8
•Download as DOCX, PDF•
0 likes•17 views
For further details contact: N.RAJASEKARAN B.E M.S 9841091117,9840103301. IMPULSE TECHNOLOGIES, Old No 251, New No 304, 2nd Floor, Arcot road , Vadapalani , Chennai-26. www.impulse.net.in Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
Report
Share
Report
Share
Recommended
A method for mining infrequent causal associations and its application in fin...
A method for mining infrequent causal associations and its application in fin...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.orgA method for mining infrequent causal associations and its application in fin...
A method for mining infrequent causal associations and its application in fin...JPINFOTECH JAYAPRAKASH
A method for mining infrequent causal associations and its application in finding adverse drug reaction signal pairsArtificial intelligence in drug discovery
This document discusses the use of artificial intelligence in drug discovery and development. It begins by defining artificial intelligence, machine learning, and deep learning. It then provides examples of how AI is currently used in various stages of the drug development process, including identifying molecular targets, finding hit compounds, optimizing lead compounds, predicting toxicity, and drug repurposing. It also discusses startups applying AI to drug discovery. Finally, it notes some limitations and drawbacks of using AI, such as potential bias in algorithms.
Case Study - Roberta
This document discusses Roberta Balcytyte's research using machine learning for early detection of rare hereditary diseases from large, imbalanced datasets. Specifically, the research aims to develop models to detect Hereditary Angioedema (HAE) using data on 1,200 HAE cases and 165 million controls. Initial results found Random Forest and AdaBoost classifiers performed best, accurately detecting HAE cases 88-89% of the time on average. The research seeks to supplement medical diagnosis by making rare disease detection faster and more accurate through machine learning.
2013.11.14 Big Data Workshop Adam Ralph - 1st set of slides
Adam Ralph from the Irish Centre for High End Computing presented this Introduction to Basic R during the Big Data Workshop hosted by the Social Sciences Computing Hub at the Whitaker Institute on the 14th November 2013
Machine Learning for Preclinical Research
This document summarizes a presentation on machine learning for preclinical research. It discusses how biomedical data sets are often small and discusses challenges in applying deep learning and other machine learning techniques with limited data. It proposes combining multiple smaller datasets using standards to create larger datasets for analysis. The document also notes issues with noise and bias in biomedical data and proposes careful curation and appropriate analysis methods. In conclusion, it advocates for carefully curated combined datasets, integrating different data types and sources, and validated application of machine learning to support preclinical research.
DSRU June 2011 1
This document discusses measures for objectively prioritizing drug safety signals and assessing the masking effect of measures of disproportionality. It proposes calculating the reported rate of fatality for drug-event pairs as a prioritization variable. It also develops a simplified masking ratio algorithm to identify drugs masking other drug-event combination signals, finding the prevalence of significant masking is low and mainly affects rarely reported events. Removing the highest masking drug only marginally affects signal rankings.
Artificial Intelligence for Discovery
DayOne Lab Talk with IBM - July 20th Basel - Artificial Intelligence for Discovery - Scott Spangler IBM Watson for Drug Discovery
Recommended
A method for mining infrequent causal associations and its application in fin...
A method for mining infrequent causal associations and its application in fin...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.orgA method for mining infrequent causal associations and its application in fin...
A method for mining infrequent causal associations and its application in fin...JPINFOTECH JAYAPRAKASH
A method for mining infrequent causal associations and its application in finding adverse drug reaction signal pairsArtificial intelligence in drug discovery
This document discusses the use of artificial intelligence in drug discovery and development. It begins by defining artificial intelligence, machine learning, and deep learning. It then provides examples of how AI is currently used in various stages of the drug development process, including identifying molecular targets, finding hit compounds, optimizing lead compounds, predicting toxicity, and drug repurposing. It also discusses startups applying AI to drug discovery. Finally, it notes some limitations and drawbacks of using AI, such as potential bias in algorithms.
