2019nCoVAS: Developing the Web Service for Epidemic Transmission Prediction, Genome Analysis, and Psychological Stress Assessment for 2019-nCoV

A Review of the Application of Spatial Transcriptomics in Neuroscience

Since spatial transcriptomics can locate and distinguish the gene expression of functional genes in special regions and tissue, it is important for us to investigate the brain development, the development mechanism of brain diseases, and the relationship between brain structure and function in Neuroscience (or Brain science). While previous studies have introduced the crucial spatial transcriptomic techniques and data analysis methods, there are few studies to comprehensively overview the key methods, data resources, and technological applications of spatial transcriptomics in Neuroscience. For these reasons, we first investigate several common spatial transcriptomic data analysis approaches and data resources. Second, we introduce the applications of the spatial transcriptomic data analysis approaches in Neuroscience. Third, we summarize the integrating spatial transcriptomics with other technologies in Neuroscience. Finally, we discuss the challenges and future research directions of spatial transcriptomics in Neuroscience.

Identification and segregation of genes with improved recurrent neural network trained with optimal gene level and mutation level features

Even though many different approaches have been employed to address the complex mutational heterogeneity of cancer, finding driver genes is still problematic since other genomic factors cannot be fully integrated for combined analyses. This research paper presents a novel gene identification and segregation model with five key processes (a) pre-processing, (b) treatment of class imbalances, (c) feature extraction, (d) feature selection, and (e) gene classification. To increase the data quality, the gathered initial information is first pre-processed utilizing data cleaning and data normalization. This turns the raw data into something that is both useful and effective. In actuality, the sample is skewed against drivers because passenger mutation markers appear in proportionally less instances than drivers do. To address the Class Imbalance Problem, improved K-Means + SMOTE are applied to the preprocessed data. The most crucial characteristics, including those at the gene and mutation levels, are then extracted from the balanced dataset. To lessen the computational load in terms of time, the best features from the retrieved features are selected using Forensic interpretation tailored hunger food search optimization (FIHFSO). The ideal features are used to train the deep learning classifier that conducts the separation procedure. In this research, an Improved Recurrent Neural Network (I-RNN) is used to make a final decision about genes. At 90% of learning percentage, the accuracy of the proposed method achieves 0.98% of 0.83, 0.81, 0.65, 0.80, 0.92 and 0.63% which is compared to the other methods like HGS, FBIO, AOA, AO, GOA and PRO respectively.

Spatiotemporal Transformer Neural Network for Time-Series Forecasting

Predicting high-dimensional short-term time-series is a difficult task due to the lack of sufficient information and the curse of dimensionality. To overcome these problems, this study proposes a novel spatiotemporal transformer neural network (STNN) for efficient prediction of short-term time-series with three major features. Firstly, the STNN can accurately and robustly predict a high-dimensional short-term time-series in a multi-step-ahead manner by exploiting high-dimensional/spatial information based on the spatiotemporal information (STI) transformation equation. Secondly, the continuous attention mechanism makes the prediction results more accurate than those of previous studies. Thirdly, we developed continuous spatial self-attention, temporal self-attention, and transformation attention mechanisms to create a bridge between effective spatial information and future temporal evolution information. Fourthly, we show that the STNN model can reconstruct the phase space of the dynamical system, which is explored in the time-series prediction. The experimental results demonstrate that the STNN significantly outperforms the existing methods on various benchmarks and real-world systems in the multi-step-ahead prediction of a short-term time-series.

An integrated platform for Brucella with knowledge graph technology: From genomic analysis to epidemiological projection

Motivation:Brucella, the causative agent of brucellosis, is a global zoonotic pathogen that threatens both veterinary and human health. The main sources of brucellosis are farm animals. Importantly, the bacteria can be used for biological warfare purposes, requiring source tracking and routine surveillance in an integrated manner. Additionally, brucellosis is classified among group B infectious diseases in China and has been reported in 31 Chinese provinces to varying degrees in urban areas. From a national biosecurity perspective, research on brucellosis surveillance has garnered considerable attention and requires an integrated platform to provide researchers with easy access to genomic analysis and provide policymakers with an improved understanding of both reported patients and detected cases for the purpose of precision public health interventions.

