Mining functional gene modules by multi-view NMF of phenome-genome association.

Background: Mining functional gene modules from genomic data is an important step to detect gene members of pathways or other relations such as protein-protein interactions. This work explores the plausibility of detecting functional gene modules by factorizing gene-phenotype association matrix from the phenotype ontology data rather than the conventionally used gene expression data. Recently, the hierarchical structure of phenotype ontologies has not been sufficiently utilized in gene clustering while functionally related genes are consistently associated with phenotypes on the same path in phenotype ontologies.

Leveraging maximum entropy and correlation on latent factors for learning representations.

Many tasks involve learning representations from matrices, and Non-negative Matrix Factorization (NMF) has been widely used due to its excellent interpretability. Through factorization, sample vectors are reconstructed as additive combinations of latent factors, which are represented as non-negative distributions over the raw input features. NMF models are significantly affected by latent factors' distribution characteristics and the correlations among them.

GC[Formula: see text]NMF: A Novel Matrix Factorization Framework for Gene-Phenotype Association Prediction.

Gene-phenotype association prediction can be applied to reveal the inherited basis of human diseases and facilitate drug development. Gene-phenotype associations are related to complex biological processes and influenced by various factors, such as relationship between phenotypes and that among genes. While due to sparseness of curated gene-phenotype associations and lack of integrated analysis of the joint effect of multiple factors, existing applications are limited to prediction accuracy and potential gene-phenotype association detection.

Prioritizing disease genes with an improved dual label propagation framework.

Background: Prioritizing disease genes is trying to identify potential disease causing genes for a given phenotype, which can be applied to reveal the inherited basis of human diseases and facilitate drug development. Our motivation is inspired by label propagation algorithm and the false positive protein-protein interactions that exist in the dataset. To the best of our knowledge, the false positive protein-protein interactions have not been considered before in disease gene prioritization.

Metrical Consistency NMF for Predicting Gene-Phenotype Associations.

Discovering gene-phenotype associations is significant to understand the disease mechanisms. Nonnegative matrix factorization (NMF) has been widely used in computational biology for its good performance and interpretability. In this paper, we proposed a novel metrical consistency NMF (MCNMF) method for candidate gene prioritization.

Exploring feasibility of multicolored CdTe quantum dots for in vitro and in vivo fluorescent imaging.

We report the use of novel multicolored CdTe quantum dots (QDs) as fluorophores for biological fluorescence imaging. The CdTe QDs were prepared to exhibit emission wavelengths in the green, yellow, and red range by using trifluoroacetic acid (TFA), L-cysteine and thioglycolic acid (TGA) as surface stabilizers, respectively. The particles have good water solubility and photostability.

Evaluation of red CdTe and near infrared CdHgTe quantum dots by fluorescent imaging.

Non-invasive fluorescent imaging of preclinical animal models in vivo is a rapidly developing field with new emerging technologies and techniques. Quantum dot (QD) fluorescent probes with longer emission wavelengths in red and near infrared (NIR) emission ranges are more amenable to deep-tissue imaging, because both scattering and autofluorescence are reduced as wavelengths are increased. We have designed and synthesized red CdTe and NIR CdHgTe QDs for fluorescent imaging.