Case Study - Roberta
This document discusses Roberta Balcytyte's research using machine learning for early detection of rare hereditary diseases from large, imbalanced datasets. Specifically, the research aims to develop models to detect Hereditary Angioedema (HAE) using data on 1,200 HAE cases and 165 million controls. Initial results found Random Forest and AdaBoost classifiers performed best, accurately detecting HAE cases 88-89% of the time on average. The research seeks to supplement medical diagnosis by making rare disease detection faster and more accurate through machine learning.
2013.11.14 Big Data Workshop Adam Ralph - 1st set of slides
Adam Ralph from the Irish Centre for High End Computing presented this Introduction to Basic R during the Big Data Workshop hosted by the Social Sciences Computing Hub at the Whitaker Institute on the 14th November 2013
Machine Learning for Preclinical Research
This document summarizes a presentation on machine learning for preclinical research. It discusses how biomedical data sets are often small and discusses challenges in applying deep learning and other machine learning techniques with limited data. It proposes combining multiple smaller datasets using standards to create larger datasets for analysis. The document also notes issues with noise and bias in biomedical data and proposes careful curation and appropriate analysis methods. In conclusion, it advocates for carefully curated combined datasets, integrating different data types and sources, and validated application of machine learning to support preclinical research.
DSRU June 2011 1
This document discusses measures for objectively prioritizing drug safety signals and assessing the masking effect of measures of disproportionality. It proposes calculating the reported rate of fatality for drug-event pairs as a prioritization variable. It also develops a simplified masking ratio algorithm to identify drugs masking other drug-event combination signals, finding the prevalence of significant masking is low and mainly affects rarely reported events. Removing the highest masking drug only marginally affects signal rankings.
Artificial Intelligence for Discovery
DayOne Lab Talk with IBM - July 20th Basel - Artificial Intelligence for Discovery - Scott Spangler IBM Watson for Drug Discovery
Data mining methodologies for pharmacovigilance
Medicines: is the applied science or practice of the diagnosis, treatment, and prevention of disease.
Bad effects called Adverse Drug Reactions (ADRs) , it differs from side effects.
Data mining methodologies for pharmacovigilance
Medicines is the applied science or practice of the diagnosis, treatment, and prevention of disease.
Bad effects called Adverse Drug Reactions (ADRs) , it differs from side effects.
Pharmacovigilance (PhV) is the science that concerns with the detection, assessment, understanding and prevention of ADRs
Indian j pharmacol b chakraborty_pv
Pharmacovigilance involves detecting adverse drug reactions through large databases of reported cases. Data mining uses statistical methods to find "signals" of previously unknown or incompletely known drug-adverse reaction associations within these databases. Bayesian methods are well-suited for data mining due to their ability to accommodate uncertainty. Once a potential new signal is detected, further causality assessment is required to determine if the drug did in fact cause the adverse reaction.
Computer software used in PHARMCAY AND PHARMACEUTICAL RESEARCH
today pharma field and R&D field used in computer software .and its data present in new software modeling used in pharma field
Journal
1) AI systems like Adam and Eve have discovered new scientific knowledge by autonomously generating and testing hypotheses about yeast genes using public databases and laboratory experiments.
2) AI is being applied throughout the drug development process, including target identification, compound design and synthesis, clinical trial optimization, and drug repurposing.
3) Partnerships between pharmaceutical companies and AI firms are exploring applications like generating new immuno-oncology treatments, metabolic disease therapies, and cancer treatments through large-scale data analysis.
Signal Detection in Pharmacovigilance: Methods and Algorithms
Signal detection in pharmacovigilance involves the identification of potential safety signals or unexpected patterns of adverse events that may indicate a previously unrecognized safety concern associated with a medication. Various methods and algorithms are employed to analyze large volumes of pharmacovigilance data and highlight signals that warrant further investigation. Here are some common methods and algorithms used for signal detection in pharmacovigilance
Drug Target Interaction (DTI) prediction (MSc. thesis)
- The document proposes a multi-view stacking ensemble method for drug-target interaction (DTI) prediction that combines predictions from multiple machine learning models trained on different drug and target feature view combinations.
