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The human brain is not only one of the most important organs in the human body, but it is also one of the most convoluted and is still not fully understood.As the process of aging continues, the brain goes through a number of changes as a whole, including both physical and histological shifts Study Design: Research utilizing a cross-sectional prospective study design Study Duration: January 2021 and continuing through November 2021. Place of Study: Allama Iqbal memorial teaching hospital khawaja Muhammad safdar medical college Sialkot Methodology: The participants in the study were given a standardized questionnaire to complete, in addition to a variety of checklists, in order to complete their assessments. The data was transferred over to SPSS version 23 for additional analysis. In order to investigate the nature of the connection that exists between age and gender, in addition to the height and diameter of the third ventricle, we turned to Pearson's product-moment correlation coefficient. Result: The analysis of variance (ANOVA), which calculates the mean difference between age groups, was performed. The p-value needs to be lower than 0.05 in order for the experiment to be considered statistically significant. The mean height of the third ventricle in males was 13.57 millimeters and the mean height in females was 13.52 millimeters. The mean diameter of the third ventricle in males was 2.98 millimeters and the mean diameter in females was 2.56 millimeters. When the size of the third ventricle in males was compared to that of the third ventricle in females, it was discovered that the size of the third ventricle in males is greater than that of the third ventricle in females. Keywords: CT Scan, ventricles dimensions, hydrocephalus, schizophrenia, psychotic disorders

Objective: Our research aims to compare the diagnostic efficacy of transvaginal and transabdominal ultrasonography for detecting adnexal masses. Study Design: A Comparative study Place and Duration: Conducted at Central Park Medical College, Lahore, during from the period August 2021 to January 2022. Methods: This study included 84 women. Once written consent has been obtained, we involve consideration such as aging, gender, menstrual abnormalities (Oligomenorrhea / Menstrual periods), infertility, recurrent multiple miscarriages, series of acts, acne vulgaris, and obesity. The ultrasound data included the location, size, borders, hypoechoic and dilatation of ovary lesions. Group M had 42 patients and underwent for transvaginal ultrasound, whereas group N had transabdominal ultrasound with MRI. Using histological results (positive/negative), frequency of adnexal mass among all cases were compared. SPSS 20.0 analyzed all data. Results: In group M mean parity was 3.5±2.13 and in group N parity was 2.9±4.13. Most common complaint among all cases was infertility and irregular cycle. We found that frequency of adnexal mass in group M was lower than that of group N. It was determined that the specificity, positive predictive value, and negative predictive value of transvaginal sonography were all lower than those of transabdominal sonography. Conclusion: In this study, we came to the conclusion that abdominal ultrasonography combined with MRI was superior in terms of its usefulness and efficacy in identifying adnexal masses with genuine levels of both specificity and sensitivity. Keywords: Accuracy, Transvaginal ultrasonography,Transabdominal ultrasonography, Adnexal Masses, Complications

Objective: Using histology as the gold standard, this study aims to determine whether or not magnetic resonance spectroscopy can reliably diagnose malignant breast lesions. Study Design: Cross-sectional Place and Duration: Conducted at department of Radiology Central Park Medical College, Lahore. Duration was 6 months from October 2021 to March 2022. Methods: This research included 75 females. Malignancy-suspicious palpable lesions were included. Patients completed a consent form admitting the hazards before any data was gathered. The kinematics and morphology of dynamically enhanced magnetic resonance (MRS) images were studied. MRS's choline peak (Cho) was utilized to detect cancer. Single-voxel technique was employed to evaluate MRS' diagnostic accuracy in cancer. MRS and biopsies were compared. Data was analyzed using SPSS 24.0. Results: Malignant lesions were found in 65 (86.7%) of the patients by MRS, and in 58 (77.3%) of the cases according to the histological findings. Malignant lesions by MRS most frequently showed a ductal enhancement or a peripheral enhancement morphology. The results of MRS showed a 78% specificity, 85% accuracy, 90% sensitivity, 75% NPV, and 91% PPV. Conclusion: MRS must be employed because of its better specificity, sensitivity, and accuracy in characterizing breast lumps. MRS is a specific, sensitive, and effective diagnostic technique for breast cancer. Keywords: Accuracy, Magnetic resonance spectroscopy (MRS), Malignant breast lesion, Histopathology

