Computational Methods for Single-Cell Imaging and Omics Data Integration

Integrating single cell omics and single cell imaging allows for a more effective characterisation of the underlying mechanisms that drive a phenotype at the tissue level, creating a comprehensive profile at the cellular level. Although the use of imaging data is well established in biomedical research, its primary application has been to observe phenotypes at the tissue or organ level, often using medical imaging techniques such as MRI, CT, and PET. These imaging technologies complement omics-based data in biomedical research because they are helpful for identifying associations between genotype and phenotype, along with functional changes occurring at the tissue level. Single cell imaging can act as an intermediary between these levels. Meanwhile new technologies continue to arrive that can be used to interrogate the genome of single cells and its related omics datasets. As these two areas, single cell imaging and single cell omics, each advance independently with the development of novel techniques, the opportunity to integrate these data types becomes more and more attractive. This review outlines some of the technologies and methods currently available for generating, processing, and analysing single-cell omics- and imaging data, and how they could be integrated to further our understanding of complex biological phenomena like ageing. We include an emphasis on machine learning algorithms because of their ability to identify complex patterns in large multidimensional data.

Precise Marketing Strategy Optimization of E-Commerce Platform Based on KNN Clustering

With the development of computer technology and the arrival of the era of artificial intelligence, the analysis of user demand bias is of great significance to the operation optimization of e-commerce platforms. Combined with CS domain signaling data, IP packet data of PS domain, and customer CRM data provided by operators, this research studies each dimension index of operator user portrait, after that the operator user portrait platform is divided into some individual subunits, and then the corresponding data mining technology is carried out to study the implementation scheme of each subunit. The system can process and mine multidimensional data of operators’ users and form user portraits on the basis of user data aggregation. Finally, based on the operator user portrait platform studied in this paper, the operator user data are analyzed from both the user’s mobile phone use behavior and user consumption behavior. Furthermore, the application value of this research in the precision marketing and personalized service of operators is illustrated.

Research on Artificial Intelligence Classification and Statistical Methods of Financial Data in Smart Cities

In order to improve the effect of financial data classification and extract effective information from financial data, this paper improves the data mining algorithm, uses linear combination of principal components to represent missing variables, and performs dimensionality reduction processing on multidimensional data. In order to achieve the standardization of sample data, this paper standardizes the data and combines statistical methods to build an intelligent financial data processing model. In addition, starting from the actual situation, this paper proposes the artificial intelligence classification and statistical methods of financial data in smart cities and designs data simulation experiments to conduct experimental analysis on the methods proposed in this paper. From the experimental results, the artificial intelligence classification and statistical method of financial data in smart cities proposed in this paper can play an important role in the statistical analysis of financial data.

Optimized Query Algorithms for Top- K Group Skyline

Skyline query is a typical multiobjective query and optimization problem, which aims to find out the information that all users may be interested in a multidimensional data set. Multiobjective optimization has been applied in many scientific fields, including engineering, economy, and logistics. It is necessary to make the optimal decision when two or more conflicting objectives are weighed. For example, maximize the service area without changing the number of express points, and in the existing business district distribution, find out the area or target point set whose target attribute is most in line with the user’s interest. Group Skyline is a further extension of the traditional definition of Skyline. It considers not only a single point but a group of points composed of multiple points. These point groups should not be dominated by other point groups. For example, in the previous example of business district selection, a single target point in line with the user’s interest is not the focus of the research, but the overall optimality of all points in the whole target area is the final result that the user wants. This paper focuses on how to efficiently solve top- k group Skyline query problem. Firstly, based on the characteristics that the low levels of Skyline dominate the high level points, a group Skyline ranking strategy and the corresponding SLGS algorithm on Skyline layer are proposed according to the number of Skyline layer and vertices in the layer. Secondly, a group Skyline ranking strategy based on vertex coverage is proposed, and corresponding VCGS algorithm and optimized algorithm VCGS+ are proposed. Finally, experiments verify the effectiveness of this method from two aspects: query response time and the quality of returned results.

The impact of COVID-19 threats, and preventive chain of action: molecule to medicine

The emergence of the novel coronavirus (SARS-CoV-2) had affected us significantly from the individual level, to nationwide and global with a big loss of finances, and the freezing of various factories, schools, and transportation in communities The pandemic started with anxiety and a loss of health guidance and policies due to the unknown causes of viral transmission to human features as well as a high infection rate with low mortality It remains the original source of Covid-19 where it comes from and what is the reality of real viral entities and its origin such as natural born and recombinant viral variants in the case of COVID-19 pandemic. This sentence is unclear. In this short perspective article, we address some issues of risk assessment and management issues using molecular-based decision tools which may benefit or provide future drills to counteract health and clinic safety against a viral pandemic. Every pandemic gives us life threatening lessons on previous and disconnected human networks due to uncertainty of viral infection, which we learned from this COVID-19 pandemic case as well. It gives us some insight on how to rebuild our community regarding the strength of public health and the integration of science tools into the early phase of medical application, such as the role of molecular diagnostics through educational engagement. To promote the value of awareness with solid knowledge-based communication and to develop resilient preventive solutions for supply chains or prevention, the systematic practice of connectivity through visual format using multidimensional data outcomes could help reconsider the leverage of molecules as a bridge for the improvement and application of updated scientific tools of prediction precisely to identify unknown pathogens encompass rigor community-based activity likelihood sensitivity and resistance to pathogen infiltrated society in the future.

