Genetic programming‐based approach to elucidate biochemical interaction networks from data

Biochemical systems are characterised by cyclic/reversible reciprocal actions, non‐linear interactions and a mixed relationship structures (linear and non‐linear; static and dynamic). Deciphering the architecture of such systems using measured data to provide quantitative information regarding the nature of relationships that exist between the measured variables is a challenging proposition. Causality detection is one of the methodologies that are applied to elucidate biochemical networks from such data. Autoregressive‐based modelling approach such as granger causality, partial directed coherence, directed transfer function and canonical variate analysis have been applied on different systems for deciphering such interactions, but with limited success. In this study, the authors propose a genetic programming‐based causality detection (GPCD) methodology which blends evolutionary computation‐based procedures along with parameter estimation methods to derive a mathematical model of the system. Application of the GPCD methodology on five data sets that contained the different challenges mentioned above indicated that GPCD performs better than the other methods in uncovering the exact structure with less false positives. On a glycolysis data set, GPCD was able to fill the ‘interaction gaps’ which were missed by other methods.Inspec keywords: autoregressive processes, biochemistry, biology computing, genetic algorithms, parameter estimation, transfer functionsOther keywords: elucidate biochemical interaction networks, biochemical systems, cyclic‐reversible reciprocal actions, nonlinear interactions, autoregressive‐based modelling, granger causality, transfer function, canonical variate analysis, genetic programming‐based causality detection methodology, GPCD methodology, mathematical model, parameter estimation methods     

Anti‐triangle centrality‐based community detection in complex networks

Community detection has been extensively studied in the past decades largely because of the fact that community exists in various networks such as technological, social and biological networks. Most of the available algorithms, however, only focus on the properties of the vertices, ignoring the roles of the edges. To explore the roles of the edges in the networks for community discovery, the authors introduce the novel edge centrality based on its antitriangle property. To investigate how the edge centrality characterises the community structure, they develop an approach based on the edge antitriangle centrality with the isolated vertex handling strategy (EACH) for community detection. EACH first calculates the edge antitriangle centrality scores for all the edges of a given network and removes the edge with the highest score per iteration until the scores of the remaining edges are all zero. Furthermore, EACH is characterised by being free of the parameters and independent of any additional measures to determine the community structure. To demonstrate the effectiveness of EACH, they compare it with the state‐of‐the art algorithms on both the synthetic networks and the real world networks. The experimental results show that EACH is more accurate and has lower complexity in terms of community discovery and especially it can gain quite inherent and consistent communities with a maximal diameter of four jumps.Inspec keywords: biology computing, complex networks, graph theory, social sciences computingOther keywords: antitriangle centrality‐based community detection, complex networks, technological networks, social networks, biological networks, vertex properties, edge roles, community discovery, antitriangle property, community structure, edge antitriangle centrality, isolated vertex handling strategy, EACH, antitriangle centrality scores, synthetic networks, real world networks     

Detecting small attractors of large Boolean networks by function‐reduction‐based strategy

Boolean networks (BNs) are widely used to model gene regulatory networks and to design therapeutic intervention strategies to affect the long‐term behaviour of systems. A central aim of Boolean‐network analysis is to find attractors that correspond to various cellular states, such as cell types or the stage of cell differentiation. This problem is NP‐hard and various algorithms have been used to tackle it with considerable success. The idea is that a singleton attractor corresponds to n consistent subsequences in the truth table. To find these subsequences, the authors gradually reduce the entire truth table of Boolean functions by extending a partial gene activity profile (GAP). Not only does this process delete inconsistent subsequences in truth tables, it also directly determines values for some nodes not extended, which means it can abandon the partial GAPs that cannot lead to an attractor as early as possible. The results of simulation show that the proposed algorithm can detect small attractors with length p = 4 in BNs of up to 200 nodes with average indegree K = 2.Inspec keywords: Boolean functions, genetics, cellular biophysicsOther keywords: detecting small attractors, function‐reduction‐based strategy, model gene regulatory networks, therapeutic intervention strategies, Boolean‐network analysis, cellular states, NP‐hard, singleton attractor, Boolean functions, partial gene activity profile, cell differentiation     

