

Computationally predicting protein-RNA interactions using only positive and unlabeled examples.

Journal of bioinformatics and computational biology
Protein-RNA interactions (PRIs) are considerably important in a wide variety of cellular processes, ranging from transcriptional and post-transcriptional regulations of gene expression to the active defense of host against virus. With the development...

More challenges for machine-learning protein interactions.

Bioinformatics (Oxford, England)
MOTIVATION: Machine learning may be the most popular computational tool in molecular biology. Providing sustained performance estimates is challenging. The standard cross-validation protocols usually fail in biology. Park and Marcotte found that even...