Discovery Techniques Research Articles

Meso-scale Discovery Assay Detects the Changes of Plasma Cytokine Levels in Mice after Low or High LET Ionizing Irradiation

ObjectiveTo obtain precise data on the changes in the levels of 29 cytokines in mice after high or low linear energy transfer (LET) irradiation and to develop an accurate model of radiation exposure based on the cytokine levels after irradiation. MethodsPlasma samples harvested from mice at different time points after carbon-ion or X-ray irradiation were analyzed using meso-scale discovery (MSD), a high-throughput and sensitive electrochemiluminescence measurement technique. Dose estimation equations were set up using multiple linear regression analysis. ResultsThe relative levels of IL-6 at 1 h, IL-5 and IL-6 at 24 h, and IL-5, IL-6 and IL-15 at 7 d after irradiation with two intensities increased dose-dependently. The minimum measured levels of IL-5, IL-6 and IL-15 were up to 4.0076 pg/mL, 16.4538 pg/mL and 0.4150 pg/mL, respectively. In addition, dose estimation models were established and verified. ConclusionsThe MSD assay can provide more accurate data regarding the changes in the levels of the cytokines IL-5, IL-6 and IL-15. These cytokines could meet the essential criteria for radiosensitive biomarkers and can be used as radiation indicators. Our prediction models can conveniently and accurately estimate the exposure dose in irradiated organism.

Metadata based Classification Techniques for Knowledge Discovery from Facebook Multimedia Database

Classification is a parlance of Data Mining to genre data of different kinds in particular classes. As we observe, social media is an immense manifesto that allows billions of people share their thoughts, updates and multimedia information as status, photo, video, link, audio and graphics. Because of this flexibility cloud has enormous data. Most of the times, this data is much complicated to retrieve and to understand. And the data may contain lot of noise and at most the data will be incomplete. To make this complication easier, the data existed on the cloud has to be classified with labels which is viable through data mining Classification techniques. In the present work, we have considered Facebook dataset which holds meta data of cosmetic company’s Facebook page. 19 different Meta Data are used as main attributes. Out of those, Meta Data ‘Type’ is concentrated for Classification. Meta data ‘Type’ is classified into four different classes such as link, status, photo and video. We have used two favored Classifiers of Data Mining that are, Bayes Classifier and Decision Tree Classifier. Data Mining Classifiers contain several classification algorithms. Few algorithms from Bayes and Decision Tree have been chosen for the experiment and explained in detail in the present work. Percentage split method is used to split the dataset as training and testing data which helps in calculating the Accuracy level of Classification and to form confusion matrix. The Accuracy results, kappa statistics, root mean squared error, relative absolute error, root relative squared error and confusion matrix of all the algorithms are compared, studied and analyzed in depth to produce the best Classifier which can label the company’s Facebook data into appropriate classes thus Knowledge Discovery is the ultimate goal of this experiment.

Neuroinflammation and Alzheimer's Disease: A Machine Learning Approach to CSF Proteomics.

In Alzheimer’s disease (AD), the contribution of pathophysiological mechanisms other than amyloidosis and tauopathy is now widely recognized, although not clearly quantifiable by means of fluid biomarkers. We aimed to identify quantifiable protein biomarkers reflecting neuroinflammation in AD using multiplex proximity extension assay (PEA) testing. Cerebrospinal fluid (CSF) samples from patients with mild cognitive impairment due to AD (AD-MCI) and from controls, i.e., patients with other neurological diseases (OND), were analyzed with the Olink Inflammation PEA biomarker panel. A machine-learning approach was then used to identify biomarkers discriminating AD-MCI (n: 34) from OND (n: 25). On univariate analysis, SIRT2, HGF, MMP-10, and CXCL5 showed high discriminatory performance (AUC 0.809, p = 5.2 × 10−4, AUC 0.802, p = 6.4 × 10−4, AUC 0.793, p = 3.2 × 10−3, AUC 0.761, p = 2.3 × 10−3, respectively), with higher CSF levels in AD-MCI patients as compared to controls. These same proteins were the best contributors to the penalized logistic regression model discriminating AD-MCI from controls (AUC of the model 0.906, p = 2.97 × 10−7). The biological processes regulated by these proteins include astrocyte and microglia activation, amyloid, and tau misfolding modulation, and blood-brain barrier dysfunction. Using a high-throughput multiplex CSF analysis coupled with a machine-learning statistical approach, we identified novel biomarkers reflecting neuroinflammation in AD. Studies confirming these results by means of different assays are needed to validate PEA as a multiplex technique for CSF analysis and biomarker discovery in the field of neurological diseases.