M‐matrix‐based stability conditions for genetic regulatory networks with time‐varying delays and noise perturbations

Stability is essential for designing and controlling any dynamic systems. Recently, the stability of genetic regulatory networks has been widely studied by employing linear matrix inequality (LMI) approach, which results in checking the existence of feasible solutions to high‐dimensional LMIs. In the previous study, the authors present several stability conditions for genetic regulatory networks with time‐varying delays, based on M ‐matrix theory and using the non‐smooth Lyapunov function, which results in determining whether a low‐dimensional matrix is a non‐singular M ‐matrix. However, the previous approach cannot be applied to analyse the stability of genetic regulatory networks with noise perturbations. Here, the authors design a smooth Lyapunov function quadratic in state variables and employ M ‐matrix theory to derive new stability conditions for genetic regulatory networks with time‐varying delays. Theoretically, these conditions are less conservative than existing ones in some genetic regulatory networks. Then the results are extended to genetic regulatory networks with time‐varying delays and noise perturbations. For genetic regulatory networks with n genes and n proteins, the derived conditions are to check if an n × n matrix is a non‐singular M ‐matrix. To further present the new theories proposed in this study, three example regulatory networks are analysed.Inspec keywords: genetics, linear matrix inequalities, Lyapunov matrix equations, molecular biophysics, noise, proteinsOther keywords: M‐matrix‐based stability condition, genetic regulatory networks, time‐varying delays, noise perturbations, linear matrix inequality approach, high‐dimensional LMI, Lyapunov function, state variables, M‐matrix theory, proteins, nonsingular M‐matrix     

Biclustering‐based association rule mining approach for predicting cancer‐associated protein interactions

Protein–protein interactions (PPIs) have been widely used to understand different biological processes and cellular functions associated with several diseases like cancer. Although some cancer‐related protein interaction databases are available, lack of experimental data and conflicting PPI data among different available databases have slowed down the cancer research. Therefore, in this study, the authors have focused on various proteins that are directly related to different types of cancer disease. They have prepared a PPI database between cancer‐associated proteins with the rest of the human proteins. They have also incorporated the annotation type and direction of each interaction. Subsequently, a biclustering‐based association rule mining algorithm is applied to predict new interactions with type and direction. This study shows the prediction power of association rule mining algorithm over the traditional classifier model without choosing a negative data set. The time complexity of the biclustering‐based association rule mining is also analysed and compared to traditional association rule mining. The authors are able to discover 38 new PPIs which are not present in the cancer database. The biological relevance of these newly predicted interactions is analysed by published literature. Recognition of such interactions may accelerate a way of developing new drugs to prevent different cancer‐related diseases.Inspec keywords: cancer, medical computing, data mining, proteins, genetics, pattern clusteringOther keywords: biological processes, cancer‐related diseases, cancer research, cancer‐related protein interaction databases, protein–protein interactions, cancer‐associated protein interactions, biclustering‐based association rule mining approach, negative data set, annotation type, human proteins, cancer‐associated proteins, PPI database, cancer disease     

Two faces of competition: target‐mediated reverse signalling in microRNA and mitogen‐activated protein kinase regulatory networks

Biomolecular regulatory networks are organised around hubs, which can interact with a large number of targets. These targets compete with each other for access to their common hubs, but whether the effect of this competition would be significant in magnitude and in function is not clear. In this review, the authors discuss recent in vivo studies that analysed the system level retroactive effects induced by target competition in microRNA and mitogen‐activated protein kinase regulatory networks. The results of these studies suggest that downstream targets can regulate the overall state of their upstream regulators, and thus cannot be ignored in analysing biomolecular networks.Inspec keywords: reviews, RNA, molecular biophysics, enzymesOther keywords: target‐mediated reverse signalling, mitogen‐activated protein kinase regulatory networks, biomolecular regulatory networks, microRNA regulatory networks, review, in vivo study     

