Prediction Analysis Research Articles

Retrospective analysis of selected aspects of public blood transfusion service as a starting point for assessment of the status of transfusion medicine in Poland Part 1: Demographic characteristics of the donor population reporting for blood donation

Background: The ongoing demographic changes in Poland affect the number of donors and donations with direct impact on provision of blood and blood components for transfusion. With predicted population-decline, the number of potential donors is likely to decrease while the demand for blood is bound to increase as more people will require blood therapy. The structure of the Polish donor population reporting for blood donation is not uniform, moreover it has undergone additional changes within the study period. So it seems justified to perform in-depth analysis of the dynamics of changes in the number and structure of donor group as well as determine the direction and intensity of these changes in spatial and temporal terms. Identification of the possible causes of change is also important. Aim: presentation of the structure characteristics of the donor group who reported at the Polish regional blood transfusion centers (RBTCs) in the period 2005–2017 with the intention of donating blood/ blood components. Materials and methods: Analyses of available data-sets — annual reports from 21 Polish RBTCs forwarded to the IHTM. The following tools were used for statistical analysis: Microsoft Office: Access and Excel, Microsoft Power Business Intelligence (Power BI) software and STATISTICA version 13.3 software (TIBCO Software Inc.). Results: In 2005–2017 the total number of donors amounted to 8 691 611 including 2 542 621 (29.3%) women and 6 148 990 (70.7%) men. Voluntary non-remunerated donors were estimated at 8 684 069 (99.9%). The average annual number of donors per RBTC was 31 837 and included 31 809 voluntary non-remunerated donors; 22 524 men and 9314 women. Countrywide, the highest number was reported for the period 2011–2013; 701 882 (8.08%), 707 578 (8.14%) and 706 246 (8.13%) for respective years and for the period 2015–2016 the numbers were 718 887 (8.27%) and 706 476 (8.13%) respectively. The highest number for the period was reported by 5 RBTCs: Warsaw (951 068 — 10.94%), Katowice (748 841; 8.62%), Poznan (702 087; 8.08%), Krakow (651 057; 7.49%) and Wroclaw (556 357; 6.40%). In the number of 2 352 099 donors who have recently reported for blood donation at the RBTCs the 25–44 age group was the largest (48%). The least numerous was the 45–65 age group — 548 885 (ca. 11%). There were 2 030 692 donors in the 18–24 age category ( > 41% of the total donor-population).Trend-analysis demonstrated an upward tendency for the category of 25–44 age donors and a downward one for the 18–24 age category. No explicit trend was observed for the 45–65 age group. Conclusion: The number of blood donations in Poland has recently reached the limit of approximately 1 200 000 donations per year. To exceed this limit will be no easy task. All efforts must therefore be focused on promoting people’s willingness to donate blood. The analysis indicates a falling tendency for the 18–24 age group and a rising one for higher age groups, which only raises concern for the future. The priority is therefore to motivate people of all agegroups to donate blood and become multiple donors with special emphasis however on the very young people as they are the potential source for many years to come. Promotion of voluntary non-remunerated blood-donation among children is equally important; in several years’ time transfusion medicine will depend on their willingness to donate blood.

Using Business Intelligence Tools to Support Medical Validation of Laboratory Tests.

Modern clinical laboratories have to confirm that the procedures used for specific tests are reliable and valid. There are several sources of errors and interferences that can invalidate the results. Medical validation refers to the plausibility check of the test results. Implausible results indicate that something might went wrong with a sample retrieved from the patient, e.g., the blood sample got contaminated with another fluid, which requires re-examination. Here, we describe how an integrated R-based business intelligence (BI) tool can be developed that increase the efficiency of the medical validation at the Institute of Clinical Chemistry (ICC) of the University Hospital Zurich. A BI software environment allowed us to digitalize steps in the validation process that were manually done in Excel worksheets, e.g., importing the data, calculating percentiles, and producing graphical outputs.