MapReduce-Based Crow Search-Adopted Partitional Clustering Algorithms for Handling Large-Scale Data

Cluster analysis is the prominent data mining technique in knowledge discovery and it discovers the hidden patterns from the data. The K-Means, K-Modes and K-Prototypes are partition based clustering algorithms and these algorithms select the initial centroids randomly. Because of its random selection of initial centroids, these algorithms provide the local optima in solutions. To solve these issues, the strategy of Crow Search algorithm is employed with these algorithms to obtain the global optimum solution. With the advances in information technology, the size of data increased in a drastic manner from terabytes to petabytes. To make proposed algorithms suitable to handle these voluminous data, the phenomena of parallel implementation of these clustering algorithms with Hadoop Mapreduce framework. The proposed algorithms are experimented with large scale data and the results are compared in terms of cluster evaluation measures and computation time with the number of nodes.

An Integrated Approach for Discovering Process Models According to Business Process Types

Process discovery technique aims at automatically generating a process model that accurately describes a Business Process (BP) based on event data. Related discovery algorithms consider recorded events are only resulting from an operational BP type. While the management community defines three BP types, which are: Management, Support and Operational. They distinguish each BP type by different proprieties like the main business process objective as domain knowledge. This puts forward the lack of process discovery technique in obtaining process models according to business process types (Management and Support). In this paper, we demonstrate that business process types can guide the process discovery technique in generating process models. A special interest is given to the use of process mining to deal with this challenge.

Deep Learning Approach for Discovery of In Silico Drugs for Combating COVID-19.

Early diagnosis of pandemic diseases such as COVID-19 can prove beneficial in dealing with difficult situations and helping radiologists and other experts manage staffing more effectively. The application of deep learning techniques for genetics, microscopy, and drug discovery has created a global impact. It can enhance and speed up the process of medical research and development of vaccines, which is required for pandemics such as COVID-19. However, current drugs such as remdesivir and clinical trials of other chemical compounds have not shown many impressive results. Therefore, it can take more time to provide effective treatment or drugs. In this paper, a deep learning approach based on logistic regression, SVM, Random Forest, and QSAR modeling is suggested. QSAR modeling is done to find the drug targets with protein interaction along with the calculation of binding affinities. Then deep learning models were used for training the molecular descriptor dataset for the robust discovery of drugs and feature extraction for combating COVID-19. Results have shown more significant binding affinities (greater than −18) for many molecules that can be used to block the multiplication of SARS-CoV-2, responsible for COVID-19.

Driver Behavior-aware Parking Availability Crowdsensing System Using Truth Discovery

Spot-level parking availability information (the availability of each spot in a parking lot) is in great demand, as it can help reduce time and energy waste while searching for a parking spot. In this article, we propose a crowdsensing system called SpotE that can provide spot-level availability in a parking lot using drivers’ smartphone sensors. SpotE only requires the sensor data from drivers’ smartphones, which avoids the high cost of installing additional sensors and enables large-scale outdoor deployment. We propose a new model that can use the parking search trajectory and final destination (e.g., an exit of the parking lot) of a single driver in a parking lot to generate the probability profile that contains the probability of each spot being occupied in a parking lot. To deal with conflicting estimation results generated from different drivers, due to the variance in different drivers’ parking behaviors, a novel aggregation approach SpotE-TD is proposed. The proposed aggregation method is based on truth discovery techniques and can handle the variety in Quality of Information of different vehicles. We evaluate our proposed method through a real-life deployment study. Results show that SpotE-TD can efficiently provide spot-level parking availability information with a 20% higher accuracy than the state-of-the-art.