Inferring non‐synonymous single‐nucleotide polymorphisms‐disease associations via integration of multiple similarity networks

Detecting associations between human genetic variants and their phenotypic effects is a significant problem in understanding genetic bases of human‐inherited diseases. The focus is on a typical type of genetic variants called non‐synonymous single nucleotide polymorphisms (nsSNPs), whose occurrence may potentially alter the structures of proteins, affecting functions of proteins, and thereby causing diseases. Most of the existing methods predict associations between nsSNPs and diseases based on features derived from only protein sequence and/or structure information, and give no information about which specific disease an nsSNP is associated with. To cope with these problems, the identification of nsSNPs that are associated with a specific disease from a set of candidate nsSNPs as a binary classification problem has been formulated. A new approach has been adopted for predicting associations between nsSNPs and diseases based on multiple nsSNP similarity networks and disease phenotype similarity networks. With a series of comprehensive validation experiments, it has been demonstrated that the proposed method is effective in both recovering the nsSNP‐disease associations and inferring suspect disease‐associated nsSNPs for both diseases with known genetic bases and diseases of unknown genetic bases.Inspec keywords: diseases, genetics, polymorphism, proteinsOther keywords: nonsynonymous single‐nucleotide polymorphisms, disease associations, multiple similarity network integration, human genetic variants, phenotypic effects, human‐inherited diseases, nonsynonymous single nucleotide polymorphisms, proteins, protein sequence, structure information, candidate nsSNP, binary classification problem, disease phenotype similarity networks, unknown genetic bases     

Integer programming‐based method for observability of singleton attractors in Boolean networks

Boolean network (BN) is a popular mathematical model for revealing the behaviour of a genetic regulatory network. Furthermore, observability, an important network feature, plays a significant role in understanding the underlying network. Several studies have been done on analysis of observability of BNs and complex networks. However, the observability of attractor cycles, which can serve as biomarker detection, has not yet been addressed in the literature. This is an important, interesting and challenging problem that deserves a detailed study. In this study, a novel problem was first proposed on attractor observability in BNs. Identification of the minimum set of consecutive nodes can be used to discriminate different attractors. Furthermore, it can serve as a biomarker for different disease types (represented as different attractor cycles). Then a novel integer programming method was developed to identify the desired set of nodes. The proposed approach is demonstrated and verified by numerical examples. The computational results further illustrates that the proposed model is effective and efficient.Inspec keywords: integer programming, Boolean algebra, complex networks, diseasesOther keywords: disease, consecutive nodes, biomarker detection, attractor cycles, complex networks, genetic regulatory network, mathematical model, Boolean networks, singleton attractors, integer programming‐based method     

On‐chip label‐free impedance‐based detection of antibiotic permeation

Biosensors are analytical tools used for the analysis of biomaterial samples and provide an understanding about the biocomposition, structure, and function of biomolecules and/or biomechanisms by converting the biological response into an electrical and/or optical signal. In particular, with the rise in antibiotic resistance amongst pathogenic bacteria, the study of antibiotic activity and transport across cell membranes in the field of biosensors has been gaining widespread importance. Herein, for the rapid and label‐free detection of antibiotic permeation across a membrane, a microelectrode integrated microfluidic device is presented. The integrated chip consists of polydimethylsiloxane based microfluidic channels bonded onto microelectrodes on‐glass and enables us to recognize the antibiotic permeation across a membrane into the model membranes based on electrical impedance measurement, while also allowing optical monitoring. Impedance testing is label free and therefore allows the detection of both fluorescent and non‐fluorescent antibiotics. As a model membrane, Giant Unilamellar Vesicles (GUVs) are used and impedance measurements were performed by a precision inductance, capacitance, and resistance metre. The measured signal recorded from the device was used to determine the change in concentration inside and outside of the GUVs. We have found that permeation of antibiotic molecules can be easily monitored over time using the proposed integrated device. The results also show a clear difference between bilayer permeation that occurs through the lipidic bilayer and porin‐mediated permeation through the porin channels inserted in the lipid bilayer.     

