Leveraging Quantum Computing for the Ising Model to Simulate Two Real Systems: Magnetic Materials and Biological Neural Networks (BNNs)

Quantum computing is a field with increasing relevance as quantum hardware improves and more applications of quantum computing are discovered. In this paper, we demonstrate the feasibility of modeling Ising Model Hamiltonians on the IBM quantum computer. We developed quantum circuits to simulate these systems more efficiently for both closed and open boundary Ising models, with and without perturbations. We tested these various geometries of systems in both 1-D and 2-D space to mimic two real systems: magnetic materials and biological neural networks (BNNs). Our quantum model is more efficient than classical computers, which can struggle to simulate large, complex systems of particles.

Effective Diagnosis of Lung Cancer via Various Data-Mining Techniques

One of the leading cancers for both genders worldwide is lung cancer.The occurrence of lung cancer has fully augmented since the early 19th century.In this manuscript,we have discussed various data mining techniques that have been employed for cancer diagnosis.Exposure to air pollution has been related to various adverse health effects.This work is subject to analysis of various air pollutants and associated health hazards and intends to evaluate the impact of air pollution caused by lung cancer.We have introduced data mining in lung cancer to air pollution,and our approach includes preprocessing,data mining,testing and evaluation,and knowledge discovery.Initially,we will eradicate the noise and irrelevant data,and following that,we will join the multiple informed sources into a common source.From that source,we will designate the information relevant to our investigation to be regained from that assortment.Following that,we will convert the designated data into a suitable mining process.The patterns are abstracted by utilizing a relational suggestion rule mining process.These patterns have revealed information,and this information is categorized with the help of an Auto Associative Neural Network classification method(AANN).The proposed method is compared with the existing method in various factors.In conclusion,the projected Auto associative neural network and relational suggestion rule mining methods accomplish a high accuracy status.

Kinetic model of vibration screening for granular materials based on biological neural network

The kinetic model is the theoretical basis for optimizing the structure and operation performance of vibration screening devices.In this paper,a biological neurodynamic equation and neural connections were established according to the motion and interaction properties of the material under vibration excitation.The material feeding to the screen and the material passing through apertures were considered as excitatory and inhibitory inputs,respectively,and the generated stable neural activity landscape was used to describe the material distribution on the 2D screen surface.The dynamic process of material vibration screening was simulated using discrete element method(DEM).By comparing the similarity between the material distribution established using biological neural network(BNN)and that obtained using DEM simulation,the optimum coefficients of BNN model under a certain screening parameter were determined,that is,one relationship between the BNN model coefficients and the screening operation parameters was established.Different screening parameters were randomly selected,and the corresponding relationships were established as a database.Then,with straw/grain ratio,aperture diameter,inclination angle,vibration strength in normal and tangential directions as inputs,five independent adaptive neuro-fuzzy inference systems(ANFIS)were established to predict the optimum BNN model coefficients,respectively.The training results indicated that ANFIS models had good stability and accuracy.The flexibility and adaptability of the proposed BNN method was demonstrated by modeling material distribution under complex feeding conditions such as multiple regions and non-uniform rate.

BMTK： a toolkit for determining modules in biological bipartite networks

Background： Module detection is widely used to analyze and visualize biological networks. A number of methods and tools have been developed to achieve it. Meanwhile, bipartite module detection is also very useful for mining and analyzing bipartite biological networks and a few methods have been developed for it. However, there is few user- friendly toolkit for this task. Methods： To this end, we develop an online web toolkit BMTK, which implements seven existing methods. Results： BMTK provides a uniform operation platform and visualization function, standardizes input and output format, and improves algorithmic structure to enhance computing speed. We also apply this toolkit onto a drug-target bipartite network to demonstrate its effectiveness. Conclusions： BMTK will be a powerful tool for detecting bipartite modules in diverse bipartite biological networks. Availability： The web application is freely accessible at http：//www.zhanglabtools.net/BMTK.

Proteomic dissection of biological pathways/processes through profiling protein-protein interaction networks

Cellular functions, either under the normal or pathological conditions or under different stresses, are the results of the coordinated action of multiple proteins interacting in macromolecular complexes or assemblies. The precise determination of the specific composition of protein complexes, especially using scalable and high-throughput methods, represents a systematic approach toward revealing particular cellular biological functions. In this regard, the direct profiling protein-protein interactions (PPIs) represent an efficient way to dissect functional pathways for revealing novel protein functions. In this review, we illustrate the technological evolution for the large-scale and precise identification of PPIs toward higher physiologically relevant accuracy. These techniques aim at improving the efficiency of complex pull-down, the signal specificity and accuracy in distinguishing specific PPIs, and the accuracy of identifying physiological relevant PPIs. A newly developed streamline proteomic approach for mapping the binary relationship of PPIs in a protein complex is introduced.