- It generates 126 view combination datasets from 14 drug views and 9 target views, then trains extra trees, random forest, and XGBoost classifiers on each view combination. Predictions from these base models are then combined using a stacking ensemble with an extra trees meta-learner.
- The method is shown to outperform single models and voting ensembles, and calibration of the meta-learner and use of local imbalance measures provide further improvements to predictive performance on DTI prediction tasks.
presentation on in silico studies
effect of in silico pharmacology and toxicology studies on usage of experimental animals used in the drug discovery and development
RxpredictPresentation.pdf
Danika Gupta developed a novel pipeline called RxPredict to more accurately predict adverse drug reactions (ADRs) through the use of knowledge graphs and deep learning. The pipeline combines multiple data modalities, including drug chemical structures, known side effects and their relationships, and contextual drug target and indication information. Experimental results showed the multi-modal ensemble approach improved ADR prediction over single modality methods and correctly predicted over 88% of ADRs reported to the FDA for 10 validation drugs. The pipeline has potential to help reduce the significant human and economic costs of ADRs.
David Odgers - BMI Retreat 2014 Poster
The document analyzes search logs from healthcare professionals using an online medical reference source to explore the potential for using these logs as a data source for post-market drug safety surveillance. The researchers annotate search queries to identify drug and adverse event terms and perform statistical analysis to detect associations between drugs and events within search sessions. Their approach achieves good discrimination for a reference set of established adverse drug events but lower performance for a reference set of more recently identified events, warranting further investigation.
COMPUTATIONAL TOOLS FOR PREDICTION OF NUCLEAR RECEPTOR MEDIATED EFFECTS
Endocrine disrupting chemicals pose a significant threat to human health, society and the environment. Many of these chemicals elicit their toxicological effects through nuclear hormone receptors, like the estrogen receptor. Computational tools for predicting receptor mediated effects have been envisaged for their potential to be used for prioritization of chemicals for toxicological evaluation to reduce the amount of costly experimental testing and enable early alerts for newly designed compounds.
Srge most important publications 2020
بعض (وليس الكل) ملخصات الأبحاث الجيدة المنشورة فى بعض المجلات الجيدة وفيها تنوع من الافكار الابحاث الابتكارية التى يخدم فيها علوم الحاسبات فيها - انها تطبيقات حياتية
Data Mining and Big Data Analytics in Pharma
The document proposes software solutions for drug research, including text mining, data warehousing, data mining, database development, and big data analytics. It discusses common challenges in drug research like the high costs and low success rates. It then describes various solutions like text mining patents and research to help identify new research opportunities and reduce duplication of efforts. It provides examples of how various pharmaceutical companies use data mining and warehousing techniques. Overall, the document pitches different IT solutions that can help pharmaceutical and life sciences companies address their research challenges and make their processes more efficient.
METHODS OF CAUSALITY ASSESMENT.@ Clinical Pharmacy 4th Pharm D
1. Causality assessment is the evaluation of the likelihood that a treatment caused an adverse event by assessing the relationship between a drug and the occurrence of the event.
2. There are various methods of causality assessment including questionnaires, algorithms, and expert opinion that each have advantages and disadvantages, with no single universally accepted method.
3. The WHO scale and Naranjo scale are two of the most widely used algorithms that categorize the level of association between a drug and adverse event based on preset criteria.
nm0915-965-2
Ranadip Pal and his team won a prize in 2012 for using multiple types of 'omics data to more successfully predict drug sensitivity than other teams. Pal then improved on this model by combining five types of data, reducing uncertainty by over 40%. Scientists are increasingly seeing the value of integrated omics analyses to better understand cellular processes. A company called General Metabolics offers integrated omics services to analyze multiple data types from cell samples together, providing insights that individual analyses cannot. This allows researchers to gain a clearer understanding of cellular physiology.
Evaluation of Logistic Regression and Neural Network Model With Sensitivity A...