This cross-sectional study is aimed at evaluating the association of mediastinal lymphadenopathy with COVID-19 prognosis in severe cases. Place and Duration of Study: Department of Medicine, Pak Emirates Military Hospital, Pakistan, from June to July 2020.One hundred and fifty (150) laboratory-confirmed SARS CoV-2 infected, severe cases in Intensive Care Unit/ High Dependency Unit were included. These cases were divided into two categories, i.e., with and without mediastinal lymphadenopathy on High Resolution Computed Tomography chest. The two categories were compared on the basis of data obtained including age, gender, comorbid, White Blood Cell count, lymphocyte count, median days of hospitalization, need for invasive ventilation, Intensive Care Unit admission, clinical outcome and High-Resolution Computed Tomography chest findings. The data was compiled on a questionnaire and analysed on SPSS 24.Total 155 severe COVID-19 patients were reviewed, out of which 36 (23.2%) had mediastinal lymphadenopathy (category 1) and 119 (76.8%) had no mediastinal lymphadenopathy (category 2). Laboratory findings including median of white blood cells and lymphocyte percentage had no significant change in both categories. Intensive care unit admissions were 12 (33.3%) and 56 (47.1%) in category 1 and 2 respectively. Median days of hospitalization (8 days) and mortality rate (16%) were almost the same in both categories.Our study concludes that presence of mediastinal lymphadenopathy in severe COVID-19 cases is not associated with worse outcome. However, overall prevalence of mediastinal lymphadenopathy in severe cases is high (23.2%).

Osteopenia is regarded as the Bone Mineral Density (BMD) which is lower than that of the average value but not as low as Osteoporosis. In Pakistan, Osteoporosis and Osteopenia among women have become one of the most common problems of recent times. Objectives: To find the frequency of osteopenia among females in Faisalabad and its association with SES (Socio Economic Status). Methodology: It was an analytical and cross-sectional study which was conducted at Niaz Medicare Clinic in Faisalabad. The study was completed in 9 months from 18 October 2019 to 18 July 2020. Non probability purposive sampling was done and 323 females were taken for the study. Results: The results demonstrated that 56.3% of the population had Osteopenia. Socio Economic Status had as statistically significant association with Osteopenia (p= 0.041). The results also revealed that the females belonging to middle class and lower class had a higher prevalence of Osteopenia than the females of upper Socio Economic Status. Conclusion: the frequency of Osteopenia was fairly high among females specifically the age group 18-29 years. There was an association found between Osteopenia and Socio Economic status (p =0.041).

Multiparity is a matter of great concern regarding the health of women as well as child. At the same time it has different determinants and socioeconomic backgrounds. Objective: To determine the determinants of lack of family planning in grand multiparous women. Methods: It is a descriptive cross sectional study including 271 GMP patients from DHQ Hospital Okara city. Mean age was 35 ± 10 years. Data was collected with the help of questionnaire and evaluated and analyzed using SPSS version 24. Mean and standard deviation was calculated for quantitative data and frequency and percentages for qualitative data. To measure the determinants of lack of family planning, chi square test was used. P values less than and equal to 0.05 were taken as significant. Results: Most of women belong to rural area 172 (63.47%) and are Muslims 259 (95.5%). Most of the families lie under upper lower 149 (54.8%) and lower middle group of income 71 (26.1%). The reason of last pregnancy was desire for baby boy 66 (24.4%), then husband’s wish 45 (16.6%) and so on. There is a good association of socioeconomic status and residence of head of family as p < 0.05, (Results are 325.272a, df = 10, p=0 .000). There is an association of Gravida and socioeconomic status of head of family as p

