MotivationA large number of distal enhancers and proximal promoters form enhancer–promoter interactions to regulate target genes in the human genome. Although recent high-throughput genome-wide mapping approaches have allowed us to more comprehensively recognize potential enhancer–promoter interactions, it is still largely unknown whether sequence-based features alone are sufficient to predict such interactions.ResultsHere, we develop a new computational method (named PEP) to predict enhancer–promoter interactions based on sequence-based features only, when the locations of putative enhancers and promoters in a particular cell type are given. The two modules in PEP (PEP-Motif and PEP-Word) use different but complementary feature extraction strategies to exploit sequence-based information. The results across six different cell types demonstrate that our method is effective in predicting enhancer–promoter interactions as compared to the state-of-the-art methods that use functional genomic signals. Our work demonstrates that sequence-based features alone can reliably predict enhancer–promoter interactions genome-wide, which could potentially facilitate the discovery of important sequence determinants for long-range gene regulation.Availability and ImplementationThe source code of PEP is available at: https://github.com/ma-compbio/PEP.Supplementary information Supplementary data are available at Bioinformatics online.

In the human genome, distal enhancers are involved in regulating target genes through proximal promoters by forming enhancer-promoter interactions. Although recently developed highthroughput experimental approaches have allowed us to recognize potential enhancer-promoter interactions genome-wide, it is still largely unclear to what extent the sequence-level information encoded in our genome help guide such interactions. Here we report a new computational method (named "SPEID") using deep learning models to predict enhancer-promoter interactions based on sequence-based features only, when the locations of putative enhancers and promoters in a particular cell type are given. Our results across six different cell types demonstrate that SPEID is effective in predicting enhancer-promoter interactions as compared to state-of-the-art methods that only use information from a single cell type. As a proof-of-principle, we also applied SPEID to identify somatic non-coding mutations in melanoma samples that may have reduced enhancerpromoter interactions in tumor genomes. This work demonstrates that deep learning models can help reveal that sequence-based features alone are sufficient to reliably predict enhancer-promoter interactions genome-wide.

In this paper, the authors present an optimization method based on modified Particle Swarm Optimization (PSO) algorithm for thinning large multiple concentric circular ring arrays of uniformly excited isotropic antennas that will generate a pencil beam in the vertical plane with minimum relative side lobe level (SLL). Two different cases have been studied, one with fixed uniform inter-element spacing and another with optimum uniform inter-element spacing. In both the cases, the number of switched off elements is made equal to 220 or more. The half-power beam width of the synthesized pattern is attempted to make equal to that of a fully populated array with uniform spacing of 0.5λ. Simulation results of the proposed thinned arrays are compared with a fully populated array to illustrate the effectiveness of our proposed method.

Background: In the human genome, distal enhancers are involved in regulating target genes through proximal promoters by forming enhancer-promoter interactions. Although recently developed high-throughput experimental approaches have allowed us to recognize potential enhancer-promoter interactions genome-wide, it is still largely unclear to what extent the sequence-level information encoded in our genome help guide such interactions. Methods: Here we report a new computational method (named "SPEID") using deep learning models to predict enhancer-promoter interactions based on sequence-based features only, when the locations of putative enhancers and promoters in a particular cell type are given. Results: Our results across six different cell types demonstrate that SPEID is effective in predicting enhancerpromoter interactions as compared to state-of-the-art methods that only use information from a single cell type. As a proof-of-principle, we also applied SPEID to identify somatic non-coding mutations in melanoma samples that may have reduced enhancer-promoter interactions in tumor genomes. Conclusions: This work demonstrates that deep learning models can help reveal that sequence-based features alone are sufficient to reliably predict enhancer-promoter interactions genome-wide.Author summary: Distal enhancers in the human genome regulate target genes by interacting with promoters, forming enhancer-promoter interactions (EPIs). Experimental approaches have allowed us to recognize potential EPIs genome-wide, but it is unclear how the sequence information encoded in our genome helps guide such interactions. Here we report a novel machine learning tool (named SPEID) using deep neural networks that predicts EPIs directly from the DNA sequences, given locations of putative enhancers and promoters. We also apply SPEID to identify mutations that may have reduced EPIs in melanoma genomes. This work demonstrates that sequence-based features are sufficient to predict EPIs genome-wide.Quantitative Biology 2019, 7(2): 122-137 https://doi.

Eye-tracking provides an opportunity to generate and analyze high-density data relevant to understanding cognition. However, while events in the real world are often dynamic, eye-tracking paradigms are typically limited to assessing gaze toward static objects. In this study, we propose a generative framework, based on a hidden Markov model (HMM), for using eye-tracking data to analyze behavior in the context of multiple moving objects of interest. We apply this framework to analyze data from a recent visual object tracking task paradigm, TrackIt, for studying selective sustained attention in children. Within this paradigm, we present two validation experiments to show that the HMM provides a viable approach to studying eye-tracking data with moving stimuli, and to illustrate the benefits of the HMM approach over some more naive possible approaches. The first experiment utilizes a novel 'supervised' variant of TrackIt, while the second compares directly with judgments made by human coders using data from the original TrackIt task. Our results suggest that the HMM-based method provides a robust analysis of eye-tracking data with moving stimuli, both for adults and for children as young as 3.5-6 years old.