Ground Moving Target Imaging for Highly Squint SAR by Modified Minimum Entropy Algorithm and Spectrum Rotation

Ground moving target (GMT) is displaced and defocused in conventional synthetic aperture radar (SAR) image due to the residual phase error of non-cooperative GMT motion. In this study, a GMT imaging (GMTIm) method is proposed for highly squint SAR. As the squint angle become large, the displace and defocus effect of the GMT image become severe and the geometry distortion of the GMT image cannot be ignored. The proposed method first deduced the two-dimensional (2-D) frequency domain signal of the GMT and the bulk compression function of the Range Migration Algorithm (RMA) in highly squint SAR. Then GMT ROI data are extracted and a modified minimum entropy algorithm (MMEA) is proposed to refocus the GMT image. MMEA introduces the idea of bisection into the iteration process to converge more efficiently than the previous minimum entropy method. To overcome the geometry distortion of the GMT image, an equivalent squint angle spectrum rotation method is proposed. Finally, to suppress the GMT image sidelobe, the sparse characteristic of GMT is considered and a sparse enhancement method is adopted. The proposed method can realize GMTIm in highly squint SAR where the squint angle reaches to 75 degrees. The PSNR and ISLR of point target in highly squint SAR is close to that in side-looking SAR. The simulated point target data and ship data are used to validate the effectiveness of the proposed method

Additional file 2: Table S2. of A genetic algorithm-based weighted ensemble method for predicting transposon-derived piRNAs

The performances of RF models and SVM models on the imbalanced Human dataset. (XLSX 13 kb

Accurate Prediction of Transposon-Derived piRNAs by Integrating Various Sequential and Physicochemical Features.

BACKGROUND:Piwi-interacting RNA (piRNA) is the largest class of small non-coding RNA molecules. The transposon-derived piRNA prediction can enrich the research contents of small ncRNAs as well as help to further understand generation mechanism of gamete. METHODS:In this paper, we attempt to differentiate transposon-derived piRNAs from non-piRNAs based on their sequential and physicochemical features by using machine learning methods. We explore six sequence-derived features, i.e. spectrum profile, mismatch profile, subsequence profile, position-specific scoring matrix, pseudo dinucleotide composition and local structure-sequence triplet elements, and systematically evaluate their performances for transposon-derived piRNA prediction. Finally, we consider two approaches: direct combination and ensemble learning to integrate useful features and achieve high-accuracy prediction models. RESULTS:We construct three datasets, covering three species: Human, Mouse and Drosophila, and evaluate the performances of prediction models by 10-fold cross validation. In the computational experiments, direct combination models achieve AUC of 0.917, 0.922 and 0.992 on Human, Mouse and Drosophila, respectively; ensemble learning models achieve AUC of 0.922, 0.926 and 0.994 on the three datasets. CONCLUSIONS:Compared with other state-of-the-art methods, our methods can lead to better performances. In conclusion, the proposed methods are promising for the transposon-derived piRNA prediction. The source codes and datasets are available in S1 File

Additional file 1: Table S1. of A genetic algorithm-based weighted ensemble method for predicting transposon-derived piRNAs

The performances of RF models and SVM models on the balanced Human dataset. (XLSX 13 kb

Additional file 1 of Predicting drug side effects by multi-label learning and ensemble learning

Datasets and source codes. (ZIP 13491 kb

MiR-223-3p functions as a tumor suppressor in lung squamous cell carcinoma by miR-223-3p-mutant p53 regulatory feedback loop

Abstract Background MicroRNAs have an important role in diverse biological processes including tumorigenesis. MiR-223 has been reported to be deregulated in several human cancer types. However, its biological role has not been functionally characterized in lung squamous cell carcinoma (LSCC). The following study investigates the role of miR-223-3p in LSCC growth and metastasis and its underlying mechanism. Methods MicroRNA profiling analyses were conducted to determine differential miRNAs expression levels in LSCC tumor tissues that successfully formed xenografts in immunocompromised mice (XG) and failed tumor tissues (no-XG). RT-PCR and in situ hybridization (ISH) was performed to evaluate the expression of miR-223-3p in 12 paired adjacent normal tissues and LSCC specimens. Cell proliferation and migration were assessed by CCK-8, colony formation and Transwell assay, respectively. The role of miR-223-3p in LSCC tumorigenesis was examined using xenograft nude models. Bioinformatics analysis, Dual-luciferase reporter assays, Chromatin immunoprecipitation (ChIP) assay and Western blot analysis were used to identify the direct target of miR-223-3p and its interactions. Results MiR-223-3p was downregulated in LSCC tissues that successfully formed xenografts (XG) compared with tumor tissues that failed (no-XG), which was also significantly reduced in LSCC tissues compared with the adjacent normal tissues. Gain- and loss-of function experiments showed that miR-223-3p inhibited proliferation and migration in vitro. More importantly, miR-223-3p overexpression greatly suppressed tumor growth in vivo. Mechanistically, we found that mutant p53 bound to the promoter region of miR-223 and reduced its transcription. Meanwhile, p53 is a direct target of miR-223-3p. Thus, miR-223-3p regulated mutant p53 expression in a feedback loop that inhibited cell proliferation and migration. Conclusions Our study identified miR-223-3p, as a tumor suppressor gene, markedly inhibited cell proliferation and migration via miR-223-3p-mutant p53 feedback loop, which suggested miR-223-3p might be a new therapeutic target in LSCC bearing p53 mutations

Accurate Prediction of Immunogenic T-Cell Epitopes from Epitope Sequences Using the Genetic Algorithm-Based Ensemble Learning

<div><p>Background</p><p>T-cell epitopes play the important role in T-cell immune response, and they are critical components in the epitope-based vaccine design. Immunogenicity is the ability to trigger an immune response. The accurate prediction of immunogenic T-cell epitopes is significant for designing useful vaccines and understanding the immune system.</p><p>Methods</p><p>In this paper, we attempt to differentiate immunogenic epitopes from non-immunogenic epitopes based on their primary structures. First of all, we explore a variety of sequence-derived features, and analyze their relationship with epitope immunogenicity. To effectively utilize various features, a genetic algorithm (GA)-based ensemble method is proposed to determine the optimal feature subset and develop the high-accuracy ensemble model. In the GA optimization, a chromosome is to represent a feature subset in the search space. For each feature subset, the selected features are utilized to construct the base predictors, and an ensemble model is developed by taking the average of outputs from base predictors. The objective of GA is to search for the optimal feature subset, which leads to the ensemble model with the best cross validation AUC (area under ROC curve) on the training set.</p><p>Results</p><p>Two datasets named ‘IMMA2’ and ‘PAAQD’ are adopted as the benchmark datasets. Compared with the state-of-the-art methods POPI, POPISK, PAAQD and our previous method, the GA-based ensemble method produces much better performances, achieving the AUC score of 0.846 on IMMA2 dataset and the AUC score of 0.829 on PAAQD dataset. The statistical analysis demonstrates the performance improvements of GA-based ensemble method are statistically significant.</p><p>Conclusions</p><p>The proposed method is a promising tool for predicting the immunogenic epitopes. The source codes and datasets are available in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0128194#pone.0128194.s001" target="_blank">S1 File</a>.</p></div

Datasets for piRNA prediction.

<p>Datasets for piRNA prediction.</p

Six sequence-derived features.

<p>Six sequence-derived features.</p