Logistic Regression (LR) is a well known classification method in the field of statistical learning. It allows probabilistic classification and shows promising results on several benchmark problems. Logistic regression enables us to investigate the relationship between a categorical outcome and a set of explanatory variables. Artificial Neural Networks (ANNs) are popularly used as universal non-linear inference models and have gained extensive popularity in recent years. Research activities are considerable and literature is growing. The goal of this research work is to compare the performance of Logistic Regression and Neural Network models on publicly available medical datasets. The evaluation process of the model is as follows. The logistic regression and neural network methods with sensitivity analysis have been evaluated for the effectiveness of the classification. The Classification Accuracy is used to measure the performance of both the models. From the experimental results it is confirmed that the neural network model with sensitivity analysis model gives more efficient result.
IRJET - A Framework for Predicting Drug Effectiveness in Human Body
This document proposes a machine learning framework for predicting drug-target interactions. It extracts features from drug molecules using FP2 fingerprints and from protein sequences using PsePSSM. It then uses Lasso dimensionality reduction to select important features before balancing the data using SMOTE. Finally, it trains an SVM classifier on the processed data to predict drug-target interactions. The framework achieved better performance than traditional methods by leveraging machine learning techniques for efficient and effective prediction of interactions without costly experiments.
In silico softwares
This document discusses various in silico software tools used for predicting genotoxicity and mutagenicity. It provides a table summarizing several software tools, including the prediction method used, applicable endpoints like mutagenicity and carcinogenicity, availability, and whether it is freely available or commercial. The table lists tools like TOPKAT, VEGA, Derek Nexus, ACD/Tox Suite, and others. It then discusses these and additional tools in more detail, focusing on the prediction approaches, models, and endpoints like developmental toxicity, reproductive toxicity, and endocrine disruption potential.
DRUG DISCOVERY & COMPUTER-AIDED DRUG DESIGN
This presentation provides an insight about Drug Discovery process using Computer Aided Drug Design (CADD) tools.
Advanced Methodologies in Pharmacovigilance
The World Health Organization defines pharmacovigilance as the science and actions connected to the detection, evaluation, understanding, and prevention of adverse effects or any other drug related problem. Pharmacovigilance is critical in ensuring that patients receive safe pharmaceuticals. We can learn more about a drugs side effects through a variety of methods, including spontaneous reporting, diligent monitoring, and database research. Novel mechanisms are being established at both the regulatory and scientific levels to increase pharmacovigilance. They include conditional approval and risk management strategies on a regulatory level, and openness and increasing patient engagement on a scientific one. OBJECTIVE To review and discuss various aspects of pharmacovigilance, including new methodological developments. V Sai Kruthika | Sarvani Ekathmika | Prathamesh Golapkar "Advanced Methodologies in Pharmacovigilance" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-2 , April 2023, URL: https://www.ijtsrd.com.com/papers/ijtsrd55052.pdf Paper URL: https://www.ijtsrd.com.com/other-scientific-research-area/other/55052/advanced-methodologies-in-pharmacovigilance/v-sai-kruthika
18
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
17
This document discusses efficient rendezvous algorithms for wireless sensor networks with mobile base stations. It proposes an approach where select sensor nodes act as rendezvous points, buffering and aggregating data from other sensors. These rendezvous points then transfer the collected data to the base station when it arrives, combining the advantages of controlled mobility and in-network caching. Algorithms are presented for rendezvous design with mobile base stations having variable or fixed tracks. Both theoretical analysis and simulations validate that this approach can achieve a good balance between energy savings and reduced data collection latency in the network.
More Related Content
Similar to 8
Data mining methodologies for pharmacovigilance
Medicines: is the applied science or practice of the diagnosis, treatment, and prevention of disease.
Bad effects called Adverse Drug Reactions (ADRs) , it differs from side effects.
Data mining methodologies for pharmacovigilance
Medicines is the applied science or practice of the diagnosis, treatment, and prevention of disease.
Bad effects called Adverse Drug Reactions (ADRs) , it differs from side effects.
