We have previously demonstrated that Tenascin-C (TNC)(+) human neuroblastoma (NB) cells transdifferentiate into tumor-derived endothelial cells (TDEC), which have been detected both in primary tumors and in tumors formed by human NB cell lines in immunodeficient mice. TDEC are genetically unstable and may favor tumor progression, suggesting that their elimination could reduce tumor growth and d...

BACKGROUND Immunological non-response (INR) despite virological suppression is associated with AIDS-defining events/death (ADE). Little is known about its association with serious non-AIDS-defining events (nADE). METHODS Patients highly-active antiretroviral therapy (HAART) with <200 CD4+/μl and achieving HIV-RNA <50 copies/ml within 12 (±3) months were categorized as INR if CD4+ T-cell count...

Neural Autoregressive Distribution Estimators (NADEs) have recently been shown as successful alternatives for modeling high dimensional multimodal distributions. One issue associated with NADEs is that they rely on a particular order of factorization for P (x). This issue has been recently addressed by a variant of NADE called Orderless NADEs and its deeper version, Deep Orderless NADE. Orderle...

The Neural Autoregressive Distribution Estimator (NADE) and its real-valued version RNADE are competitive density models of multidimensional data across a variety of domains. These models use a fixed, arbitrary ordering of the data dimensions. One can easily condition on variables at the beginning of the ordering, and marginalize out variables at the end of the ordering, however other inference...

We present Neural Autoregressive Distribution Estimation (NADE) models, which are neural network architectures applied to the problem of unsupervised distribution and density esitmation. They leverage the probability product rule and a weight sharing scheme inspired from restricted Boltzmann machines, to yield an estimator that is both tractable and has good generalization performance. We discu...

Training of the neural autoregressive density estimator (NADE) can be viewed as doing one step of probabilistic inference on missing values in data. We propose a new model that extends this inference scheme to multiple steps, arguing that it is easier to learn to improve a reconstruction in k steps rather than to learn to reconstruct in a single inference step. The proposed model is an unsuperv...

We present an approach based on feed-forward neural networks for learning the distribution of textual documents. This approach is inspired by the Neural Autoregressive Distribution Estimator (NADE) model, which has been shown to be a good estimator of the distribution of discrete-valued high-dimensional vectors. In this paper, we present how NADE can successfully be adapted to the case of textu...

UNLABELLED Mycobacterium tuberculosis remains a major cause of death due to the lack of treatment accessibility, HIV coinfection, and drug resistance. Development of new drugs targeting previously unexplored pathways is essential to shorten treatment time and eliminate persistent M. tuberculosis. A promising biochemical pathway which may be targeted to kill both replicating and nonreplicating M...