Background and objective Hepatitis C infection is prevalent in Pakistan. The purpose of this study was to observe the therapeutic effects of conventional interferon in combination with ribavirin among treatment-naive hepatitis C patients. Methods This descriptive cross-sectional study of hepatitis C combination therapy was conducted at our institute after approval. All the patients received treatment with conventional interferon (5-MU three times weekly) and ribavirin (1000mg/day) for four weeks. A follow-up for the rapid virological response (RVR) was done in the fourth week of treatment. Results The mean age of the patients was 37.43. There was a gradual decrease in RVR with increasing age after four weeks of treatment. Conclusion The combination therapy showed good RVR in the fourth week among all hepatitis C patients.

The incomplete identification of structural variants (SVs) from whole-genome sequencing data limits studies of human genetic diversity and disease association. Here, we apply a suite of long-read, short-read, strand-specific sequencing technologies, optical mapping, and variant discovery algorithms to comprehensively analyze three trios to define the full spectrum of human genetic variation in a haplotype-resolved manner. We identify 818,054 indel variants (, Structural variants (SVs) in human genomes contribute diversity and diseases. Here, the authors use a multi-platform strategy to generate haplotype-resolved SVs for three human parent–child trios.

BackgroundThe 3-dimensional (3D) conformation of chromatin inside the nucleus is integral to a variety of nuclear processes including transcriptional regulation, DNA replication, and DNA damage repair. Aberrations in 3D chromatin conformation have been implicated in developmental abnormalities and cancer. Despite the importance of 3D chromatin conformation to cellular function and human health, little is known about how 3D chromatin conformation varies in the human population, or whether DNA sequence variation between individuals influences 3D chromatin conformation.ResultsTo address these questions, we perform Hi-C on lymphoblastoid cell lines from 20 individuals. We identify thousands of regions across the genome where 3D chromatin conformation varies between individuals and find that this variation is often accompanied by variation in gene expression, histone modifications, and transcription factor binding. Moreover, we find that DNA sequence variation influences several features of 3D chromatin conformation including loop strength, contact insulation, contact directionality, and density of local cis contacts. We map hundreds of quantitative trait loci associated with 3D chromatin features and find evidence that some of these same variants are associated at modest levels with other molecular phenotypes as well as complex disease risk.ConclusionOur results demonstrate that common DNA sequence variants can influence 3D chromatin conformation, pointing to a more pervasive role for 3D chromatin conformation in human phenotypic variation than previously recognized.

Genetic studies of autism spectrum disorder (ASD) have established that de novo duplications and deletions contribute to risk. However, ascertainment of structural variants (SVs) has been restricted by the coarse resolution of current approaches. By applying a custom pipeline for SV discovery, genotyping, and de novo assembly to genome sequencing of 235 subjects (71 affected individuals, 26 healthy siblings, and their parents), we compiled an atlas of 29,719 SV loci (5,213/genome), comprising 11 different classes. We found a high diversity of de novo mutations, the majority of which were undetectable by previous methods. In addition, we observed complex mutation clusters where combinations of de novo SVs, nucleotide substitutions, and indels occurred as a single event. We estimate a high rate of structural mutation in humans (20%) and propose that genetic risk for ASD is attributable to an elevated frequency of gene-disrupting de novo SVs, but not an elevated rate of genome rearrangement.

This descriptive study aimed at exploring the teaching practices of elementary science teachers and the support provided by the management of 80 Islamabad Model Schools, after introducing new textbooks based on science curriculum 2006. The curriculum enforces the dissemination of content through student centered and inquiry based teaching approach for conceptual development of the students. It is important because this approach facilitates students to understand the included themes of Science, Technology, Society and Environment in the textbooks and to make them able to integrate the scientific concepts in real life situations. In this respect, the data were taken from the schools’ heads and science teachers through questionnaires, while some heads were also interviewed about the actual application of the curriculum. They also suggested ways to remove the hurdles regarding the improvement of science education. The correlated results were presented by analyzing the effects of the school’s profile, the teacher’s profile and the support of management on the practices of the science curriculum. The positive values are seen in some of the practices, which reflected the potential of teachers in pedagogical skills, while management support was often seen in the teaching of science contents. For this, they believed in the participation of teachers, parents, students and other schools and more often facilitate teachers for concept building of the students, but rarely recommended teachers for trainings. In the interview, they commented that the science teaching practices may be improved by teacher training, timely delivery of textbooks, available resources for the induction of dropout students and controlling school security threats.