Unravelling the integrated information systems and management control paradox: enhancing dynamic capability through business intelligence

AbstractThe purpose of this study is to examine whether the interactive effects of integrated information systems (IIS) and business intelligence (BI)‐enabled management control system (MCS) lead to the assimilation of integrated management control information into business processes and whether this assimilation enhances organisational performance. Data were gathered through a large survey and included 419 senior and middle managers representing 347 Australian organisations. The respondents were from organisations that use a single international vendor’s BI software designed to provide integrated MCS capability. As hypothesised, the results confirm the positive influence of the interaction between IIS and BI‐enabled MCS on assimilation of integrated management control information at the business process level, the positive relationship between business process level assimilation and business process performance, and finally the positive relationship between business process performance and overall organisational performance. The results also show that the interaction term has a significant indirect relationship with business process improvement and organisational performance.

Optimizing the Analytical Value of Oncology-Related Data Based on an In-Memory Analysis Layer: Development and Assessment of the Munich Online Comprehensive Cancer Analysis Platform.

BackgroundMany comprehensive cancer centers incorporate tumor documentation software supplying structured information from the associated centers’ oncology patients for internal and external audit purposes. However, much of the documentation data included in these systems often remain unused and unknown by most of the clinicians at the sites.ObjectiveTo improve access to such data for analytical purposes, a prerollout of an analysis layer based on the business intelligence software QlikView was implemented. This software allows for the real-time analysis and inspection of oncology-related data. The system is meant to increase access to the data while simultaneously providing tools for user-friendly real-time analytics.MethodsThe system combines in-memory capabilities (based on QlikView software) with innovative techniques that compress the complexity of the data, consequently improving its readability as well as its accessibility for designated end users. Aside from the technical and conceptual components, the software’s implementation necessitated a complex system of permission and governance.ResultsA continuously running system including daily updates with a user-friendly Web interface and real-time usage was established. This paper introduces its main components and major design ideas. A commented video summarizing and presenting the work can be found within the Multimedia Appendix.ConclusionsThe system has been well-received by a focus group of physicians within an initial prerollout. Aside from improving data transparency, the system’s main benefits are its quality and process control capabilities, knowledge discovery, and hypothesis generation. Limitations such as run time, governance, or misinterpretation of data are considered.

Augmenting the Business Intelligence Lifecycle Model with Usability: Using eye‑Tracking to Discover the why of Usability Problems

Humans and machines generate data at an alarming rate but still struggle to analyse and process this data fast enough. In order to meet this rising demand, business intelligence (BI) software firms are constantly incorporating new reporting or analytical functionalities in their software. At the same time, in order to be used optimally, they must ensure these features do not overwhelm users. There has however recently been cases where organisations, even after significant monetary investments, fail to realise the full potential of BI tools, sometimes reporting less than 20% utilisation. The purpose of this study was to investigate possible causes of the low utilisation and test the inclusion of a BI usability process. The study uses an off‑the‑shelf tool as a matter of convenience but proposes that including an empirical evaluation process as part of the BI design and development could increase BI adoption and usage. A small feasibility study was conducted to determine whether eye‑tracking is viable as a means of determining why the usability problems exist. Findings suggest that analysis of eye‑tracking data, together with traditional usability measures offer insight into how users solve problems and use the BI tool. Therefore, it will be a valuable aid in the design and testing of BI tools before release to enhance the usability therefore and potentially increase adoption and usage rates and should be included as a phase in the design and development lifecycle.

