Potential trends in the intensity and irregularity of the Earth’s natural pulse electromagnetic field (ENPEMF) signal may be related to the occurrence of seismic hazards. To investigate this potential variation trend, radial basis function (RBF) forecast models have been applied in the field of ENPEMF signal intensity forecast, but its forecast accuracy is not high enough to be used in the field of seismic hazard monitoring and other fields that require high accuracy. In addition, although the rapid development of deep learning has improved the accuracy of time series forecasts, ENPEMF signals are susceptible to noise which may lead to erroneous identification of pregnant earthquake information. Based on this, this paper proposes a chaotic LSTM forecasting model based on fractional Fourier transform (FrFT) optimized VMD (FrVMD) to forecast the underlying ENPEMF signal intensity trend. The strategy of the proposed model is the FrVMD algorithm to decompose and reconstruct the original signal for noise reduction, whereupon forecasting the phase space reconstructed ENPEMF data by chaotic parameters optimizing the LSTM network. The model of this paper is applied to the ENPEMF signal before and after the Ms7.0 magnitude earthquake in Lushan on April 20, 2013. The numerical results show that the proposed model forecasts the intensity trend of ENPEMF data more accurately, for the forecast effect and accuracy are better than the traditional radial basis function (RBF) network, chaotic RBF network, and traditional LSTM network model, which helps to identify the pregnant earthquake information and provides support for electromagnetic monitoring analysis before geological disasters and strong earthquakes.

The Earth’s natural pulsed electromagnetic field (ENPEMF) signal is released by the instantaneous disturbance of the Earth’s naturally varying magnetic field, contains a large amount of information about the changing geological structures and their kinetic principles. The ENPEMF signals acquired by the equipment are often accompanied by a great deal of noise, and effective noise reduction is required for accurate signal analysis. We propose a new noise reduction method for a very low signal-to-noise ratio (SNR), called the Blaschke unwinding adaptive Fourier decomposition, and use synthetic signals to demonstrate the high accuracy of the proposed noise reduction method. Meanwhile, the Root-phase deep feature representation (RPDP) of the ENPEMF signal acquired during the Lushan Ms7.0 earthquake is explored using the roots obtained by Blaschke unwinding. The results show that the time-based RPDP can accurately reveal the deep feature of the ENPEMF signal before and after the earthquake, and extract the anomalous changes, potentially revealing the electromagnetic anomaly information contained in the ENPEMF signal.

Alternative splicing enables a gene translating into different isoforms and into the corresponding proteoforms, which actually accomplish various biological functions of a living body. Isoform-isoform interactions (IIIs) provide a higher resolution interactome to explore the cellular processes and disease mechanisms than the canonically studied protein-protein interactions (PPIs), which are often recorded at the coarse gene level. The knowledge of IIIs is critical to map pathways, understand protein complexity and functional diversity, but the known IIIs are very scanty. In this paper, we propose a deep learning based method called DeepIII to systematically predict genome-wide IIIs by integrating diverse data sources, including RNA-seq datasets of different human tissues, exon array data, domain-domain interactions (DDIs) of proteins, nucleotide sequences and amino acid sequences. Particularly, DeepIII fuses these data to learn the representation of isoform pairs with a four-layer deep neural networks, and then performs binary classification on the learnt representation to achieve the prediction of IIIs. Experimental results show that DeepIII achieves a superior prediction performance to the state-of-the-art solutions and the III network constructed by DeepIII gives more accurate isoform function prediction. Case studies further confirm that DeepIII can differentiate the individual interaction partners of different isoforms spliced from the same gene. The code and datasets of DeepIII are available at http://mlda.swu.edu.cn/codes.php?name=DeepIII.

Discovering driver pathways is an essential task to understand the pathogenesis of cancer and to design precise treatments for cancer patients. Increasing evidences have been indicating that multiple pathways often function cooperatively in carcinogenesis. In this study, we propose an approach called CDPath to discover cooperative driver pathways. CDPath first uses Integer Linear Programming to explore driver core modules from mutation profiles by enforcing co-occurrence and functional interaction relations between modules, and by maximizing the mutual exclusivity and coverage within modules. Next, to enforce cooperation of pathways and help the follow-up exact cooperative driver pathways discovery, it performs Markov clustering on pathway-pathway interaction network to cluster pathways. After that, it identifies pathways in different modules but in the same clusters as cooperative driver pathways. We apply CDPath on two TCGA datasets: breast cancer (BRCA) and endometrial cancer (UCEC). The results show that CDPath can identify known (i.e., TP53) and potential driver genes (i.e., SPTBN2). In addition, the identified cooperative driver pathways are related with the target cancer, and they are involved with carcinogenesis and several key biological processes. CDPath can uncover more potential biological associations between pathways (over 100 percent) and more cooperative driver pathways (over 200 percent) than competitive approaches. The demo codes of CDPath are available at http://mlda.swu.edu.cn/codes.php?name=CDPath.

