Prediction and Validation of Gene-Disease Associations Using Methods Inspired by Social Network Analyses.

Prediction and validation of gene-disease associations using methods inspired by social network analyses.

PLoS One. 2013; 8(5): e58977
Singh-Blom UM, Natarajan N, Tewari A, Woods JO, Dhillon IS, Marcotte EM

Correctly identifying associations of genes with diseases has long been a goal in biology. With the emergence of large-scale gene-phenotype association datasets in biology, we can leverage statistical and machine learning methods to help us achieve this goal. In this paper, we present two methods for predicting gene-disease associations based on functional gene associations and gene-phenotype associations in model organisms. The first method, the Katz measure, is motivated from its success in social network link prediction, and is very closely related to some of the recent methods proposed for gene-disease association inference. The second method, called Catapult (Combining dATa Across species using Positive-Unlabeled Learning Techniques), is a supervised machine learning method that uses a biased support vector machine where the features are derived from walks in a heterogeneous gene-trait network. We study the performance of the proposed methods and related state-of-the-art methods using two different evaluation strategies, on two distinct data sets, namely OMIM phenotypes and drug-target interactions. Finally, by measuring the performance of the methods using two different evaluation strategies, we show that even though both methods perform very well, the Katz measure is better at identifying associations between traits and poorly studied genes, whereas Catapult is better suited to correctly identifying gene-trait associations overall. The authors want to thank Jon Laurent and Kris McGary for some of the data used, and Li and Patra for making their code available. Most of Ambuj Tewari’s contribution to this work happened while he was a postdoctoral fellow at the University of Texas at Austin. HubMed – drug

Upconversion nanoparticles for photodynamic therapy and other cancer therapeutics.

Theranostics. 2013; 3(5): 317-30
Wang C, Cheng L, Liu Z

Photodynamic therapy (PDT) is a non-invasive treatment modality for a variety of diseases including cancer. PDT based on upconversion nanoparticles (UCNPs) has received much attention in recent years. Under near-infrared (NIR) light excitation, UCNPs are able to emit high-energy visible light, which can activate surrounding photosensitizer (PS) molecules to produce singlet oxygen and kill cancer cells. Owing to the high tissue penetration ability of NIR light, NIR-excited UCNPs can be used to activate PS molecules in much deeper tissues compared to traditional PDT induced by visible or ultraviolet (UV) light. In addition to the application of UCNPs as an energy donor in PDT, via similar mechanisms, they could also be used for the NIR light-triggered drug release or activation of ‘caged’ imaging or therapeutic molecules. In this review, we will summarize the latest progresses regarding the applications of UCNPs for photodynamic therapy, NIR triggered drug and gene delivery, as well as several other UCNP-based cancer therapeutic approaches. The future prospects and challenges in this emerging field will be also discussed. HubMed – drug

Multifunctional upconversion-magnetic hybrid nanostructured materials: synthesis and bioapplications.

Theranostics. 2013; 3(5): 292-305
Li X, Zhao D, Zhang F

THE COMBINATION OF NANOTECHNOLOGY AND BIOLOGY HAS DEVELOPED INTO AN EMERGING RESEARCH AREA: nano-biotechnology. Upconversion nanoparticles (UCNPs) have attracted a great deal of attention in bioapplications due to their high chemical stability, low toxicity, and high signal-to-noise ratio. Magnetic nanoparticles (MNPs) are also well-established nanomaterials that offer controlled size, ability to be manipulated externally, and enhancement of contrast in magnetic resonance imaging (MRI). As a result, these nanoparticles could have many applications in biology and medicine, including protein purification, drug delivery, and medical imaging. Because of the potential benefits of multimodal functionality in biomedical applications, researchers would like to design and fabricate multifunctional upconversion-magnetic hybrid nanostructured materials. The hybrid nanostructures, which combine UCNPs with MNPs, exhibit upconversion fluorescence alongside superparamagnetism property. Such structures could provide a platform for enhanced bioimaging and controlled drug delivery. We expect that the combination of unique structural characteristics and integrated functions of multifunctional upconversion-magnetic nanoparticles will attract increasing research interest and could lead to new opportunities in nano-bioapplications. HubMed – drug

Chethana Trust: Mysore, India – A brief look into the Chethana trust of Mysore India. The trust is in collaboration with the Sri K. Pattabhi Jois charitable trust of Mysore India. Together …