Certain type of documents such as tweets are collected by specifying a set of keywords. As topics of interest change with time it is beneficial to adjust keywords dynamically. The challenge is that these need to be specified ahead of knowing the forthcoming documents and the underlying topics. The future topics should mimic past topics of interest yet there should be some novelty in them. We develop a keyword-based topic model that dynamically selects a subset of keywords to be used to collect future documents. The generative process first selects keywords and then the underlying documents based on the specified keywords. The model is trained by using a variational lower bound and stochastic gradient optimization. The inference consists of finding a subset of keywords where given a subset the model predicts the underlying topic-word matrix for the unknown forthcoming documents. We compare the keyword topic model against a benchmark model using viral predictions of tweets combined with a topic model. The keyword-based topic model outperforms this sophisticated baseline model by 67%.

Deep recurrent neural networks perform well on sequence data and are the model of choice. However, it is a daunting task to decide the structure of the networks, i.e. the number of layers, especially considering different computational needs of a sequence. We propose a layer flexible recurrent neural network with adaptive computation time, and expand it to a sequence to sequence model. Different from the adaptive computation time model, our model has a dynamic number of transmission states which vary by step and sequence. We evaluate the model on a financial data set and Wikipedia language modeling. Experimental results show the performance improvement of 7 % to 12 % and indicate the model's ability to dynamically change the number of layers along with the computational steps.

BACKGROUND: Antibiotic resistance genes (ARGs) can spread among pathogens via horizontal gene transfer, resulting in imparities in their distribution even within the same species. Therefore, a pan-genome approach to analyzing resistomes is necessary for thoroughly characterizing patterns of ARGs distribution within particular pathogen populations. Software tools are readily available for either ARGs identification or pan-genome analysis, but few exist to combine the two functions. RESULTS: We developed Pan Resistome Analysis Pipeline (PRAP) for the rapid identification of antibiotic resistance genes from various formats of whole genome sequences based on the CARD or ResFinder databases. Detailed annotations were used to analyze pan-resistome features and characterize distributions of ARGs. The contribution of different alleles to antibiotic resistance was predicted by a random forest classifier. Results of analysis were presented in browsable files along with a variety of visualization options. We demonstrated the performance of PRAP by analyzing the genomes of 26 Salmonella enterica isolates from Shanghai, China. CONCLUSIONS: PRAP was effective for identifying ARGs and visualizing pan-resistome features, therefore facilitating pan-genomic investigation of ARGs. This tool has the ability to further excavate potential relationships between antibiotic resistance genes and their phenotypic traits.

Wearables with embedded electrodes and sensors are capable of continuously performing Electrocardiography (ECG), recording electrical activity of the heart, while estimating heart-rate of the wearer. Recent advances in wearable technology have generated comfortable flexible sensors that conform to the contours of the body and can measure heart-rate in the field by capturing QRS complexes of ECG signals. Due to various activities of daily living (ADL), skin deformation by means of lateral, rotational or skin stretching can cause changes in the current pathways of the sensor creating noisy data. The challenge is to disentangle the noise from the usable data in order to accurately detect QRS complexes of ECG signals. In this paper we design a framework to capture noise from a miniaturized flexible sensor, the Biostamp1(with 4 leads), worn on the chest of 16 participants performing a set of structured ADL in a home setting, and a baseball player (pitcher). We present a machine-learning framework using a consensus fusion classifier comprising a Support Vector Machine and Neural Network learned model to remove noise while preserving neighboring R-peaks. We evaluate the model using Leave One Subject Out (LOSO) and yield an average of 83% F-measure on the 17 participants (including the pitcher). The low false negative rate provides accurate heart-rate detection on a finegrained (every 5 seconds) level, in the presence of intermittent stretching of the skin. Our results increase the reliability of detecting heart-rate in real-world and player settings, increasing the utility of flexible ECG-based sensors in the field.

UNLABELLED: SORTALLER is an online allergen classifier based on allergen family featured peptide (AFFP) dataset and normalized BLAST E-values, which establish the featured vectors for support vector machine (SVM). AFFPs are allergen-specific peptides panned from irredundant allergens and harbor perfect information with noise fragments eliminated because of their similarity to non-allergens. SORTALLER performed significantly better than other existing software and reached a perfect balance with high specificity (98.4%) and sensitivity (98.6%) for discriminating allergenic proteins from several independent datasets of protein sequences of diverse sources, also highlighting with the Matthews correlation coefficient (MCC) as high as 0.970, fast running speed and rapidly predicting a batch of amino acid sequences with a single click. AVAILABILITY AND IMPLEMENTATION: http://sortaller.gzhmc.edu.cn/.

BACKGROUND: A new method for the prediction of protein structural classes is constructed based on Rough Sets algorithm, which is a rule-based data mining method. Amino acid compositions and 8 physicochemical properties data are used as conditional attributes for the construction of decision system. After reducing the decision system, decision rules are generated, which can be used to classify new objects. RESULTS: In this study, self-consistency and jackknife tests on the datasets constructed by G.P. Zhou (Journal of Protein Chemistry, 1998, 17: 729-738) are used to verify the performance of this method, and are compared with some of prior works. The results showed that the rough sets approach is very promising and may play a complementary role to the existing powerful approaches, such as the component-coupled, neural network, SVM, and LogitBoost approaches. CONCLUSION: The results with high success rates indicate that the rough sets approach as proposed in this paper might hold a high potential to become a useful tool in bioinformatics.

MOTIVATION: Simple sequence repeats or microsatellites have been found abundantly in many genomes. However, the significance of distribution preference has not been completely understood. Completion of the Arabidopsis genome sequencing allows us to better understand and characterize microsatellites. RESULTS: Microsatellite distribution was more abundant in 5'-flanking regions of genes compared with that expected in the whole genome, with an over-representation of AG and AAG repeats; there were clear differences from distributions in 3'-flanks and coding fractions, where triplet frequencies evidently corresponded to codon usage. We identified 1140 full-length genes that contained at least one locus of AG or AAG repeats in their upstream sequences, and whose functional characteristics were significantly associated with the repeats. This observation indicates that selective pressure markedly differed in the three transcribed regions, with positive selection of AG and AAG repeats in 5'-flanks close to those genes whose products are preferentially involved in transcription.