Summit Partners and RoboSoft, LLC: Mezzanine Debt Investment

In February 2013, John Carroll and Alexander Whittemore, both managing directors at Summit Partners (Summit), are considering an investment in RoboSoft, LLC (RoboSoft), a provider of data-center automation, business intelligence, and security software solutions, primarily for the IBM i operating system. Summit had previously invested in RoboSoft, and did well when it exited the company in 2007. Over the ensuing years, Summit had followed RoboSoft and was considering a second investment in the company when it was put up for sale again in late 2012. This time, Summit planned to invest $103.6 million from its growth equity fund and $43.9 million from its subordinated debt fund to buy out the company. This case is designed to introduce students to mezzanine investments. Because the deal involves both an equity and a subordinated debt investment, students can compare the investment considerations and return expectations of both types of investors. The case contains the actual deal team's investment memorandum summarizing the merits of the RoboSoft investment. The students are asked to qualitatively evaluate the potential benefits and risks of the investment from the perspective of a debt investor and an equity investor, and to quantitatively calculate the internal rate of returns (IRRs) and cash-on-cash returns (CoCs) of Summit's equity and subordinated debt fund investments. This case is appropriate for classes that survey private equity investments, or for corporate financing classes that wish to compare the risk and return of equity and debt investments. It is assumed that students have taken valuation courses and understand residual equity cash flow valuation methods. There are Excel files—one for students, one for instructors—to support analysis of this case. Excerpt UVA-F-1845 Rev. Apr. 16, 2020 Summit Partners and RoboSoft, LLC: Mezzanine Debt Investment In late February 2013, John Carroll and Alexander Whittemore, both managing directors of Summit Partners (Summit), were finishing a memorandum to the investment committee (IC) summarizing the merits of an investment in RoboSoft, LLC (RoboSoft) (Exhibit 1). The company was a provider of data-center automation, business intelligence, and security software solutions, primarily for the IBM i operating system (IBM i). As they discussed the situation, Carroll and Whittemore were reminded just how old this technology was. The precursor of the IBM i, the System 34/36/38, was developed in the 1970s, followed by the AS/400 in 1988—all before the internet, PCs, and cell phones became common household items. Although IBM continued to update the series, new operating systems had entered the market (e.g., Unix, Linux, and Windows) and began to erode its market position. The same issue had been a concern when Summit originally invested in RoboSoft in 2005. That investment, however, had worked out well and generated a 5x times; return for Summit's growth equity fund when the business was sold to its current owners, Adison Partners (Adison), in September 2007. In November 2012, Adison engaged Richmond Advisors to begin a new process for selling the company. After selling the business to Adison in 2007, Summit had maintained its relationship with the company and thought highly of the management team. As a result, Summit reviewed the investment again in this sale process and decided to put in a first-round bid for RoboSoft in January 2013. That bid had gotten Summit through the first round, and second-round bids were due in a few days. This time, the firm was contemplating a potential $ 43.9 million investment from its subordinated debt fund and a $ 103.6 million investment from its growth equity fund. Some of the same concerns Summit had faced with its earlier investment—possible obsolescence of the iSeries and RoboSoft's relatively slow organic growth—remained issues now. Additionally, investing money for a second time in the same company was likely to draw special scrutiny from the IC. . . .

IOT based Energy Management System for an Autonomous Hybrid Micro-Grid

Raising rate and require of power has led a lot of organizations to discover elegant ways for monitoring, controlling and reduction energy. To create an innovative idea to reduce the rate of energy consumption smart EMS (Energy Management System) is proposed in this paper. To develop IoT technologies and Big Data is used to improved hold energy utilization in commercial, housing and industrial sectors. An EMS is used to build smart homes is proposed for he developed cities. In this system, every residence tool is interfaced with a data attainment module that is an IoT object with an exclusive IP address ensuing in a huge mesh wireless network of devices. The data gaining SoC (System on Chip) module collects energy utilization data from every device of every stylish residence and send data to a centralized server for supplementary handing out and study. This information from all housing areas accumulates in the utility’s server as Big Data. EMS consumes off-the-shelf BI (Business Intelligence) and Big Data surveys software packages which improves the energy usages also to assemble user order. While air conditioning gives to 60% of power use in American countries, HVAC (Ventilation, Air Conditioning and Heating) are in use as a research to approve the proposed system.