Identifying driver modules based on multi‐omics biological networks in prostate cancer

The development of sequencing technology has promoted the expansion of cancer genome data. It is necessary to identify the pathogenesis of cancer at the molecular level and explore reliable treatment methods and precise drug targets in cancer by identifying carcinogenic functional modules in massive multi‐omics data. However, there are still limitations to identifying carcinogenic driver modules by utilising genetic characteristics simply. Therefore, this study proposes a computational method, NetAP, to identify driver modules in prostate cancer. Firstly, high mutual exclusivity, high coverage, and high topological similarity between genes are integrated to construct a weight function, which calculates the weight of gene pairs in a biological network. Secondly, the random walk method is utilised to reevaluate the strength of interaction among genes. Finally, the optimal driver modules are identified by utilising the affinity propagation algorithm. According to the results, the authors’ method identifies more validated driver genes and driver modules compared with the other previous methods. Thus, the proposed NetAP method can identify carcinogenic driver modules effectively and reliably, and the experimental results provide a powerful basis for cancer diagnosis, treatment and drug targets.

Abbreviations

CNV

Copy number variation

FN

False Negative

FP

False Positive

HPRD

Human protein reference database

JS

Jensen–Shannon

KL

Kullback–Leibler

NCG

A comprehensive catalogue of known and candidate cancer genes from cancer sequencing screens.

PRAD

Prostate adenocarcinoma

PPI

Protein–protein interaction

TCGA

The cancer genome Atlas

TN

True Negative

TP

True Positive

     

Voxel‐based meshing and unit‐cell analysis of textile composites

Unit‐cell homogenization techniques are frequently used together with the finite element method to compute effective mechanical properties for a wide range of different composites and heterogeneous materials systems. For systems with very complicated material arrangements, mesh generation can be a considerable obstacle to usage of these techniques. In this work, pixel‐based (2D) and voxel‐based (3D) meshing concepts borrowed from image processing are thus developed and employed to construct the finite element models used in computing the micro‐scale stress and strain fields in the composite. The potential advantage of these techniques is that generation of unit‐cell models can be automated, thus requiring far less human time than traditional finite element models. Essential ideas and algorithms for implementation of proposed techniques are presented. In addition, a new error estimator based on sensitivity of virtual strain energy to mesh refinement is presented and applied. The computational costs and rate of convergence for the proposed methods are presented for three different mesh‐refinement algorithms: uniform refinement; selective refinement based on material boundary resolution; and adaptive refinement based on error estimation. Copyright © 2003 John Wiley & Sons, Ltd.     

Investigation of graphene‐on‐metal substrates for SPR‐based sensor using finite‐difference time domain

In this study, the authors investigated the effects of a single layer graphene as a coating layer on top of metal thin films such as silver, gold, aluminum and copper using finite‐difference time domain method. To enhance the resolution of surface plasmon resonance (SPR) sensor, it is necessary to increase the SPR reflectivity and decrease the full‐width‐half maximum (FWHM) of the SPR curve so that there is minimum uncertainty in the determination of the resonance dip. Numerical data was verified with analytical and experimental data where all the data were in good agreement with resonance angle differing in <10% due to noise present in components such as humidity and temperature. In further analysis, reflectivity and FWHM were compared among four types of metal with various thin film thicknesses where graphene was applied on top of the metal layers, and data was compared against pure conventional metal thin films. A 60 nm‐thick Au thin film results in higher performance with reflectivity of 92.4% and FWHM of 0.88° whereas single layer graphene‐on‐60 nm‐thick Au gave reflectivity of 91.7% and FWHM of 1.32°. However, a graphene‐on‐40 nm‐thick Ag also gave good performance with narrower FWHM of 0.88° and reflection spectra of 89.2%.Inspec keywords: graphene, surface plasmon resonance, finite difference time‐domain analysis, reflectivity, metallic thin films, silver, gold, aluminium, copper, chemical sensors, biological techniquesOther keywords: graphene‐on‐metal substrates, SPR‐based sensor, finite‐difference time domain, metal thin films, surface plasmon resonance sensor, SPR curve, resonance angles, reflectivity, C, Ag, Au, Al, Cu     