The incomplete identification of structural variants (SVs) from whole-genome sequencing data limits studies of human genetic diversity and disease association. Here, we apply a suite of long-read, short-read, and strand-specific sequencing technologies, optical mapping, and variant discovery algorithms to comprehensively analyze three human parent–child trios to define the full spectrum of human genetic variation in a haplotype-resolved manner. We identify 818,054 indel variants (

This paper considers the problem of learning the structure of gene regulatory networks from gene expression time series data. A more realistic scenario when the state space model representing a gene network evolves nonlinearly is considered while a linear model is assumed for the microarray data. To capture the nonlinearity, a particle filter-based state estimation algorithm is considered instead of the contemporary linear approximation-based approaches. The parameters characterizing the regulatory relations among various genes are estimated online using a Kalman filter. Since a particular gene interacts with a few other genes only, the parameter vector is expected to be sparse. The state estimates delivered by the particle filter and the observed microarray data are then subjected to a LASSO-based least squares regression operation which yields a parsimonious and efficient description of the regulatory network by setting the irrelevant coefficients to zero. The performance of the aforementioned algorithm is compared with the extended Kalman filter (EKF) and Unscented Kalman Filter (UKF) employing the Mean Square Error (MSE) as the fidelity criterion in recovering the parameters of gene regulatory networks from synthetic data and real biological data. Extensive computer simulations illustrate that the proposed particle filter-based network inference algorithm outperforms EKF and UKF, and therefore, it can serve as a natural framework for modeling gene regulatory networks with nonlinear and sparse structure.

Network component analysis (NCA) is an important method for inferring transcriptional regulatory networks (TRNs) and recovering transcription factor activities (TFAs) using gene expression data, and the prior information about the connectivity matrix. The algorithms currently available crucially depend on the completeness of this prior information. However, inaccuracies in the measurement process may render incompleteness in the available knowledge about the connectivity matrix. Hence, computationally efficient algorithms are needed to overcome the possible incompleteness in the available data. We present a sparse network component analysis algorithm (sparseNCA), which incorporates the effect of incompleteness in the estimation of TRNs by imposing an additional sparsity constraint using the $\ell _1$ norm, which results in a greater estimation accuracy. In order to improve the computational efficiency, an iterative re-weighted $\ell _2$ method is proposed for the NCA problem which not only promotes sparsity but is hundreds of times faster than the $\ell _1$ norm based solution. The performance of sparseNCA is rigorously compared to that of FastNCA and NINCA using synthetic data as well as real data. It is shown that sparseNCA outperforms the existing state-of-the-art algorithms both in terms of estimation accuracy and consistency with the added advantage of low computational complexity. The performance of sparseNCA compared to its predecessors is particularly pronounced in case of incomplete prior information about the sparsity of the network. Subnetwork analysis is performed on the E.coli data which reiterates the superior consistency of the proposed algorithm.

For the needs of the 21st century, the Government of Malaysia has conceptualized the Malaysia Education Blueprint 2013-2025 which embodies 11 strategic and operational shifts. In Shift 1, it is emphasized that the quality of Science, Technology, Engineering, and Mathematics (STEM) Education will be enhanced. This study employed the mixed-method approach using the "one-group pre-test and post-test design". Accordingly, this paper describes the pedagogical practice of Project-based Inquiry Learning (PIL) which promotes STEM Education among Year 1 students in the move to progress in tandem with Shift 1. Specifically, using the context of a magnet which has been stipulated in the Primary School Standard Curriculum, Year 1 students experienced the STEM Education through the STEM Pedagogy in which they raised questions upon the presentation of a relevant stimulus (Inquiry Phase), explored the ways in which a train carriage or coach could be assembled by means of recycled materials and magnets (Exploration Phase), designed a train carriage (Design Phase), and ultimately reflected on their inventions (Reflection Phase). The cognitive and affective impacts through the use of this Project-based Inquiry Learning are presented. Implications for the teaching and learning of science are discussed within the context of STEM Education.