Nonlinear signal transduction network with multistate

Signal transduction is an important and basic mechanism to cell life activities.The stochastic state transition of receptor induces the release of signaling molecular,which triggers the state transition of other receptors.It constructs a nonlinear sigaling network,and leads to robust switchlike properties which are critical to biological function.Network architectures and state transitions of receptor affect the performance of this biological network.In this work,we perform a study of nonlinear signaling on biological polymorphic network by analyzing network dynamics of the Ca^(2+)-induced Ca^(2+)release(CICR)mechanism,where fast and slow processes are involved and the receptor has four conformational states.Three types of networks,Erdos–R´enyi(ER)network,Watts–Strogatz(WS)network,and BaraB´asi–Albert(BA)network,are considered with different parameters.The dynamics of the biological networks exhibit different patterns at different time scales.At short time scale,the second open state is essential to reproduce the quasi-bistable regime,which emerges at a critical strength of connection for all three states involved in the fast processes and disappears at another critical point.The pattern at short time scale is not sensitive to the network architecture.At long time scale,only monostable regime is observed,and difference of network architectures affects the results more seriously.Our finding identifies features of nonlinear signaling networks with multistate that may underlie their biological function.

Detection and Classification of Diabetic Retinopathy Using DCNN and BSN Models

Diabetes is associated with many complications that could lead to death.Diabetic retinopathy,a complication of diabetes,is difficult to diagnose and may lead to vision loss.Visual identification of micro features in fundus images for the diagnosis of DR is a complex and challenging task for clinicians.Because clinical testing involves complex procedures and is timeconsuming,an automated system would help ophthalmologists to detect DR and administer treatment in a timelymanner so that blindness can be avoided.Previous research works have focused on image processing algorithms,or neural networks,or signal processing techniques alone to detect diabetic retinopathy.Therefore,we aimed to develop a novel integrated approach to increase the accuracy of detection.This approach utilized both convolutional neural networks and signal processing techniques.In this proposed method,the biological electro retinogram(ERG)sensor network(BSN)and deep convolution neural network(DCNN)were developed to detect and classify DR.In the BSN system,electrodes were used to record ERGsignal,which was preprocessed to be noise-free.Processing was performed in the frequency domain by the application of fast Fourier transform(FFT)and mel frequency cepstral coefficients(MFCCs)were extracted.Artificial neural network(ANN)classifier was used to classify the signals of eyes with DR and normal eye.Additionally,fundus images were captured using a fundus camera,and these were used as the input for DCNN-based analysis.The DCNN consisted of many layers to facilitate the extraction of features and classification of fundus images into normal images,non-proliferative DR(NPDR)or earlystage DR images,and proliferative DR(PDR)or advanced-stage DR images.Furthermore,it classifiedNPDRaccording tomicroaneurysms,hemorrhages,cotton wool spots,and exudates,and the presence of new blood vessels indicated PDR.The accuracy,sensitivity,and specificity of the ANNclassifier were found to be 94%,95%,and 93%,respectively.Both the accuracy rate and sensitivity rate of theDCNNclassifierwas 96.5%for the images acquired from various hospitals as well as databases.A comparison between the accuracy rates of BSN andDCNN approaches showed thatDCNNwith fundus images decreased the error rate to 4%.

Statistical Identification of Important Nodes in Biological Systems

Biological systems can be modeled and described by biological networks.Biological networks are typical complex networks with widely real-world applications.Many problems arising in biological systems can be boiled down to the identification of important nodes.For example,biomedical researchers frequently need to identify important genes that potentially leaded to disease phenotypes in animal and explore crucial genes that were responsible for stress responsiveness in plants.To facilitate the identification of important nodes in biological systems,one needs to know network structures or behavioral data of nodes(such as gene expression data).If network topology was known,various centrality measures can be developed to solve the problem;while if only behavioral data of nodes were given,some sophisticated statistical methods can be employed.This paper reviewed some of the recent works on statistical identification of important nodes in biological systems from three aspects,that is,1)in general complex networks based on complex networks theory and epidemic dynamic models;2)in biological networks based on network motifs;and 3)in plants based on RNA-seq data.The identification of important nodes in a complex system can be seen as a mapping from the system to the ranking score vector of nodes,such mapping is not necessarily with explicit form.The three aspects reflected three typical approaches on ranking nodes in biological systems and can be integrated into one general framework.This paper also proposed some challenges and future works on the related topics.The associated investigations have potential real-world applications in the control of biological systems,network medicine and new variety cultivation of crops.