Chattering‐ free hybrid adaptive neuro‐fuzzy inference system‐particle swarm optimisation data fusion‐based BG‐level control

In this study, a closed‐loop control scheme is proposed for the glucose–insulin regulatory system in type‐1 diabetic mellitus (T1DM) patients. Some innovative hybrid glucose–insulin regulators have combined artificial intelligence such as fuzzy logic and genetic algorithm with well known Palumbo model to regulate the blood glucose (BG) level in T1DM patients. However, most of these approaches have focused on the glucose reference tracking, and the qualitative of this tracking such as chattering reduction of insulin injection has not been well‐studied. Higher‐order sliding mode (HoSM) controllers have been employed to attenuate the effect of chattering. Owing to the delayed nature and non‐linear property of glucose–insulin mechanism as well as various unmeasurable disturbances, even the HoSM methods are partly successful. In this study, data fusion of adaptive neuro‐fuzzy inference systems optimised by particle swarm optimisation has been presented. The excellent performance of the proposed hybrid controller, i.e. desired BG‐level tracking and chattering reduction in the presence of daily glucose‐level disturbances is verified.Inspec keywords: fuzzy control, variable structure systems, particle swarm optimisation, neurocontrollers, fuzzy neural nets, blood, genetic algorithms, closed loop systems, medical control systems, fuzzy reasoning, diseases, nonlinear control systems, sugarOther keywords: data fusion, adaptive neuro‐fuzzy inference systems, particle swarm optimisation, hybrid controller, desired BG‐level tracking, chattering reduction, daily glucose‐level disturbances, closed‐loop control scheme, glucose–insulin regulatory system, type‐1 diabetic mellitus patients, innovative hybrid glucose–insulin regulators, artificial intelligence, fuzzy logic, genetic algorithm, Palumbo model, blood glucose level, T1DM patients, glucose reference tracking, insulin injection, mode controllers, glucose–insulin mechanism, chattering‐free hybrid adaptive neuro‐fuzzy inference system, particle swarm optimisation data fusion‐based BG‐level control     

Analysis and classification of DNA‐binding sites in single‐stranded and double‐stranded DNA‐binding proteins using protein information

Single‐stranded DNA‐binding proteins (SSBs) and double‐stranded DNA‐binding proteins (DSBs) play different roles in biological processes when they bind to single‐stranded DNA (ssDNA) or double‐stranded DNA (dsDNA). However, the underlying binding mechanisms of SSBs and DSBs have not yet been fully understood. Here, the authors firstly constructed two groups of ssDNA and dsDNA specific binding sites from two non‐redundant sets of SSBs and DSBs. They further analysed the relationship between the two classes of binding sites and a newly proposed set of features (residue charge distribution, secondary structure and spatial shape). To assess and utilise the predictive power of these features, they trained a classification model using support vector machine to make predictions about the ssDNA and the dsDNA binding sites. The author''s analysis and prediction results indicated that the two classes of binding sites can be distinguishable by the three types of features, and the final classifier using all the features achieved satisfactory performance. In conclusion, the proposed features will deepen their understanding of the specificity of proteins which bind to ssDNA or dsDNA.Inspec keywords: biology computing, DNA, molecular biophysics, molecular configurations, pattern classification, proteins, support vector machinesOther keywords: dsDNA binding sites, ssDNA binding sites, support vector machine, classiflcation model, spatial shape, secondary structure, residue charge distribution, binding mechanisms, biological process, protein information, double‐stranded DNA‐binding proteins, single‐stranded DNA‐binding proteins     

Observer‐based resilient finite‐time control of blood gases model during extra‐corporeal circulation

