33 research outputs found
Impact of Socio-Cultural Disharmony on the Educational System
No full textThe paper focuses on the major problems of the young generation in respect of their profession, education and personal life, relationship, creativity etc. Authors mentioned that if a man or woman has been satisfied by his or his activities then they become prosperous in their life. Students at the very early stage of education should be guided by moral and ethical education. Only moral and ethical values can save the life of distressed people. Socio-cultural disaster leads to unsatisfied generation. It also produces uneven circumstances in the land we live in. It may not be possible to live a happy fruitful life in the space of socio-cultural disaster. It has been concluded that only a proper education system aiming at character building and skill development can be the measure to defend against socio-cultural disasters. Self-respect and freedom of thinking are very much essential for proper education
FuzzyPPI: Human Proteome at Fuzzy Semantic Space
No full textLarge scale protein-protein interaction (PPI) network of an organism provides key insights into the cellular and molecular functionalities, signaling pathways and underlying disease mechanisms. If we consider the complete interactome of any given organism, the total number of unexplored protein interactions significantly outnumbers the known positive and negative interactions. For Human 20,350 reviewed proteins can generate over ~207 million potential interactions. However, the combination of all known PPI datasets, contains only ~5.6 million positive and ~758k negative protein-protein interactions (NPPI), that together is ~3.1% what is more, conventional PPI prediction methods produce binary results. At the same time recent studies show that protein binding affinities may prove to be effective in detecting protein complexes, disease association analysis, signaling network reconstruction, etc. In this work we present a fuzzy semantic scoring function using the Gene Ontology (GO) graphs to assess the binding affinity between any two proteins at an organism level. We have implemented a distributed algorithm in Apache Spark that computes this function and processed the complete Human PPI network of ~182 million potential interactions resulting from 19,106 reviewed proteins for which GO annotations are available. The quality of the computed scores has been validated with respect to the available state-of-the-art methods on benchmark data sets
Electrical conductivity and current-voltage hysteresis behavior of BiFeO3 doped PVA nanocomposite film
No full textRUBic: rapid unsupervised biclustering
No full textBiclustering of biologically meaningful binary information is essential in many applications related to drug discovery, like protein-protein interactions and gene expressions. However, for robust performance in recently emerging large health datasets, it is important for new biclustering algorithms to be scalable and fast. We present a rapid unsupervised biclustering (RUBic) algorithm that achieves this objective with a novel encoding and search strategy. RUBic significantly reduces the computational overhead on both synthetic and experimental datasets shows significant computational benefits, with respect to several state-of-the-art biclustering algorithms. In 100 synthetic binary datasets, our method took ~71.1s to extract 494,872 biclusters. In the human PPI database of size 4085x4085, our method generates 1840 biclusters in ~48.6s. On a central nervous system embryonic tumor gene expression dataset of size 712,940, our algorithm takes 101 min to produce 747,069 biclusters, while the recent competing algorithms take significantly more time to produce the same result. RUBic is also evaluated on five different gene expression datasets and shows significant speed-up in execution time with respect to existing approaches to extract significant KEGG-enriched bi-clustering. RUBic can operate on two modes, base and flex, where base mode generates maximal biclusters and flex mode generates less number of clusters and faster based on their biological significance with respect to KEGG pathways. The code is available at ( https://github.com/CMATERJU-BIOINFO/RUBic ) for academic use only
A survey on Ebola genome and current trends in computational research on the Ebola virus
No full textJournal of Informetrics: A Bibliometric Profile
No full textThis paper critically analyses 239 scholarly communications published in the inaugural five volumes of Journal of Informetrics (JOI) to examine growth of literature, types of communications, authorship pattern, collaboration trend, predominant research domains, etc. Subsequent analysis focuses on prolific contributors, degree of collaboration, and time-lag trend. Findings reveal that - publication output doubles over the study period as article publications increase considerably; though single-authored
contributions were significant (30 %), majority of contributions were collaborated by two-authors (36 %),
while average authorship accounts for 2.28 per communications. Degree of collaboration (DC) was
impressive (0.699) but not overwhelming as research collaborations has emanated from 199 higher learning institutions of 32 countries across the globe. Ranking of prolific contributors has shown Prof. Egghe on the top followed by L Bornmann; R Rousseau and L Leydesdoff. Result also shows upward trend of keyword usage with an average of 4.55 per items, of which h-index, citation analysis,
bibliometrics, g-index, etc, expectedly predominates. Scholarly nature of source journal has been further ascertained from increasing citations and reference usage trend. Moreover, growing hardness of the field has been attributed to JOI due to the increasing usage of tables and figures. Study also showed that the journal takes an average of about four month time to publish a manuscrip
ML-DTD: Machine Learning-Based Drug Target Discovery for the Potential Treatment of COVID-19