Genetic studies of Autism Spectrum Disorder (ASD) have established that de novo duplications and deletions contribute to risk. However, ascertainment of structural variation (SV) has been restricted by the coarse resolution of current approaches. By applying a custom pipeline for SV discovery, genotyping and de novo assembly to genome sequencing of 235 subjects, 71 cases, 26 sibling controls and their parents, we present an atlas of 1.2 million SVs (5,213/genome), comprising 11 different classes. We demonstrate a high diversity of de novo mutations, a majority of which were undetectable by previous methods. In addition, we observe complex mutation clusters where combinations of de novo SVs, nucleotide substitutions and indels occurred as a single event. We estimate a high rate of structural mutation in humans (20%). Genetic risk for ASD is attributable to an elevated frequency of gene-disrupting de novo SVs but not an elevated rate of genome rearrangement.

Structural variants are implicated in numerous diseases and make up the majority of varying nucleotides among human genomes. Here we describe an integrated set of eight structural variant classes comprising both balanced and unbalanced variants, which we constructed using short-read DNA sequencing data and statistically phased onto haplotype blocks in 26 human populations. Analysing this set, we identify numerous gene-intersecting structural variants exhibiting population stratification and describe naturally occurring homozygous gene knockouts that suggest the dispensability of a variety of human genes. We demonstrate that structural variants are enriched on haplotypes identified by genome-wide association studies and exhibit enrichment for expression quantitative trait loci. Additionally, we uncover appreciable levels of structural variant complexity at different scales, including genic loci subject to clusters of repeated rearrangement and complex structural variants with multiple breakpoints likely to have formed through individual mutational events. Our catalogue will enhance future studies into structural variant demography, functional impact and disease association.