This study aims at designing an observer‐based resilient controller to regulate the amount of oxygen and carbon dioxide in the blood of patients during the extra‐corporeal blood circulation process. More precisely, in this study, a suitable observer‐based resilient controller is constructed to regulate the levels of patient blood gases in a finite interval of time. The finite‐time boundedness with the prescribed H∞ performance index of the considered blood gases control system against modelling uncertainty and external disturbances is ensured by using Lyapunov stability analysis. Moreover, a set of sufficient conditions for obtaining the controller gain is developed in the form of linear matrix inequalities (LMIs). Finally, the effectiveness of the proposed robust finite‐time control scheme is verified through simulation results. The result reveals that the blood gases are maintained in their physiological ranges during a stable extra‐corporeal circulation process via the proposed observer‐based resilient controller.Inspec keywords: blood, haemodynamics, oxygen, carbon compounds, controllers, medical control systems, biomedical equipment, Lyapunov methods, linear matrix inequalitiesOther keywords: observer‐based resilient finite‐time control, observer‐based resilient controller, oxygen amount, carbon dioxide amount, extracorporeal blood circulation process, patient blood gas levels, finite time interval, finite‐time boundedness, H_∞ performance index, blood gases control system, Lyapunov stability analysis, controller gain, linear matrix inequalities, physiological ranges, LMIs, CO₂ , O₂      

In vitro evaluation of biodegradable nHAP‐Chitosan‐Gelatin‐based scaffold for tissue engineering application

The present study focuses on fabrication and characterisation of porous composite scaffold containing hydroxyapatite (HAP), chitosan, and gelatin with an average pore size of 250–1010 nm for improving wound repair and regeneration by Electrospinning method. From the results of X ‐Ray Diffraction (XRD) study, the peaks correspond to crystallographic structure of HAP powder. The presence of functional group bonds of HAP powder, Chitosan and scaffold was studied using Fourier Transform Infrared Spectroscopy (FTIR). The surface morphology of the scaffold was observed using Scanning Electron Microscope (SEM). The Bioactivity of the Nano composite scaffolds was studied using simulated body fluid solution at 37 ± 1°C. The biodegradability test was studied using Tris‐Buffer solution for the prepared nanocomposites [nano Chitosan, nano Chitosan gelatin, Nano based Hydroxyapatite Chitosan gelatin]. The cell migration and potential biocompatibility of nHAP‐chitosan‐gelatin scaffold was assessed via wound scratch assay and were compared to povedeen as control. Cytocompatibility evaluation for Vero Cells using wound scratch assay showed that the fabricated porous nanocomposite scaffold possess higher cell proliferation and growth than that of povedeen. Thus, the study showed that the developed nanocomposite scaffolds are potential candidates for regenerating damaged cell tissue in wound healing process.Inspec keywords: nanofabrication, tissue engineering, electrospinning, wounds, cellular biophysics, scanning electron microscopy, surface morphology, X‐ray diffraction, biomedical materials, nanomedicine, porosity, biodegradable materials, nanoporous materials, calcium compounds, gelatin, nanocomposites, Fourier transform infrared spectra, nanoparticles, precipitation (physical chemistry)Other keywords: average pore size, wound repair, crystallographic structure, HAP powder, functional group bonds, simulated body fluid solution, biodegradability test, Tris‐Buffer solution, cell migration, wound scratch assay, tissue engineering, electrospinning method, X‐ray diffraction, Fourier transform infrared spectroscopy, scanning electron microscopy, biocompatibility, cytocompatibility, porous nanocomposite scaffold, cell tissue, nHAP‐chitosan‐gelatin scaffold composites, wet chemical precipitation method, surface morphology, nanohydroxyapatite‐nanochitosan‐gelatin scaffold composites, cell proliferation, wound healing, (Ca₁₀ (PO₄)₆ (OH)₂)     

Identifying genuine protein–protein interactions within communities of gene co‐expression networks using a deconvolution method