High-performance thin layer chromatography: A powerful analytical technique in pharmaceutical drug discovery

HPTLC is an advanced type of planar chromatography used extensively in the recent years for fingerprinting of medicinal plants, products and for screening lichen substances, quantification of active ingredients and herbal drugs, phytochemical and biomedical analysis and also used to check of presence of adulterants in the formulation. It is more sensitive and possible to run more sample in a short period of time, by using small amount of solvent. It is one of the sophisticated instrumental techniques based on the full capabilities of thin layer chromatography. In this paper, a brief discussion of the type of instrument used in HPTLC, its entire approach, and how this procedure is superior to TLC is presented. This article attempts to concentrate on the use of HPTLC by including examples of medications, medicinal drugs, and formulations that have been analyzed using HPTLC.

Towards a Better Urban Design in Respect to Environmental Behavior Using Knowledge Discovery

Urbanization growth is creeping, and the number of cities will increase each day more than before. Predictions say that approximately 70% of the world population will live in urban areas by 2050. Hence, new approaches must be considered while designing/upgrading/enhancing urban places. This article describes a new theoretical model to enhance the urban design process through Knowledge Discovery techniques based on Artificial Intelligence, data mining, machine learning, 5G, and Micro-World. The research findings enable assessing the whole process of Urban Design phases by proposing new techniques using the Knowledge Discovery by combining the traditional research methods used frequently in the Urban Design Process with the Knowledge Discovery Techniques. Through reviewing and analyzing the urban design and its relation to urban behavior, the authors deduct the correlations between the urban design process and the knowledge discovery techniques that can build solutions based on objective output, not only the traditional subjective ones. The new research results explain the exact application relevant to each step in the urban design process. Also, the research will supplement and improve urban studies to understand the profound relationship between Urban Environmental Psychology and Artificial Intelligence. It can serve the urban designers and planners by using the knowledge discovery to enhance urban practice by providing a more comprehensive range of solutions.

Synchronized Tissue Acquisition Techniques for Novel Biomarker Discovery: Are You Ready to Waltz?

Synchronized Tissue Acquisition Techniques for Novel Biomarker Discovery: Are You Ready to Waltz?

SINDy-BVP: Sparse identification of nonlinear dynamics for boundary value problems

This article develops a data-driven model discovery and system identification technique for spatially-dependent boundary value problems to infer both the operator and spatially-dependent parameters that describe the heterogenous, physical quantities of a system.

Lung adenocarcinoma and lung squamous cell carcinoma cancer classification, biomarker identification, and gene expression analysis using overlapping feature selection methods

Lung cancer is one of the deadliest cancers in the world. Two of the most common subtypes, lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), have drastically different biological signatures, yet they are often treated similarly and classified together as non-small cell lung cancer (NSCLC). LUAD and LUSC biomarkers are scarce, and their distinct biological mechanisms have yet to be elucidated. To detect biologically relevant markers, many studies have attempted to improve traditional machine learning algorithms or develop novel algorithms for biomarker discovery. However, few have used overlapping machine learning or feature selection methods for cancer classification, biomarker identification, or gene expression analysis. This study proposes to use overlapping traditional feature selection or feature reduction techniques for cancer classification and biomarker discovery. The genes selected by the overlapping method were then verified using random forest. The classification statistics of the overlapping method were compared to those of the traditional feature selection methods. The identified biomarkers were validated in an external dataset using AUC and ROC analysis. Gene expression analysis was then performed to further investigate biological differences between LUAD and LUSC. Overall, our method achieved classification results comparable to, if not better than, the traditional algorithms. It also identified multiple known biomarkers, and five potentially novel biomarkers with high discriminating values between LUAD and LUSC. Many of the biomarkers also exhibit significant prognostic potential, particularly in LUAD. Our study also unraveled distinct biological pathways between LUAD and LUSC.

Development of Therapeutic Antibodies and Modulating the Characteristics of Therapeutic Antibodies to Maximize the Therapeutic Efficacy.

Monoclonal antibodies (mAb) have been used as therapeutic agents for various diseases, and immunoglobulin G (IgG) is mainly used among antibody isotypes due to its structural and functional properties. So far, regardless of the purpose of the therapeutic antibody, wildtype IgG has been mainly used, but recently, the engineered antibodies with various strategies according to the role of the therapeutic antibody have been used to maximize the therapeutic efficacy. In this review paper, first, the overall structural features and functional characteristics of antibody IgG, second, the old and new techniques for antibody discovery, and finally, several antibody engineering strategies for maximizing therapeutic efficacy according to the role of a therapeutic antibody will be introduced.