Pharmacovigilance (PhV) is the science that concerns with the detection, assessment, understanding and prevention of ADRs
Indian j pharmacol b chakraborty_pv
Pharmacovigilance involves detecting adverse drug reactions through large databases of reported cases. Data mining uses statistical methods to find "signals" of previously unknown or incompletely known drug-adverse reaction associations within these databases. Bayesian methods are well-suited for data mining due to their ability to accommodate uncertainty. Once a potential new signal is detected, further causality assessment is required to determine if the drug did in fact cause the adverse reaction.
Computer software used in PHARMCAY AND PHARMACEUTICAL RESEARCH
today pharma field and R&D field used in computer software .and its data present in new software modeling used in pharma field
Journal
1) AI systems like Adam and Eve have discovered new scientific knowledge by autonomously generating and testing hypotheses about yeast genes using public databases and laboratory experiments.
2) AI is being applied throughout the drug development process, including target identification, compound design and synthesis, clinical trial optimization, and drug repurposing.
3) Partnerships between pharmaceutical companies and AI firms are exploring applications like generating new immuno-oncology treatments, metabolic disease therapies, and cancer treatments through large-scale data analysis.
Signal Detection in Pharmacovigilance: Methods and Algorithms
Signal detection in pharmacovigilance involves the identification of potential safety signals or unexpected patterns of adverse events that may indicate a previously unrecognized safety concern associated with a medication. Various methods and algorithms are employed to analyze large volumes of pharmacovigilance data and highlight signals that warrant further investigation. Here are some common methods and algorithms used for signal detection in pharmacovigilance
Drug Target Interaction (DTI) prediction (MSc. thesis)
- The document proposes a multi-view stacking ensemble method for drug-target interaction (DTI) prediction that combines predictions from multiple machine learning models trained on different drug and target feature view combinations.
- It generates 126 view combination datasets from 14 drug views and 9 target views, then trains extra trees, random forest, and XGBoost classifiers on each view combination. Predictions from these base models are then combined using a stacking ensemble with an extra trees meta-learner.
- The method is shown to outperform single models and voting ensembles, and calibration of the meta-learner and use of local imbalance measures provide further improvements to predictive performance on DTI prediction tasks.
presentation on in silico studies
effect of in silico pharmacology and toxicology studies on usage of experimental animals used in the drug discovery and development
RxpredictPresentation.pdf
Danika Gupta developed a novel pipeline called RxPredict to more accurately predict adverse drug reactions (ADRs) through the use of knowledge graphs and deep learning. The pipeline combines multiple data modalities, including drug chemical structures, known side effects and their relationships, and contextual drug target and indication information. Experimental results showed the multi-modal ensemble approach improved ADR prediction over single modality methods and correctly predicted over 88% of ADRs reported to the FDA for 10 validation drugs. The pipeline has potential to help reduce the significant human and economic costs of ADRs.
David Odgers - BMI Retreat 2014 Poster
The document analyzes search logs from healthcare professionals using an online medical reference source to explore the potential for using these logs as a data source for post-market drug safety surveillance. The researchers annotate search queries to identify drug and adverse event terms and perform statistical analysis to detect associations between drugs and events within search sessions. Their approach achieves good discrimination for a reference set of established adverse drug events but lower performance for a reference set of more recently identified events, warranting further investigation.
COMPUTATIONAL TOOLS FOR PREDICTION OF NUCLEAR RECEPTOR MEDIATED EFFECTS
Endocrine disrupting chemicals pose a significant threat to human health, society and the environment. Many of these chemicals elicit their toxicological effects through nuclear hormone receptors, like the estrogen receptor. Computational tools for predicting receptor mediated effects have been envisaged for their potential to be used for prioritization of chemicals for toxicological evaluation to reduce the amount of costly experimental testing and enable early alerts for newly designed compounds.
Srge most important publications 2020
بعض (وليس الكل) ملخصات الأبحاث الجيدة المنشورة فى بعض المجلات الجيدة وفيها تنوع من الافكار الابحاث الابتكارية التى يخدم فيها علوم الحاسبات فيها - انها تطبيقات حياتية
Data Mining and Big Data Analytics in Pharma
The document proposes software solutions for drug research, including text mining, data warehousing, data mining, database development, and big data analytics. It discusses common challenges in drug research like the high costs and low success rates. It then describes various solutions like text mining patents and research to help identify new research opportunities and reduce duplication of efforts. It provides examples of how various pharmaceutical companies use data mining and warehousing techniques. Overall, the document pitches different IT solutions that can help pharmaceutical and life sciences companies address their research challenges and make their processes more efficient.