The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies., Wellcome Trust (London, England) (Core Award 090532/Z/09/Z), Wellcome Trust (London, England) (Senior Investigator Award 095552/Z/11/Z ), Wellcome Trust (London, England) (WT095908), Wellcome Trust (London, England) (WT109497), Wellcome Trust (London, England) (WT098051), Wellcome Trust (London, England) (WT086084/Z/08/Z), Wellcome Trust (London, England) (WT100956/Z/13/Z ), Wellcome Trust (London, England) (WT097307), Wellcome Trust (London, England) (WT0855322/Z/08/Z ), Wellcome Trust (London, England) (WT090770/Z/09/Z ), Wellcome Trust (London, England) (Major Overseas program in Vietnam grant 089276/Z.09/Z), Medical Research Council (Great Britain) (grant G0801823), Biotechnology and Biological Sciences Research Council (Great Britain) (grant BB/I02593X/1), Biotechnology and Biological Sciences Research Council (Great Britain) (grant BB/I021213/1), Zhongguo ke xue ji shu qing bao yan jiu suo. Office of 863 Programme of China (2012AA02A201), National Basic Research Program of China (2011CB809201), National Basic Research Program of China (2011CB809202), National Basic Research Program of China (2011CB809203), National Natural Science Foundation of China (31161130357), Shenzhen Municipal Government of China (grant ZYC201105170397A), Canadian Institutes of Health Research (grant 136855), Quebec Ministry of Economic Development, Innovation, and Exports (PSR-SIIRI-195), Germany. Bundesministerium für Bildung und Forschung (0315428A), Germany. Bundesministerium für Bildung und Forschung (01GS08201), Germany. Bundesministerium für Bildung und Forschung (BMBF-EPITREAT grant 0316190A), Deutsche Forschungsgemeinschaft (Emmy Noether Grant KO4037/1-1), Beatriu de Pinos Program (2006 BP-A 10144), Beatriu de Pinos Program (2009 BP-B 00274), Spanish National Institute for Health (grant PRB2 IPT13/0001-ISCIII-SGEFI/FEDER), Japan Society for the Promotion of Science (fellowship number PE13075), Marie Curie Actions Career Integration (grant 303772), Fonds National Suisse del la Recherche, SNSF, Scientifique (31003A_130342), National Center for Biotechnology Information (U.S.) (U54HG3067), National Center for Biotechnology Information (U.S.) (U54HG3273), National Center for Biotechnology Information (U.S.) (U01HG5211), National Center for Biotechnology Information (U.S.) (U54HG3079), National Center for Biotechnology Information (U.S.) (R01HG2898), National Center for Biotechnology Information (U.S.) (R01HG2385), National Center for Biotechnology Information (U.S.) (RC2HG5552), National Center for Biotechnology Information (U.S.) (U01HG6513), National Center for Biotechnology Information (U.S.) (U01HG5214), National Center for Biotechnology Information (U.S.) (U01HG5715), National Center for Biotechnology Information (U.S.) (U01HG5718), National Center for Biotechnology Information (U.S.) (U01HG5728), National Center for Biotechnology Information (U.S.) (U41HG7635), National Center for Biotechnology Information (U.S.) (U41HG7497), National Center for Biotechnology Information (U.S.) (R01HG4960), National Center for Biotechnology Information (U.S.) (R01HG5701), National Center for Biotechnology Information (U.S.) (R01HG5214), National Center for Biotechnology Information (U.S.) (R01HG6855), National Center for Biotechnology Information (U.S.) (R01HG7068), National Center for Biotechnology Information (U.S.) (R01HG7644), National Center for Biotechnology Information (U.S.) (DP2OD6514), National Center for Biotechnology Information (U.S.) (DP5OD9154), National Center for Biotechnology Information (U.S.) (R01CA166661), National Center for Biotechnology Information (U.S.) (R01CA172652), National Center for Biotechnology Information (U.S.) (P01GM99568), National Center for Biotechnology Information (U.S.) (R01GM59290), National Center for Biotechnology Information (U.S.) (R01GM104390), National Center for Biotechnology Information (U.S.) (T32GM7790), National Center for Biotechnology Information (U.S.) (R01HL87699), National Center for Biotechnology Information (U.S.) (R01HL104608), National Center for Biotechnology Information (U.S.) (T32HL94284), National Center for Biotechnology Information (U.S.) (HHSN268201100040C), National Center for Biotechnology Information (U.S.) (HHSN272201000025C), Lundbeck Foundation (grant R170-2014-1039, Simons Foundation (SFARI award SF51), National Science Foundation (U.S.) (Research Fellowship DGE-1147470)

Non-iterative network component analysis (NINCA), proposed by Jacklin at.al, employs convex optimization methods to estimate the transcription factor control strengths and transcription factor activities. While NINCA provides good estimation accuracy and higher consistency, the costly optimization routine used therein renders a high computational complexity. This correspondence presents a closed form solution to estimate the connectivity matrix which is tens of times faster, and provides similar accuracy and consistency, thus making the closed form NINCA (CFNINCA) algorithm useful for large data sets encountered in practice. The proposed solution is assessed for accuracy and consistency using synthetic and yeast cell cycle data sets by comparing with the existing state-of-the-art algorithms. The robustness of the algorithm to the possible inaccuracies in prior information is also analyzed and it is observed that CFNINCA and NINCA are much more robust to erroneous prior information as compared to FastNCA.