Digging in Documents: Using Text Mining to Reveal Hidden Knowledge in the Lithium-Ion Battery Literature

Scientific publications constitute the main information source of battery-related data for academic research. There is an impressive number of papers published on Lithium Ion Batteries (LIBs) for instance. Such impressive amount of data in principle could be used by machine learning (ML) methods in order to optimize efforts and even make possible the discovery of new optimal experimental conditions. Yet, the majority is published as unstructured data, making ML analysis difficult. Here, in order to unravel researchers’ habits when reporting their results, we present a text mining study using primarily publications containing original experimental data from the LIB field. We analyze the reported data structure and we discuss the remaining challenges for applying automated knowledge discovery techniques. We then present a Named Entity Recognition approach to automatically extract quantified parameters from the battery literature and how to build an in house generated database containing a wide diversity of materials, experimental parameters and conditions. Our in house approach achieves a data extraction F-scores ≥ 90% and can thereby pave the way towards an automatized way of generating large databases for use in battery ML models. References El-Bouysidy, H., Lombardo, T., Primo, E. N., Duquesnoy, M., Morcrette, M., Johansson, P., Simon, P., Grimaud, A., & Franco, A. A. submitted (2020).Torayev, Amangeldi, Pieter CMM Magusin, Clare P. Grey, Céline Merlet, and Alejandro A. Franco. Journal of Physics: Materials 2, no. 4 (2019): 044004.

Diverse functional autoantibodies in patients with COVID-19.

COVID-19 manifests with a wide spectrum of clinical phenotypes that are characterized by exaggerated and misdirected host immune responses1-6. Although pathological innate immune activation is well-documented in severe disease1, the effect of autoantibodies on disease progression is less well-defined. Here we use a high-throughput autoantibody discovery technique known as rapid extracellular antigen profiling7 to screen a cohort of 194 individuals infected with SARS-CoV-2, comprising 172patients with COVID-19 and 22healthcare workers with mild disease or asymptomatic infection, for autoantibodies against 2,770extracellular and secreted proteins (members of the exoproteome). We found that patients with COVID-19 exhibit marked increases in autoantibody reactivities as compared to uninfected individuals, and show a high prevalence of autoantibodies against immunomodulatory proteins (including cytokines, chemokines, complement components and cell-surface proteins). We established that these autoantibodies perturb immune function and impair virological control by inhibiting immunoreceptor signalling and by altering peripheral immune cell composition, and found that mouse surrogates of these autoantibodies increase disease severity in a mouse model of SARS-CoV-2 infection. Our analysis of autoantibodies against tissue-associated antigens revealed associations with specific clinical characteristics. Our findings suggest a pathological role for exoproteome-directed autoantibodies in COVID-19, with diverse effects on immune functionality and associations with clinical outcomes.

The Log Skeleton Visualizer in ProM 6.9

Process discovery is an important area in the field of process mining. To help advance this area, a process discovery contest (PDC) has been set up, which allows us to compare different approaches. At the moment of writing, there have been three instances of the PDC: in 2016, in 2017, and in 2019. This paper introduces the winning contribution to the PDC 2019, called the Log Skeleton Visualizer. This visualizer uses a novel type of process models called log skeletons. In contrast with many workflow net-based discovery techniques, these log skeletons do not rely on the directly follows relation. As a result, log skeletons offer circumstantial information on the event log at hand rather than only sequential information. Using this visualizer, we were able to classify 898 out of 900 traces correctly for the PDC 2019 and to win this contest.