METHODS OF CAUSALITY ASSESMENT.@ Clinical Pharmacy 4th Pharm D
1. Causality assessment is the evaluation of the likelihood that a treatment caused an adverse event by assessing the relationship between a drug and the occurrence of the event.
2. There are various methods of causality assessment including questionnaires, algorithms, and expert opinion that each have advantages and disadvantages, with no single universally accepted method.
3. The WHO scale and Naranjo scale are two of the most widely used algorithms that categorize the level of association between a drug and adverse event based on preset criteria.
nm0915-965-2
Ranadip Pal and his team won a prize in 2012 for using multiple types of 'omics data to more successfully predict drug sensitivity than other teams. Pal then improved on this model by combining five types of data, reducing uncertainty by over 40%. Scientists are increasingly seeing the value of integrated omics analyses to better understand cellular processes. A company called General Metabolics offers integrated omics services to analyze multiple data types from cell samples together, providing insights that individual analyses cannot. This allows researchers to gain a clearer understanding of cellular physiology.
Evaluation of Logistic Regression and Neural Network Model With Sensitivity A...
Logistic Regression (LR) is a well known classification method in the field of statistical learning. It allows probabilistic classification and shows promising results on several benchmark problems. Logistic regression enables us to investigate the relationship between a categorical outcome and a set of explanatory variables. Artificial Neural Networks (ANNs) are popularly used as universal non-linear inference models and have gained extensive popularity in recent years. Research activities are considerable and literature is growing. The goal of this research work is to compare the performance of Logistic Regression and Neural Network models on publicly available medical datasets. The evaluation process of the model is as follows. The logistic regression and neural network methods with sensitivity analysis have been evaluated for the effectiveness of the classification. The Classification Accuracy is used to measure the performance of both the models. From the experimental results it is confirmed that the neural network model with sensitivity analysis model gives more efficient result.
IRJET - A Framework for Predicting Drug Effectiveness in Human Body
This document proposes a machine learning framework for predicting drug-target interactions. It extracts features from drug molecules using FP2 fingerprints and from protein sequences using PsePSSM. It then uses Lasso dimensionality reduction to select important features before balancing the data using SMOTE. Finally, it trains an SVM classifier on the processed data to predict drug-target interactions. The framework achieved better performance than traditional methods by leveraging machine learning techniques for efficient and effective prediction of interactions without costly experiments.
In silico softwares
This document discusses various in silico software tools used for predicting genotoxicity and mutagenicity. It provides a table summarizing several software tools, including the prediction method used, applicable endpoints like mutagenicity and carcinogenicity, availability, and whether it is freely available or commercial. The table lists tools like TOPKAT, VEGA, Derek Nexus, ACD/Tox Suite, and others. It then discusses these and additional tools in more detail, focusing on the prediction approaches, models, and endpoints like developmental toxicity, reproductive toxicity, and endocrine disruption potential.
DRUG DISCOVERY & COMPUTER-AIDED DRUG DESIGN
This presentation provides an insight about Drug Discovery process using Computer Aided Drug Design (CADD) tools.
