Automatic Classification of Bright Retinal Lesions via Deep Network Features

The diabetic retinopathy is timely diagonalized through color eye fundus images by experienced ophthalmologists, in order to recognize potential retinal features and identify early-blindness cases. In this paper, it is proposed to extract deep features from the last fully-connected layer of, four different, pre-trained convolutional neural networks. These features are then feeded into a non-linear classifier to discriminate three-class diabetic cases, i.e., normal, exudates, and drusen. Averaged across 1113 color retinal images collected from six publicly available annotated datasets, the deep features approach perform better than the classical bag-of-words approach. The proposed approaches have an average accuracy between 91.23% and 92.00% with more than 13% improvement over the traditional state of art methods.Comment: Preprint submitted to Journal of Medical Imaging | SPIE (Tue, Jul 28, 2017

Generating Politically-Relevant Event Data

Automatically generated political event data is an important part of the social science data ecosystem. The approaches for generating this data, though, have remained largely the same for two decades. During this time, the field of computational linguistics has progressed tremendously. This paper presents an overview of political event data, including methods and ontologies, and a set of experiments to determine the applicability of deep neural networks to the extraction of political events from news text

TITER: predicting translation initiation sites by deep learning.

MotivationTranslation initiation is a key step in the regulation of gene expression. In addition to the annotated translation initiation sites (TISs), the translation process may also start at multiple alternative TISs (including both AUG and non-AUG codons), which makes it challenging to predict TISs and study the underlying regulatory mechanisms. Meanwhile, the advent of several high-throughput sequencing techniques for profiling initiating ribosomes at single-nucleotide resolution, e.g. GTI-seq and QTI-seq, provides abundant data for systematically studying the general principles of translation initiation and the development of computational method for TIS identification.MethodsWe have developed a deep learning-based framework, named TITER, for accurately predicting TISs on a genome-wide scale based on QTI-seq data. TITER extracts the sequence features of translation initiation from the surrounding sequence contexts of TISs using a hybrid neural network and further integrates the prior preference of TIS codon composition into a unified prediction framework.ResultsExtensive tests demonstrated that TITER can greatly outperform the state-of-the-art prediction methods in identifying TISs. In addition, TITER was able to identify important sequence signatures for individual types of TIS codons, including a Kozak-sequence-like motif for AUG start codon. Furthermore, the TITER prediction score can be related to the strength of translation initiation in various biological scenarios, including the repressive effect of the upstream open reading frames on gene expression and the mutational effects influencing translation initiation efficiency.Availability and implementationTITER is available as an open-source software and can be downloaded from https://github.com/zhangsaithu/titer [email protected] or [email protected] informationSupplementary data are available at Bioinformatics online