Nanoemulgel- A revolutionary approach for local gel oriented formulation

In previous years, incredible efforts have been made for different techniques of drug discovery, a variety of drug molecules available today have contributed to the resolution of the numerous health care system challenges. Unfortunately because of their restricted clinical use, more than half of such drugs have either been classified in BSC (biopharmaceutical classification system) class II / IV, or both are excluded from the development process. A nanotechnological theory acknowledges great anticipation and it is observed that lipoid processing is appropriate for the development of these drugs. From numerous nanolipoidal formulations Nanoemulsion based gel i.e. Nanoemulgel, has been considered to be an appropriate strategy for effective drug delivery across topical route. Nanoemulgel exists in two systems in which nanoemulsion containing drug is integrated in a gel base. Lipophilic drugs can be instantly integrated, and because of the finely dispersed oil droplets in the gel form, the skin absorption co-efficient of the incorporated drugs can be increased in numerous stages. At the same time, it can be more precisely targeted to the site of action and can prevent first pass metabolism and alleviate the patient from gastric/ systemic incompatibilities. The nanoemulgel drug delivery mechanism is a formulation-related technology designed to enhance drug absorption and lipophilic drug therapeutic profile. A significant growth in nanoemulgel has been noted in recent years owing to the high toleration of the formulation to the patients, due to their non-greasy, convenient spreadability, easy applicability, strong therapeutic and safety feature. Even after having few drawbacks, the formulation of nanoemulgel may in future be recognized as key and effective targets for the topical delivery of lipophilic drugs. Keywords: Nanolipoidal delivery, Nanoemulgel, Lipophilic drug, Human skin, etc.

Ubiquitination Regulators Discovered by Virtual Screening for the Treatment of Cancer.

The ubiquitin-proteasome system oversees cellular protein degradation in order to regulate various critical processes, such as cell cycle control and DNA repair. Ubiquitination can serve as a marker for mutation, chemical damage, transcriptional or translational errors, and heat-induced denaturation. However, aberrant ubiquitination and degradation of tumor suppressor proteins may result in the growth and metastasis of cancer. Hence, targeting the ubiquitination cascade reaction has become a potential strategy for treating malignant diseases. Meanwhile, computer-aided methods have become widely accepted as fast and efficient techniques for early stage drug discovery. This review summarizes ubiquitination regulators that have been discovered via virtual screening and their applications for cancer treatment.

An empirical study to examine the student activity analysis components of technology using an extended multi-labeled gradient boosting methodology

Abstract The data surveying this study mines methods for extracting informative and interesting knowledge from learning student datasets. The amount of data stored in education big databases is growing at an alarming rate. As such, data processing and knowledge discovery techniques can be used to discover interesting relationships between different characteristics and assessments of students. Initially to propose a novel self-analysis model which performs analysis about the different big data learning services. From the result provided by the user, the service will be ranked and analyze about the knowledge from daily activity gathered by the learner. The proposed model performs analysis to provide efficient Educational Data Mining (EDM) E-Learning system based on machine learning to ranking the users and performance evaluation in their activities. The existing method does not consider the learner activities, ranking and it focus on single feature based classification so the system doesn’t provide a better classification result. These issues to overcome introduce an Extended Multi-Labeled Gradient Boosting (XMGB) improve the classification performance both of imbalanced dataset. The method initial stage apply the Adaptive Expectation-Maximization Mixture (AEMM) method cluster the data various group and extract the E-learning multiple feature set. In this method to compute the classification time and memory provide an efficient result. Then introduce an academic activity based weight score analysis model to evaluate the learner daily activates and exam score its help to the ranking and cluster the data groups. In this proposed method analysis result to evaluate the following performance matrix: Recall, precision, F measure, classification accuracy. In this simulation analysis of the proposed method XMGB are provide an efficient classification with less time complexity compared to other existing methods.

Streaming Social Event Detection and Evolution Discovery in Heterogeneous Information Networks

Events are happening in real world and real time, which can be planned and organized for occasions, such as social gatherings, festival celebrations, influential meetings, or sports activities. Social media platforms generate a lot of real-time text information regarding public events with different topics. However, mining social events is challenging because events typically exhibit heterogeneous texture and metadata are often ambiguous. In this article, we first design a novel event-based meta-schema to characterize the semantic relatedness of social events and then build an event-based heterogeneous information network (HIN) integrating information from external knowledge base. Second, we propose a novel Pairwise Popularity Graph Convolutional Network, named as PP-GCN, based on weighted meta-path instance similarity and textual semantic representation as inputs, to perform fine-grained social event categorization and learn the optimal weights of meta-paths in different tasks. Third, we propose a streaming social event detection and evolution discovery framework for HINs based on meta-path similarity search, historical information about meta-paths, and heterogeneous DBSCAN clustering method. Comprehensive experiments on real-world streaming social text data are conducted to compare various social event detection and evolution discovery algorithms. Experimental results demonstrate that our proposed framework outperforms other alternative social event detection and evolution discovery techniques.