Advanced Methodologies in Pharmacovigilance
The World Health Organization defines pharmacovigilance as the science and actions connected to the detection, evaluation, understanding, and prevention of adverse effects or any other drug related problem. Pharmacovigilance is critical in ensuring that patients receive safe pharmaceuticals. We can learn more about a drugs side effects through a variety of methods, including spontaneous reporting, diligent monitoring, and database research. Novel mechanisms are being established at both the regulatory and scientific levels to increase pharmacovigilance. They include conditional approval and risk management strategies on a regulatory level, and openness and increasing patient engagement on a scientific one. OBJECTIVE To review and discuss various aspects of pharmacovigilance, including new methodological developments. V Sai Kruthika | Sarvani Ekathmika | Prathamesh Golapkar "Advanced Methodologies in Pharmacovigilance" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-2 , April 2023, URL: https://www.ijtsrd.com.com/papers/ijtsrd55052.pdf Paper URL: https://www.ijtsrd.com.com/other-scientific-research-area/other/55052/advanced-methodologies-in-pharmacovigilance/v-sai-kruthika
Similar to 8 (20)
Computer software used in PHARMCAY AND PHARMACEUTICAL RESEARCH
Computer software used in PHARMCAY AND PHARMACEUTICAL RESEARCH
Signal Detection in Pharmacovigilance: Methods and Algorithms
Signal Detection in Pharmacovigilance: Methods and Algorithms
Drug Target Interaction (DTI) prediction (MSc. thesis)
Drug Target Interaction (DTI) prediction (MSc. thesis)
COMPUTATIONAL TOOLS FOR PREDICTION OF NUCLEAR RECEPTOR MEDIATED EFFECTS
COMPUTATIONAL TOOLS FOR PREDICTION OF NUCLEAR RECEPTOR MEDIATED EFFECTS
METHODS OF CAUSALITY ASSESMENT.@ Clinical Pharmacy 4th Pharm D
METHODS OF CAUSALITY ASSESMENT.@ Clinical Pharmacy 4th Pharm D
Evaluation of Logistic Regression and Neural Network Model With Sensitivity A...
Evaluation of Logistic Regression and Neural Network Model With Sensitivity A...
IRJET - A Framework for Predicting Drug Effectiveness in Human Body
IRJET - A Framework for Predicting Drug Effectiveness in Human Body
More from Technology_solution
18
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
17
This document discusses efficient rendezvous algorithms for wireless sensor networks with mobile base stations. It proposes an approach where select sensor nodes act as rendezvous points, buffering and aggregating data from other sensors. These rendezvous points then transfer the collected data to the base station when it arrives, combining the advantages of controlled mobility and in-network caching. Algorithms are presented for rendezvous design with mobile base stations having variable or fixed tracks. Both theoretical analysis and simulations validate that this approach can achieve a good balance between energy savings and reduced data collection latency in the network.
16
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
15
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
25
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
24
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
23
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
22
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
21
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
20
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
19
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
18
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
17
This document discusses preventing private information inference attacks on social networks. It explores how released social networking data could be used to predict undisclosed private information about individuals, such as their political affiliation or sexual orientation. It then describes three sanitization techniques that could be used to decrease the effectiveness of such attacks. An experiment is conducted applying these techniques to a Facebook dataset to attempt to discover sensitive attributes through collective inference and show that the sanitization methods decrease the effectiveness of local and relational classification algorithms.
16
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
15
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
14
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
13
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
12
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
11
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
10
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
Recently uploaded
Traditional Musical Instruments of Arunachal Pradesh and Uttar Pradesh - RAYH...
Traditional Musical Instruments of Arunachal Pradesh and Uttar Pradesh
RHEOLOGY Physical pharmaceutics-II notes for B.pharm 4th sem students
Physical pharmaceutics notes for B.pharm students
Temple of Asclepius in Thrace. Excavation results
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
Mule event processing models | MuleSoft Mysore Meetup #47
Mule event processing models | MuleSoft Mysore Meetup #47
Event Link:- https://meetups.mulesoft.com/events/details/mulesoft-mysore-presents-mule-event-processing-models/
Agenda
● What is event processing in MuleSoft?
● Types of event processing models in Mule 4
● Distinction between the reactive, parallel, blocking & non-blocking processing
For Upcoming Meetups Join Mysore Meetup Group - https://meetups.mulesoft.com/mysore/YouTube:- youtube.com/@mulesoftmysore
Mysore WhatsApp group:- https://chat.whatsapp.com/EhqtHtCC75vCAX7gaO842N
Speaker:-
Shivani Yasaswi - https://www.linkedin.com/in/shivaniyasaswi/
Organizers:-
Shubham Chaurasia - https://www.linkedin.com/in/shubhamchaurasia1/
Giridhar Meka - https://www.linkedin.com/in/giridharmeka
Priya Shaw - https://www.linkedin.com/in/priya-shaw
BÀI TẬP BỔ TRỢ TIẾNG ANH LỚP 9 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2024-2025 - ...