PBNA: An Improved Probabilistic Biological Network Alignment Method

Biological network alignment is an important research topic in the field of bioinformatics. Nowadays almost every existing alignment method is designed to solve the deterministic biological network alignment problem.However, it is worth noting that interactions in biological networks, like many other processes in the biological realm,are probabilistic events. Therefore, more accurate and better results can be obtained if biological networks are characterized by probabilistic graphs. This probabilistic information, however, increases difficulties in analyzing networks and only few methods can handle the probabilistic information. Therefore, in this paper, an improved Probabilistic Biological Network Alignment（PBNA） is proposed. Based on Iso Rank, PBNA is able to use the probabilistic information. Furthermore, PBNA takes advantages of Contributor and Probability Generating Function（PGF） to improve the accuracy of node similarity value and reduce the computational complexity of random variables in similarity matrix. Experimental results on dataset of the Protein-Protein Interaction（PPI） networks provided by Todor demonstrate that PBNA can produce some alignment results that ignored by the deterministic methods, and produce more biologically meaningful alignment results than Iso Rank does in most of the cases based on the Gene Ontology Consistency（GOC） measure. Compared with Prob method, which is designed exactly to solve the probabilistic alignment problem, PBNA can obtain more biologically meaningful mappings in less time.

Constructing backbone network by using tinker algorithm

Revealing how a biological network is organized to realize its function is one of the main topics in systems biology. Tile functional backbone network, defined as the primary structure of the biological network, is of great importance in maintaining the main function of the biological network. We propose a new algorithm, the tinker algorithm, to determine this core structure and apply it in the cell-cycle system. With this algorithm, the backbone network of the cell-cycle network can be determined accurately and efficiently in various models such as the Boolean model, stochastic model, and ordinary differential equation model. Results show that our algorithm is more efficient than that used in the previous research. We hope this method can be put into practical use in relevant future studies.

Multiomics metabolic and epigenetics regulatory network in cancer:A systems biology perspective

Genetic,epigenetic,and metabolic alterations are all hallmarks of cancer.However,the epigenome and metabolome are both highly complex and dynamic biological networks in vivo.The interplay between the epigenome and metabolome contributes to a biological system that is responsive to the tumor microenvironment and possesses a wealth of unknown biomarkers and targets of cancer therapy.From this perspective,we first review the state of high-throughput biological data acquisition(i.e.multiomics data)and analysis(i.e.computational tools)and then propose a conceptual in silico metabolic and epigenetic regulatory network(MER-Net)that is based on these current high-throughput methods.The conceptual MER-Net is aimed at linking metabolomic and epigenomic networks through observation of biological processes,omics data acquisition,analysis of network information,and integration with validated database knowledge.Thus,MER-Net could be used to reveal new potential biomarkers and therapeutic targets using deep learning models to integrate and analyze large multiomics networks.We propose that MER-Net can serve as a tool to guide integrated metabolomics and epigenomics research or can be modified to answer other complex biological and clinical questions using multiomics data.

Recovering context-specific gene network modules from expression data: A brief review

With the popularization of microarray experi-ments in biomedical laboratories,how to make context-specific knowledge discovery from expression data becomes a hot topic.While the static“reference networks”for key model organisms are nearly at hand,the endeavors to recover context-specific network modules are still at the beginning.Currently,this is achieved through filtering existing edges of the ensemble reference network or constructing gene networks ab initio.In this paper,we briefly review recent progress in the field and point out some research directions awaiting improved work,includ-ing expression-data-guided revision of reference networks.

Bioinformatics—Mining the genome for information

Since the launching of the human genome sequencing project in the 1990s,genomic research has already achieved definite results.At the beginning of the present century,the complete genomes of several model organisms have already been sequenced,including a number of prokaryote microorganisms and the eukaryotes yeast(Saccharomyces cerevisiae),nematode(C.elegans),fruit fly(Drosophila melanogaster)and thale cress(Arabidopsis thaliana)as well as the major part of the human genome.These achievements signified that a new era of data mining and analysis on the human genome had commenced.The language of human genetics would gradually be read and understood,and the genetic information underlying metabolism,development,differentiation and evolution would progressively become known to mankind.Large amounts of data are already accumulating,but at present many of the rules that should guide the understanding of this information are yet unknown.Bioinformatics research is thus not only becoming more important,but is also faced with severe challenges as well as great opportunities.