No full textRecent research has highlighted that a large section of druggable protein targets in the Human interactome remains unexplored for various diseases. It might lead to the drug repurposing study and help in the in-silico prediction of new drug-human protein target interactions. The same applies to the current pandemic of COVID-19 disease in global health issues. It is highly desirable to identify potential human drug targets for COVID-19 using a machine learning approach since it saves time and labor compared to traditional experimental methods. Structure-based drug discovery where druggability is determined by molecular docking is only appropriate for the protein whose three-dimensional structures are available. With machine learning algorithms, differentiating relevant features for predicting targets and non-targets can be used for the proteins whose 3-D structures are unavailable. In this research, a Machine Learning-based Drug Target Discovery (ML-DTD) approach is proposed where a machine learning model is initially built up and tested on the curated dataset consisting of COVID-19 human drug targets and non-targets formed by using the Therapeutic Target Database (TTD) and human interactome using several classifiers like XGBBoost Classifier, AdaBoost Classifier, Logistic Regression, Support Vector Classification, Decision Tree Classifier, Random Forest Classifier, Naive Bayes Classifier, and K-Nearest Neighbour Classifier (KNN). In this method, protein features include Gene Set Enrichment Analysis (GSEA) ranking, properties derived from the protein sequence, and encoded protein network centrality-based measures. Among all these, XGBBoost, KNN, and Random Forest models are satisfactory and consistent. This model is further used to predict novel COVID-19 human drug targets, which are further validated by target pathway analysis, the emergence of allied repurposed drugs, and their subsequent docking study
Is Fostamatinib a possible drug for COVID-19? – A computational study
No full textCOVID-19 has turned out to be a global pandemic within a very short period since its first origin in China in December
2019. With the gradual increase in the mortality rate all over the world, there is an urgent need for an effectual drug. Though no clinically approved vaccine or drug is available until now but scientists are trying hard to identify potential antivirals to this new coronavirus. Several drugs like hydroxychloroquine, remdesivir, azithromycin etc. are put under evaluation in more than 300 clinical trials for the treatment of COVID-19. Few of them already show encouraging results. The main agent of disease progression of COVID-19 is SARS-CoV2/nCoV, which is believed to have ~89% genetic resemblance with SARSCoV, a coronavirus responsible for the massive outbreak in 2003. With this hypothesis, a recently developed in silico Human-nCoV network and potential COVID-19 spreader proteins, have been derived from the Human-SARS-CoV protein interactions using SIS model and fuzzy thresholding, followed by a potential FDA drugs target based validation. We then perform a two-way analysis to identify the potential drug targets of COVID-19. In the first analysis, we identify the complete list of FDA drugs for the 37 level 1 and 4948 level 2 spreader proteins in this network followed by the application of a consensus strategy. In the second analysis, the same consensus strategy is applied but on a curated overlapping set of key genes identified from COVID-19 symptoms, risk factors and clinical outcome. The applied consensus strategy in both the analysis reveals that Fostamatinib, a FDA approved drug, has the highest drug consensus score both in level 1 and level 2. Further analysis reveals that Fostamatinib also targets CYP3A4, a level 2 spreader protein and the most common target for
most of the potential COVID-19 drugs. A subsequent docking study also reveals that Fostamatinib has also the highest docking score with respect to 6LU7, the crystal structure of COVID-19 main protease in complex with an inhibitor N3, in comparison to other potential drugs like hydroxychloroquine, remdesivir, favipiravir and darunavir. Our computational study suggests that Fostamatinib may also be considered as one of the potential candidates for further clinical trials in pursuit to counter the spread of COVID-19
Assessment of GO-Based Protein Interaction Affinities in the Large-Scale Human–Coronavirus Family Interactome
No full textSARS-CoV-2 is a novel coronavirus that replicates itself via interacting with the host proteins. As a result, identifying virus and host protein-protein interactions could help researchers better understand the virus disease transmission behavior and identify possible COVID-19 drugs. The International Committee on Virus Taxonomy has determined that nCoV is genetically 89% compared to the SARS-CoV epidemic in 2003. This paper focuses on assessing the host–pathogen protein interaction affinity of the coronavirus family, having 44 different variants. In light of these considerations, a GO-semantic scoring function is provided based on Gene Ontology (GO) graphs for determining the binding affinity of any two proteins at the organism level. Based on the availability of the GO annotation of the proteins, 11 viral variants, viz., SARS-CoV-2, SARS, MERS, Bat coronavirus HKU3, Bat coronavirus Rp3/2004, Bat coronavirus HKU5, Murine coronavirus, Bovine coronavirus, Rat coronavirus, Bat coronavirus HKU4, Bat coronavirus 133/2005, are considered from 44 viral variants. The fuzzy scoring function of the entire host–pathogen network has been processed with ~180 million potential interactions generated from 19,281 host proteins and around 242 viral proteins. ~4.5 million potential level one host–pathogen interactions are computed based on the estimated interaction affinity threshold. The resulting host–pathogen interactome is also validated with state-of-the-art experimental networks. The study has also been extended further toward the drug-repurposing study by analyzing the FDA-listed COVID drugs