IOT based Energy Management System for an Autonomous Hybrid Micro-Grid

Raising rate and require of power has led a lot of organizations to discover elegant ways for monitoring, controlling and reduction energy. To create an innovative idea to reduce the rate of energy consumption smart EMS (Energy Management System) is proposed in this paper. To develop IoT technologies and Big Data is used to improved hold energy utilization in commercial, housing and industrial sectors. An EMS is used to build smart homes is proposed for he developed cities. In this system, every residence tool is interfaced with a data attainment module that is an IoT object with an exclusive IP address ensuing in a huge mesh wireless network of devices. The data gaining SoC (System on Chip) module collects energy utilization data from every device of every stylish residence and send data to a centralized server for supplementary handing out and study. This information from all housing areas accumulates in the utility’s server as Big Data. EMS consumes off-the-shelf BI (Business Intelligence) and Big Data surveys software packages which improves the energy usages also to assemble user order. While air conditioning gives to 60% of power use in American countries, HVAC (Ventilation, Air Conditioning and Heating) are in use as a research to approve the proposed system.

A novel analysis-prediction approach for geometrically nonlinear problems using group method of data handling

A novel analysis-prediction (ANP) approach for geometrically nonlinear problems of solid mechanics is for the first time proposed in this paper. The key concept of this approach is: (1) A part of equilibrium path is traced by numerical analysis; (2) Data of this part is then used for training predictive network; (3) Applying the trained network, the rest of the equilibrium path is simply traced by pure prediction without using any analysis. As an illustration for ANP approach, the analysis package is in this study established based on isogeometric shell analysis using the first-order shear deformation shell theory (FSDT). As the main advantage of the proposed approach, computational cost is significantly lower than that of the conventional approach based on pure numerical analysis. In addition, the predictive networks are built via group method of data handling (GMDH) known as a self-organizing deep learning method for time series forecasting problems without requirement of big data. Some numerical examples are provided to confirm the high accuracy and efficiency of the proposed approach. The approach not only could be applied to a wide range of computational mechanics problems in which nonlinear response occurs but also to other computational engineering fields.

Improving quality care through it/analytics for team-based care interventions.

e18263 Background: Team based care is essential to achieve coordinated, high-quality care. We recognized a responsibility to identify new treatment patients to provided targeted, team based interventions. Previous attempts at identifying new patients were difficult, manual, and delayed. Our aim was to provide personnel with the appropriate means to identify new patients for services such as patient education, financial navigation, social work assessments and outreach calls for care coordination. As a result of multiple barriers and challenges, only 51% of our new patients were being identified. This project fit well in the larger context of the transformation of health care delivery through our participation in the Oncology Care Model, which places an emphasis on team based care. Methods: We used the Plan-Do-Study-Act continuous improvement model to develop an automated data set to identify new treatment patients across the oncology service line and leveraged our Information Technology (IT) Analytics team. A report was established based utilizing logic to identify the first appointment scheduled within the department and a secondary tab to look back at new patients within a three week time frame. Information is housed within a business intelligence software system that is easy for end users to see and understand. The initial report was validated for accuracy at an identification rate of 93% and the 3 week “look back” report gives the ability to identify the remaining patients. Results: We have seen marked improvements metrics as a result of the utilization of this report. Our Financial Navigators have been able to sustain out of pocket estimate calls at > 94% YTD 2018. Social Work initial patient assessments have sustained at 79% for the last 6 months, and we have seen nearly 400 documented oncology patient education encounters each month. Identification of these patients allows us to target patient education, outreach, and psychosocial assessments at initiation of therapy. Care Coordinators are able to establish patient lists to track eligibility for survivorship at the appropriate time points. Conclusions: This project demonstrates the value of using IT/Data Analytics for continuous quality improvement projects related to patient centered team based care. The cross collaboration of stakeholders in data analysis should be explored as a mechanism to transform care through key services and interventions.