Motivation: Network component analysis (NCA) is an efficient method of reconstructing the transcription factor activity (TFA), which makes use of the gene expression data and prior information available about transcription factor (TF)–gene regulations. Most of the contemporary algorithms either exhibit the drawback of inconsistency and poor reliability, or suffer from prohibitive computational complexity. In addition, the existing algorithms do not possess the ability to counteract the presence of outliers in the microarray data. Hence, robust and computationally efficient algorithms are needed to enable practical applications. Results: We propose ROBust Network Component Analysis (ROBNCA), a novel iterative algorithm that explicitly models the possible outliers in the microarray data. An attractive feature of the ROBNCA algorithm is the derivation of a closed form solution for estimating the connectivity matrix, which was not available in prior contributions. The ROBNCA algorithm is compared with FastNCA and the non-iterative NCA (NI-NCA). ROBNCA estimates the TF activity profiles as well as the TF–gene control strength matrix with a much higher degree of accuracy than FastNCA and NI-NCA, irrespective of varying noise, correlation and/or amount of outliers in case of synthetic data. The ROBNCA algorithm is also tested on Saccharomyces cerevisiae data and Escherichia coli data, and it is observed to outperform the existing algorithms. The run time of the ROBNCA algorithm is comparable with that of FastNCA, and is hundreds of times faster than NI-NCA. Availability: The ROBNCA software is available at http://people.tamu.edu/∼amina/ROBNCA Contact: serpedin@ece.tamu.edu Supplementary information: Supplementary data are available at Bioinformatics online.

The large influx of data from high-throughput genomic and proteomic technologies has encouraged the researchers to seek approaches for understanding the structure of gene regulatory networks and proteomic networks. This work reviews some of the most important statistical methods used for modeling of gene regulatory networks (GRNs) and protein-protein interaction (PPI) networks. The paper focuses on the recent advances in the statistical graphical modeling techniques, state-space representation models, and information theoretic methods that were proposed for inferring the topology of GRNs. It appears that the problem of inferring the structure of PPI networks is quite different from that of GRNs. Clustering and probabilistic graphical modeling techniques are of prime importance in the statistical inference of PPI networks, and some of the recent approaches using these techniques are also reviewed in this paper. Performance evaluation criteria for the approaches used for modeling GRNs and PPI networks are also discussed.

This paper proposes a novel algorithm for inferring gene regulatory networks which makes use of cubature Kalman filter (CKF) and Kalman filter (KF) techniques in conjunction with compressed sensing methods. The gene network is described using a state-space model. A nonlinear model for the evolution of gene expression is considered, while the gene expression data is assumed to follow a linear Gaussian model. The hidden states are estimated using CKF. The system parameters are modeled as a Gauss-Markov process and are estimated using compressed sensing-based KF. These parameters provide insight into the regulatory relations among the genes. The Cramér-Rao lower bound of the parameter estimates is calculated for the system model and used as a benchmark to assess the estimation accuracy. The proposed algorithm is evaluated rigorously using synthetic data in different scenarios which include different number of genes and varying number of sample points. In addition, the algorithm is tested on the DREAM4 in silico data sets as well as the in vivo data sets from IRMA network. The proposed algorithm shows superior performance in terms of accuracy, robustness, and scalability.

This paper reviews the information theoretic methods used for inferring gene regulatory networks. Mutual information has been widely used as a dependency measure to estimate the undirected interactions between genes using steady state data. However, employing time-series data results in a directed graph. Since two genes may be interacting with each other via an intermediate gene, their mutual information may show a direct dependency. To resolve this issue, data processing inequality and conditional mutual information have been employed. Mutual information, being a symmetric measure, is unable to predict directed edges using the steady-state data alone, while algorithms using time-series data can be computationally complex as more data is involved. Therefore, non-symmetric measures such as φ mixing coefficients have recently been proposed in the literature. The algorithms using these techniques are also discussed in this article. Estimation of information-theoretic metrics is explained which is a core component of all the methods. Performance metrics that are frequently used to test the robustness and accuracy of the algorithms are also described and some avenues of future research are proposed.