Noise Resistant Multidimensional Data Fusion via Quasi-Cliques on Hypergraphs

Cross-matching data stored on separate files is an everyday activity in the scientific domain. However sometimes the relation between attributes may not be obvious. The discovery of foreign keys on relational databases is a similar problem. Thus techniques devised for this problem can be adapted. Nonetheless, given the different nature of the data, which can be subject to uncertainty, this adaptation is not trivial.<br>This paper firstly introduces the concept of Equally-Distributed Dependencies, which is similar to the Inclusion Dependencies from the relational domain. We describe a correspondence in order to bridge existing ideas. We then propose PresQ: a new algorithm based on the search of maximal quasi-cliques on hyper-graphs to make it more robust to the nature of uncertain numerical data. This algorithm has been tested on three public datasets, showing promising results both in its capacity to find multidimensional equally-distributed sets of attributes and in run-time.

Noise Resistant Multidimensional Data Fusion via Quasi-Cliques on Hypergraphs

Cross-matching data stored on separate files is an everyday activity in the scientific domain. However sometimes the relation between attributes may not be obvious. The discovery of foreign keys on relational databases is a similar problem. Thus techniques devised for this problem can be adapted. Nonetheless, given the different nature of the data, which can be subject to uncertainty, this adaptation is not trivial.<br>This paper firstly introduces the concept of Equally-Distributed Dependencies, which is similar to the Inclusion Dependencies from the relational domain. We describe a correspondence in order to bridge existing ideas. We then propose PresQ: a new algorithm based on the search of maximal quasi-cliques on hyper-graphs to make it more robust to the nature of uncertain numerical data. This algorithm has been tested on three public datasets, showing promising results both in its capacity to find multidimensional equally-distributed sets of attributes and in run-time.

Comprehensible Visualization of Multidimensional Data: Sum of Ranking Differences-Based Parallel Coordinates

A novel visualization technique is proposed for the sum of ranking differences method (SRD) based on parallel coordinates. An axis is defined for each variable, on which the data are depicted row-wise. By connecting data, the lines may intersect. The fewer intersections between the variables, the more similar they are and the clearer the figure becomes. Therefore, the visualization depends on what techniques are used to order the variables. The key idea is to employ the SRD method to measure the degree of similarity of the variables, establishing a distance-based order. The distances between the axes are not uniformly distributed in the proposed visualization; their closeness reflects similarity, according to their SRD value. The proposed algorithm identifies false similarities through an iterative approach, where the angles between the SRD values determine which side a variable is plotted. Visualization of the algorithm is provided by MATLAB/Octave source codes. The proposed tool is applied to study how the sources of greenhouse gas emissions can be grouped based on the statistical data of the countries. A comparison to multidimensional scaling (MDS)-based ordering is also given. The use case demonstrates the applicability of the method and the synergies of the incorporation of the SRD method into parallel coordinates.

Efficiency Analysis of OLAP-data Hypercube Decomposition for Exponential Computational Complexity Methods

The paper studies problems of reduction (decomposition) of OLAP-hypercube multidimensional data models. When decomposing large hyper-cubes of multidimensional data into sub-cube components the goal is to increase the computational performance of analytical OLAP systems, which is related to decreasing computational complexity of reduction methods for solving OLAP-data analysis problems with respect to the computational complexity of non-reduction methods, applied to data directly all over the hypercube. The paper formalizes the concepts of reduction and non-reduction methods and gives a definition of the upper bound for the change in the computational complexity of reduction methods in the decomposition of the problem of analyzing multidimensional OLAP-data in comparison with non-reduction methods in the class of exponential degree of computational complexity.The exact values of the upper bound for changing computational complexity are obtained for the hypercube decomposition into two sub-cubes on sets consisting of an even and an odd number of sub-cube structures, and its main properties are given, which are used to determine the decomposition efficiency. A formula for the efficiency of decomposition into two sub-cube structures for reduction of OLAP data analysis problems is obtained, and it is shown that with an increase in the dimension “n” of the lattice specifying the number of sub-cubes in the hypercube data structure, the efficiency of such a decomposition obeys an exponential law with an exponent “n/2”, regardless of the parity “n”. The examples show the possibility to use the values (found) of the upper bound for the change in computational complexity to establish the effectiveness criteria for reduction methods and the expediency of decomposition in specific cases.The paper results can be used in processing and analysis of information arrays of hypercube structures of analytical OLAP systems belonging to the Big-Data or super-large computer systems of multidimensional data.