Shifting needs for expertise: Digitized taskscapes in the Norwegian oil and gas industry

This article assesses the shaping of expertise within a small Norwegian firm distributing digital services to the global oil and gas industry. The oil price drop of 2014 shifted firm priorities from creating consulting reports about reliability of resource extracting operations to developing business intelligence software for oil and gas corporations. Drawing on data from ethnographic fieldwork, I seek to trace the transformation of knowledge practices within the intermediary. The investigation reveals that the taskscapes of the firm’s consultants and sales persons were increasingly directed toward interfaces, establishing new techniques of mediation between employees and clients. I argue that the researched firm underwent a successful transition toward the production of software because of its ability to locally transfer knowledge on its software among its internal professional groups.

Orakl preuzeo startap DataFox koji se bavi veštačkom inteligencijom

All the major artificial intelligence based product and service providers in the software industry are acquiring the AI startup to enhance the existing features and capabilities of their offerings to customers. Oracle is no exception to this strategy. It has acquired DataFox to enhance AI powered capabilities, expand available data sources for its business intelligence software. This paper is an attempt to study various facets of DataFox acquisitions such as deal itself, DataFox capabilities (products/applications, customers, competitors), reasons for Oracle to acquire DataFox, and its impact on existing products and applications of Oracle. In addition, the paper compares the acquisition with the model suggested by Bonzom and Netessine (2016) and concludes it is more for innovation, change in culture, reaching new markets, and having AI platform and less for problem solving by analysing nine propositions.

APPLICATION OF DATA BASE SYSTEMS BIG DATA AND BUSINESS INTELLIGENCE SOFTWARE IN INTEGRATED RISK MANAGEMENT IN ORGANIZATION

Currently, business analytics uses computerized platforms containing ready-made reporting formulas in the field of Business Intelligence. In recent years, software companies supporting enterprise management offer advanced applications of information-analytical Business Intelligence class systems consisting of modular development of these systems and combining business intelligence software with platforms that use data warehouse technology, multi-dimensional analytical processing software and data mining and processing applications. This article describes an example of this type of computerized analytical platform for business entities, which is included in analytical applications that allow quick access to necessary, aggregated and multi-criteria processed information. The software allows entrepreneurs and corporate managers as well as entities from the SME sector on the one hand to use embedded patterns of reports or analyzes, and on the other hand to self-develop and configure analyzes carried out, tailored to the specifics of a specific entity. Such analytical applications make it possible to build integrated risk management systems in the organization.

Integrated Analytical Hierarchy Process and Objective Matrix in Balanced Scorecard Dashboard Model for Performance Measurement

Measuring organizational performance is pivotal for a comprehensive understanding of strengths, weaknesses and to improve the quality of any organization’s performance. Balanced Scorecard (BSC) is the strategic evolution tool that is widely used to measure the organizational performances, and achievements from various aspects, both financial and non-financial. In this research, BSC was not only a straight jacket concept but also a high potential tool for measuring and managing tangible and accurate data through the application of several methods. This research weighted the variables of BSC based on significance values of Analytical Hierarchy Process (AHP) and Optimization of Measurement with Objective Matrix (OMAX). Moreover, a recommendation analysis was given based on the cause and effect analysis of variables and the achievement of Key Performance Indicators (KPIs). The flow of information, data, and performance measurement processes were designed into Business Intelligence (BI) software development i.e. BI-MonevDash. The framework and software BI-MonevDash proposed can be used as a new chosen tool for measuring and monitoring organizational performance. Recommendations could facilitate the leaders in decision making to improve the organizational performance and reduce risks.

An Evaluation Instrument (Q-U) for Measuring the usability of Business Intelligence Application

The main purpose of Business Intelligence (BI) applications is to focus on supporting organizations' strategic, operational and tactical decisions by providing comprehensive, accurate and vivid data to the decision makers. Usability testing is an important part of software development. It focuses on how well users can understand and utilize a product/software in fulfilling their intended goals. In the software development, most software tends to be complex. Hence, the way to ensure that such software will satisfy users will be done by measuring the usability of such software. Due to using many data integration techniques in developing BI application, thus, the different software have different instruments to measure the usability of BI software. In this paper, the new instrument (Q-U) for measuring BI software was developed. A systematic approach was adopted which suggested by many researchers in developing the intended Q-U instrument.The literature has been reviewed in a systematic manner to elicitation Q-U instrument attributes. Multi-methods were used in validating Q-U instrument such as face validity, a pilot study for testing the goodness of Q-U consistency, and factor analysis. Multi-methods were used in validating Q-U instrument such as face validity, a pilot study for testing the goodness of Q-U consistency, and factor analysis as well as Bartlett’s test for measuring the reliability of Q_U. The finding obtained indicates the workability of Q_U in measuring the usability OF BI applications. Ultimately, Q_U was used in measuring BI application in two different sectors.