This paper considers the problem of inferring gene regulatory networks using time series data. A nonlinear model is assumed for the gene expression profiles, whereas the microarray data follows a linear Gaussian model. A particle filter based approach is proposed to estimate the gene expression profiles and the parameters are estimated online using Kalman filter. In order to capture the inherent sparsity of the gene networks, LASSO based least square optimization is performed. The performance of the proposed algorithm is compared with the extended Kalman filter (EKF) algorithm using Mean Square Error (MSE) as the fidelity criterion. The simulations are performed using the synthetic as well as real data and the proposed algorithm is observed to outperform the EKF in the scenarios considered.

Background and Objectives: Gene regulatory networks model the interactions among the genes and provide a decision rule describing activation and repression of each gene via various proteins. In order to be able to capture the complex gene interactions efficiently, it is imperative to develop algorithms that model the nonlinear interactions among the genes. This work considers the problem of inferring gene regulatory networks using time series data. A nonlinear model is assumed for the gene expression profiles, whereas the microarray data follows a linear Gaussian model. Methods: A particle filter based approach is proposed to estimate the gene expression profiles and the parameters are estimated online using the Kalman filter. In order to capture the inherent sparsity of gene networks, a least squares shrinkage selection operator (LASSO) based regression and model selection algorithm is proposed. Results: The performance of the aforementioned algorithm is rigorously evaluated for synthetic as well as real biological data sets arising from Drosophila melanogaster time series gene expression profiles. The results are contrasted with those reported in the literature. The performance of the proposed algorithm is compared with the extended Kalman filter (EKF) algorithm using mean square error (MSE) as the fidelity criterion. The proposed algorithm is observed to outperform the EKF in the scenarios considered. Conclusions: This work considered the problem of modeling and learning of gene regulatory networks using a nonlinear dynamical model. This represents a quite general modeling set-up. The gene network is modeled using a state space approach, and particle filtering is used for state estimation. The parameters regulating the interaction among genes are supplied by an online Kalman filter. Since the parameter vector is sparse, LASSO identifies the subset of these parameters pertaining to the relevant system coefficients. Extensive performance evaluations demonstrate that the proposed particle filter based approach outperforms EKF in terms of MSE.

Background & Objectives: Gene regulatory network inference is a problem of prime importance in the budding field of systems biology and bioinformatics. The process which uses the information present in the genes to produce functional gene products is termed gene expression, and it can be measured using the microarray technology. The scope of this work is to propose a series of information theoretic algorithms which utilize the gene expression data to reverse engineer gene regulatory networks. Methods: Using information theoretic tools, this work proposes several algorithms to indicate how the genes are interacting with each other in a biological system. One of the advantages associated with these information theoretic algorithms is that they work with minimum assumptions. The concept of mutual information is a fundamental quantity in information theory that is used extensively in this work to measure the degree of dependency between genes. There are situations when two genes are interacting but only via an intermediate gene. By employing data processing inequality and condition mutual information, this work shows the possibility to identify such indirect interactions. Results: The computer simulations illustrate that the proposed information theoretic methods provide some of the most robust algorithms for inferring the topology of gene regulatory networks. While estimating the genetic interactions, it is necessary to put a constraint on the complexity of the network so as to avoid over-fitting the network. To this end, minimum description length principle and Bayesian information criterion are used for model selection. Results performed on both synthetic and realistic gene regulatory networks attest the superior performance of the proposed algorithms. Conclusions: This work describes different information theoretic approaches for determining the possible interactions between genes using gene expression data. The performance metrics used to assess these algorithms are also discussed. It is concluded that information theoretic methods represent powerful tools for inference of gene regulatory networks.

A novel technique for the inference of gene regulatory networks is proposed which utilizes cubature Kalman filter (CKF). The gene network is modeled using the state-space approach. A non-linear model for the evolution of gene expression is considered and the microarray data is assumed to follow a linear Gaussian model. CKF is used to estimate the hidden states as well as the unknown static parameters of the model. These parameters provide an insight into the regulatory relations among the genes. The proposed algorithm delievers superior performance than the linearization based extended Kalman filter (EKF) for synthetic as well as real world biological data.