Results: For the first time in China, we have developed a comprehensive information platform for Brucella based on dynamic visualization of the incidence (reported patients) and prevalence (detected cases) of brucellosis in mainland China. Especially, our study establishes a knowledge graph for the literature sources of Brucella data so that it can be expanded, queried, and analyzed. When similar “epidemiological comprehensive platforms” are established in the distant future, we can use knowledge graph to share its information. Additionally, we propose a software package for genomic sequence analysis. This platform provides a specialized, dynamic, and visual point-and-click interface for studying brucellosis in mainland China and improving the exploration of Brucella in the fields of bioinformatics and disease prevention for both human and veterinary medicine.

Artificial intelligence in cancer target identification and drug discovery

Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.

Developing an Embedding, Koopman and Autoencoder Technologies-Based Multi-Omics Time Series Predictive Model (EKATP) for Systems Biology research

It is very important for systems biologists to predict the state of the multi-omics time series for disease occurrence and health detection. However, it is difficult to make the prediction due to the high-dimensional, nonlinear and noisy characteristics of the multi-omics time series data. For this reason, this study innovatively proposes an Embedding, Koopman and Autoencoder technologies-based multi-omics time series predictive model (EKATP) to predict the future state of a high-dimensional nonlinear multi-omics time series. We evaluate this EKATP by using a genomics time series with chaotic behavior, a proteomics time series with oscillating behavior and a metabolomics time series with flow behavior. The computational experiments demonstrate that our proposed EKATP can substantially improve the accuracy, robustness and generalizability to predict the future state of a time series for multi-omics data.

MCDB: A comprehensive curated mitotic catastrophe database for retrieval, protein sequence alignment, and target prediction

Mitotic catastrophe (MC) is a form of programmed cell death induced by mitotic process disorders, which is very important in tumor prevention, development, and drug resistance. Because rapidly increased data for MC is vigorously promoting the tumor-related biomedical and clinical study, it is urgent for us to develop a professional and comprehensive database to curate MC-related data. Mitotic Catastrophe Database (MCDB) consists of 1214 genes/proteins and 5014 compounds collected and organized from more than 8000 research articles. Also, MCDB defines the confidence level, classification criteria, and uniform naming rules for MC-related data, which greatly improves data reliability and retrieval convenience. Moreover, MCDB develops protein sequence alignment and target prediction functions. The former can be used to predict new potential MC-related genes and proteins, and the latter can facilitate the identification of potential target proteins of unknown MC-related compounds. In short, MCDB is such a proprietary, standard, and comprehensive database for MC-relate data that will facilitate the exploration of MC from chemists to biologists in the fields of medicinal chemistry, molecular biology, bioinformatics, oncology and so on. The MCDB is distributed on http://www.combio-lezhang.online/MCDB/index_html/.

Correlation-Based Analysis of COVID-19 Virus Genome Versus Other Fatal Virus Genomes

Virus attacks have had devastating effects on mankind. The prominent viruses such as Ebola virus (2012), SARS-CoV or Severe acute respiratory syndrome, Middle East respiratory syndrome-related coronavirus called as the MERS (EMC/2012), Spanish flu (H1N1 virus-1918) and the most recent COVID-19(SARS-CoV-2) are the ones that have created a difficult situation for the survival of the human race. Currently, throughout the world, a global pandemic situation has put economy, livelihood and human existence in a very pathetic situation. Most of the above-mentioned viruses exhibit some similar characteristics and genetic pattern. Analysing such characteristics and genetic pattern can help the researchers to get a deeper insight into the viruses and helps in finding appropriate medicine or cure. To address these issues, this paper proposes an experimental analysis of the above-mentioned viruses data using correlation methods. The virus data considered for the experimental analysis include the distribution of various amino acids, protein sequences, 3D modelling of viruses, pairwise alignment of proteins that comprise the DNA genome of the viruses. Furthermore, this comparative analysis can be used by the researchers and organizations like WHO(World Health Organization), computational biologists, genetic engineers to frame a layout for studying the DNA sequence distribution, percentage of GC (guanine-cytosine) protein which determines the heat stability of viruses. We have used the Biopython to illustrate the gene study of prominent viruses and have derived results and insights in the form of 3D modelling. The experimental results are more promising with an accuracy rate of 96% in overall virus relationship calculation.