Improving quality care through IT/analytics for team-based care interventions.

316 Background: Team based care is essential to achieve coordinated, high-quality care. We recognized a responsibility to identify new treatment patients to provided targeted, team based interventions. Previous attempts at identifying new patients were difficult, manual, and delayed. Our aim was to provide personnel with the appropriate means to identify new patients for services such as patient education, financial navigation, social work assessments and outreach calls for care coordination. As a result of multiple barriers and challenges, only 51% of our new patients were being identified. This project fit well in the larger context of the transformation of health care delivery through our participation in the Oncology Care Model, which places an emphasis on team based care. Methods: We used the Plan-Do-Study-Act continuous improvement model to develop an automated data set to identify new treatment patients across the oncology service line and leveraged our Information Technology (IT) Analytics team. A report was established based utilizing logic to identify the first appointment scheduled within the department and a secondary tab to look back at new patients within a three week time frame. Information is housed within a business intelligence software system that is easy for end users to see and understand. The initial report was validated for accuracy at an identification rate of 93% and the 3 week “look back” report gives the ability to identify the remaining patients. Results: We have seen marked improvements metrics as a result of the utilization of this report. Our Financial Navigators have been able to improve out of pocket estimate calls to 98% in April of 2018. Social Work initial patient assessments have improved by 40%, post chemotherapy outreach calls from RN Care Coordinators have improved 12%, and we have seen a 20% increase in the number of documented oncology patient education encounters. Conclusions: This project demonstrates the value of using IT/Data Analytics for continuous quality improvement projects related to patient centered team based care. The cross collaboration of stakeholders in data analysis should be explored as a mechanism to transform care through key services and interventions.

User Testing an Information Foraging Tool for Ambulatory Surgical Site Infection Surveillance.

Surveillance for surgical site infections (SSIs) after ambulatory surgery in children requires a detailed manual chart review to assess criteria defined by the National Health and Safety Network (NHSN). Electronic health records (EHRs) impose an inefficient search process where infection preventionists must manually review every postsurgical encounter (< 30 days). Using text mining and business intelligence software, we developed an information foraging application, the SSI Workbench, to visually present which postsurgical encounters included SSI-related terms and synonyms, antibiotic, and culture orders. This article compares the Workbench and EHR on four dimensions: (1) effectiveness, (2) efficiency, (3) workload, and (4) usability. Comparative usability test of Workbench and EHR. Objective test metrics are time per case, encounters reviewed per case, time per encounter, and retrieval of information meeting NHSN definitions. Subjective measures are cognitive load using the National Aeronautics and Space Administration (NASA) Task Load Index (NASA TLX), and a questionnaire on system usability and utility. Eight infection preventionists participated in the test. There was no difference in effectiveness as subjects retrieved information from all cases, using both systems, to meet the NHSN criteria. There was no difference in efficiency in time per case between the Workbench and EHR (8.58 vs. 7.39 minutes, p = 0.36). However, with the Workbench subjects opened fewer encounters per case (3.0 vs. 7.5, p = 0.002), spent more time per encounter (2.23 vs. 0.92 minutes, p = 0.002), rated the Workbench lower in cognitive load (NASA TLX, 24 vs. 33, p = 0.02), and significantly higher in measures of usability. Compared with the EHR, the Workbench was more usable, short, and reduced cognitive load. In overall efficiency, the Workbench did not save time, but demonstrated a shift from between-encounter foraging to within-encounter foraging and was rated as significantly more efficient. Our results suggest that infection surveillance can be better supported by systems applying information foraging theory.