Direct relationships between biological molecules connected in a gene co‐expression network tend to reflect real biological activities such as gene regulation, protein–protein interactions (PPIs), and metabolisation. As correlation‐based networks contain numerous indirect connections, those direct relationships are always ‘hidden’ in them. Compared with the global network, network communities imply more biological significance on predicting protein function, detecting protein complexes and studying network evolution. Therefore, identifying direct relationships in communities is a pervasive and important topic in the biological sciences. Unfortunately, this field has not been well studied. A major thrust of this study is to apply a deconvolution algorithm on communities stemming from different gene co‐expression networks, which are constructed by fixing different thresholds for robustness analysis. Using the fifth Dialogue on Reverse Engineering Assessment and Methods challenge (DREAM5) framework, the authors demonstrate that nearly all new communities extracted from a ‘deconvolution filter’ contain more genuine PPIs than before deconvolution.Inspec keywords: proteins, deconvolution, genetics, bioinformatics, biology computing, molecular biophysicsOther keywords: identifying genuine protein–protein interactions, gene co‐expression network, deconvolution method, direct relationships, biological molecules, biological activities, gene regulation, correlation‐based networks, numerous indirect connections, global network, network communities, biological significance, protein function, protein complexes, studying network evolution, biological sciences, different gene co‐expression networks     

Microelectromechanical system‐based biosensor for label‐free detection of human cytomegalovirus

The human cytomegalovirus (HCMV) is an asymptomatic common virus that is typically harmless, but in some cases, it can be life threatening. Thus, there is an urgent need to develop novel diagnostic methods and strengthen the efforts to combat this virus. A microcantilever‐based biosensor functionalised with the UL83‐antibody of HCMV (UL83‐HCMV antibody) has been developed to detect the UL83‐antigen of HCMV (UL83‐HCMV antigen) at different concentrations ranging from 0.3 to 300 ng/ml. The response of the biosensor to the presence of UL83‐HCMV antigen was measured through the shift in resonance frequency before and after antigen–antibody binding. The system shows a low detection limit of 84 pg/ml, which is comparable to traditional sensors, and a detection time of less than 15 min was achieved. The selectivity of the sensor was demonstrated using three different proteins with and without the UL83‐HCMV antigen. The biosensor shows high selectivity for the UL83‐HCMV antigen. Mass loading by the UL83‐HCMV antigen was roughly estimated with a sensitivity of ∼30 fg/Hz. This technique is crucial for the fabrication of portable and low‐cost biosensors that can be used in real‐time monitoring and enables early medical diagnosis.     

Identification of essential proteins based on a new combination of topological and biological features in weighted protein–protein interaction networks

The identification of essential proteins in protein–protein interaction (PPI) networks is not only important in understanding the process of cellular life but also useful in diagnosis and drug design. The network topology‐based centrality measures are sensitive to noise of network. Moreover, these measures cannot detect low‐connectivity essential proteins. The authors have proposed a new method using a combination of topological centrality measures and biological features based on statistical analyses of essential proteins and protein complexes. With incomplete PPI networks, they face the challenge of false‐positive interactions. To remove these interactions, the PPI networks are weighted by gene ontology. Furthermore, they use a combination of classifiers, including the newly proposed measures and traditional weighted centrality measures, to improve the precision of identification. This combination is evaluated using the logistic regression model in terms of significance levels. The proposed method has been implemented and compared to both previous and more recent efficient computational methods using six statistical standards. The results show that the proposed method is more precise in identifying essential proteins than the previous methods. This level of precision was obtained through the use of four different data sets: YHQ‐W, YMBD‐W, YDIP‐W and YMIPS‐W.Inspec keywords: proteins, drugs, biology computing, ontologies (artificial intelligence), topologyOther keywords: biological features, weighted protein–protein interaction networks, network topology‐based centrality measures, low‐connectivity essential proteins, topological centrality measures, protein complexes, incomplete PPI networks, weighted centrality measures, YHQ‐W dataset, YMBD‐W dataset, YDIP‐W dataset, YMIPS‐W dataset