A widely used approach in transcriptome analysis is the alignment of short reads to a reference genome. However, owing to the deficiencies of specially designed analytical systems, short reads unmapped to the genome sequence are usually ignored, resulting in the loss of significant biological information and insights. To fill this gap, we present Comprehensive Assembly and Functional annotation of Unmapped RNA-Seq data (CAFU), a Galaxy-based framework that can facilitate the large-scale analysis of unmapped RNA sequencing (RNA-Seq) reads from single- and mixed-species samples. By taking advantage of machine learning techniques, CAFU addresses the issue of accurately identifying the species origin of transcripts assembled using unmapped reads from mixed-species samples. CAFU also represents an innovation in that it provides a comprehensive collection of functions required for transcript confidence evaluation, coding potential calculation, sequence and expression characterization and function annotation. These functions and their dependencies have been integrated into a Galaxy framework that provides access to CAFU via a user-friendly interface, dramatically simplifying complex exploration tasks involving unmapped RNA-Seq reads. CAFU has been validated with RNA-Seq data sets from wheat and Zea mays (maize) samples. CAFU is freely available via GitHub: https://github.com/cma2015/CAFU.

The carcinogenesis is typically involved with the cooperations of multiple pathways. Therefore, discovering cooperative driver pathways can provide more precise therapy to patients. Existing cooperative driver pathway identification methods can only identify few (or previously well-known) driver pathways, because of noisy or single type knowledge, or of the insufficient attention to pathway cooperations. To address these problems, we develop a novel approach called CoPath that leverages genomic alteration profiles and bi-clustering to discover cooperative driver pathways. Based on the mutation profiles reconstructed from somatic mutations and copy number variations, CoPath firstly uses a greedy search on the gene signaling network to identify mutually exclusive modules of genes, which have common downstream events in the network. Next, it incorporates the identified mutually exclusive modules and gene interaction network as prior knowledge, and introduces a dual regularized bi-clustering method on gene expression data to cluster cooperative mutually exclusive modules. Finally, it identifies the modules in the same cluster as cooperative driver pathways. Extensive experiments on real cancer genomics dataset (Breast from TCGA) shows that CoPath can not only detect individual driver pathways but also uncover more relations between potential driver genes and pathways than related competitive approaches.

BACKGROUND: Cell direct reprogramming technology has been rapidly developed with its low risk of tumor risk and avoidance of ethical issues caused by stem cells, but it is still limited to specific cell types. Direct reprogramming from an original cell to target cell type needs the cell similarity and cell specific regulatory network. The position and function of cells in vivo, can provide some hints about the cell similarity. However, it still needs further clarification based on molecular level studies. RESULT: CellSim is therefore developed to offer a solution for cell similarity calculation and a tool of bioinformatics for researchers. CellSim is a novel tool for the similarity calculation of different cells based on cell ontology and molecular networks in over 2000 different human cell types and presents sharing regulation networks of part cells. CellSim can also calculate cell types by entering a list of genes, including more than 250 human normal tissue specific cell types and 130 cancer cell types. The results are shown in both tables and spider charts which can be preserved easily and freely. CONCLUSION: CellSim aims to provide a computational strategy for cell similarity and the identification of distinct cell types. Stable CellSim releases (Windows, Linux, and Mac OS/X) are available at: www.cellsim.nwsuaflmz.com , and source code is available at: https://github.com/lileijie1992/CellSim/ .

MOTIVATION: A number of methods have been reported that predict protein-protein interactions (PPIs) with high accuracy using only simple sequence-based features such as amino acid 3mer content. This is surprising, given that many protein interactions have high specificity that depends on detailed atomic recognition between physiochemically complementary surfaces. Are the reported high accuracies realistic? RESULTS: We find that the reported accuracies of the predictions are significantly over-estimated, and strongly dependent on the structure of the training and testing datasets used. The choice of which protein pairs are deemed as non-interactions in the training data has a variable impact on the accuracy estimates, and the accuracies can be artificially inflated by a bias towards dominant samples in the positive data which result from the presence of hub proteins in the protein interaction network. To address this bias, we propose a positive set-specific method to create a 'balanced' negative set maintaining the degree distribution for each protein, leading to the conclusion that simple sequence-based features contain insufficient information to be useful for predicting PPIs, but that protein domain-based features have some predictive value. AVAILABILITY: Our method, named 'BRS-nonint', is available at http://www.bioinformatics.leeds.ac.uk/BRS-nonint/. All the datasets used in this study are derived from publicly available data, and are available at http://www.bioinformatics.leeds.ac.uk/BRS-nonint/PPI_RandomBalance.html CONTACT: [email protected]; [email protected].