A Location Intelligence System for the Assessment of Pluvial Flooding Risk and the Identification of Storm Water Pollutant Sources from Roads in Suburbanised Areas

The interplay of an ever-growing number of inhabitants, sprawl development, soil sealing, changes in urban traffic characteristics, as well as observed climate trends gives rise to more frequent pluvial flooding in cities, a higher run-off of water, and an increasing pollution of surface water. The aim of this research is to develop a location intelligence system for the assessment of pluvial flooding risks and the identification of storm water pollutant sources from roads in newly-developed areas. The system combines geographic information systems and business intelligence software, and it is based on the original Pluvial Flood Risk Assessment tool. The location intelligence system effectively identifies the spatial and temporal distribution of pluvial flood risks, allows to preliminarily evaluate the total run-off from roads, and helps localise potential places for new water management infrastructure. Further improvements concern the modelling of a flow accumulation and drainage system, the application of weather radar precipitation data, and traffic monitoring and modelling.

The disciplines of management and IT have indeed merged: new empirical data

Advancement in the intelligence field can only be achieved through new observations and the presentation of new empirical data. This is a continuous process and includes how we as employees engage with software and technical solutions. Just as it is impossible to teach or learn anything in marketing today without a deep understanding of digital marketing, in the same way is it impossible to make advancements in intelligence studies without first-hand experience with business intelligence software and new IT-equipment. Management and IT have indeed merged. This understanding has been an integrated part of JISIB since the journal started some eight years ago. And as always, we are less interested in how new technologies are developed (for that there are excellent technical journals) than about the management practice of these developments. This issue follows very much on this track.

In silico and in vitro analysis of coumarin derivative induced anticancer effects by undergoing intrinsic pathway mediated apoptosis in human stomach cancer

BackgroundCoumarin plays a vital role in drug discovery process due to its diverse biologically active components. Recently, coumarin derivatives are paying attention to treat various diseases including cancer. The effect of coumarin derivatives on gastric cancer is not well established although gastric cancer being the fourth leading cancer. Therefore, we attempt to study the effect of styrene substituted biscoumarin (SSBC) to induce apoptosis and inhibit cancer proliferation using in silico and in vitro approaches. MethodsWe performed 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay to identify the anti-proliferative activity of SSBC in stomach cancer cell lines (AGS) and toxicity of the compared was also assessed using lung normal cell lines (L-132 and MRC-5). A docking study was carried out between anti-apoptotic protein (BCL2) and SSBC compound. Furthermore, we analyzed the drug likeliness by screening pharmacological properties (ADME) and biological activity of SSBC by performing spectrum prediction analysis (PASS). The apoptotic effect of SSBC in AGS cell lines were detected using flow cytometry (FACS), Hoechst staining and DAPI/PI staining. Later, the regulation of apoptotic pathway by SSBC was also confirmed by qRT-PCR and western blotting analysis. ResultsThe inhibition concentration (IC50) of SSBC was assayed against AGS and lung normal cell lines (L-132 and MRC-5). The IC50 value of SSBC toward AGS, L-132 and MRC-5 was 4.56, 268 and 285 μg/ml, respectively. In silico analysis predicted SSBC could bind to the active site of BH3 domain of anti-apoptotic protein and thus resulted in apoptotic mediated cell death. ADME prediction of SSBC exhibit strong binding capacity of 99.08% and showed absorption rate about 95.57% in the intestine. In addition, biological activity of SSBC was also predicted using PASS program and we found SSBC exhibit high activity for various cancer related protein expression including apoptosis pathway proteins such as caspase 3 stimulant, apoptosis agonist. Furthermore, apoptosis of AGS was also assessed using Hoechst staining, DAPI/PI analysis, flow-cytometric analysis, qRT-PCR and western blot analysis. ConclusionOur study denotes that SSBC could be very effective against AGS by inducing apoptosis through intrinsic pathway and recommended for in vivo and human trials.