BÀI TẬP BỔ TRỢ TIẾNG ANH LỚP 9 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2024-2025 - ...Nguyen Thanh Tu Collection
https://app.box.com/s/tacvl9ekroe9hqupdnjruiypvm9rdanePengantar Penggunaan Flutter - Dart programming language1.pptx
Pengantar Penggunaan Flutter - Dart programming language1.pptx
مصحف القراءات العشر أعد أحرف الخلاف سمير بسيوني.pdf
مصحف أحرف الخلاف للقراء العشرةأعد أحرف الخلاف بالتلوين وصلا سمير بسيوني غفر الله له
Benner "Expanding Pathways to Publishing Careers"
This presentation was provided by Rebecca Benner, Ph.D., of the American Society of Anesthesiologists, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
The History of Stoke Newington Street Names
Presented at the Stoke Newington Literary Festival on 9th June 2024
www.StokeNewingtonHistory.com
How to deliver Powerpoint Presentations.pptx
"How to make and deliver dynamic presentations by making it more interactive to captivate your audience attention"
Gender and Mental Health - Counselling and Family Therapy Applications and In...
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
BÀI TẬP DẠY THÊM TIẾNG ANH LỚP 7 CẢ NĂM FRIENDS PLUS SÁCH CHÂN TRỜI SÁNG TẠO ...
BÀI TẬP DẠY THÊM TIẾNG ANH LỚP 7 CẢ NĂM FRIENDS PLUS SÁCH CHÂN TRỜI SÁNG TẠO ...Nguyen Thanh Tu Collection
https://app.box.com/s/qhtvq32h4ybf9t49ku85x0n3xl4jhr15Stack Memory Organization of 8086 Microprocessor
The stack memory organization of 8086 microprocessor.
Recently uploaded (20)
Traditional Musical Instruments of Arunachal Pradesh and Uttar Pradesh - RAYH...
Traditional Musical Instruments of Arunachal Pradesh and Uttar Pradesh - RAYH...
RHEOLOGY Physical pharmaceutics-II notes for B.pharm 4th sem students
RHEOLOGY Physical pharmaceutics-II notes for B.pharm 4th sem students
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
Mule event processing models | MuleSoft Mysore Meetup #47
Mule event processing models | MuleSoft Mysore Meetup #47
BÀI TẬP BỔ TRỢ TIẾNG ANH LỚP 9 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2024-2025 - ...
BÀI TẬP BỔ TRỢ TIẾNG ANH LỚP 9 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2024-2025 - ...
Pengantar Penggunaan Flutter - Dart programming language1.pptx
Pengantar Penggunaan Flutter - Dart programming language1.pptx
مصحف القراءات العشر أعد أحرف الخلاف سمير بسيوني.pdf
مصحف القراءات العشر أعد أحرف الخلاف سمير بسيوني.pdf
SWOT analysis in the project Keeping the Memory @live.pptx
SWOT analysis in the project Keeping the Memory @live.pptx
Gender and Mental Health - Counselling and Family Therapy Applications and In...
Gender and Mental Health - Counselling and Family Therapy Applications and In...
BÀI TẬP DẠY THÊM TIẾNG ANH LỚP 7 CẢ NĂM FRIENDS PLUS SÁCH CHÂN TRỜI SÁNG TẠO ...
BÀI TẬP DẠY THÊM TIẾNG ANH LỚP 7 CẢ NĂM FRIENDS PLUS SÁCH CHÂN TRỜI SÁNG TẠO ...
8
- 1. Impulse Technologies Beacons U to World of technology 044-42133143, 98401 03301,9841091117 ieeeprojects@yahoo.com www.impulse.net.in A Method for Mining Infrequent Causal Associations and Its Application in Finding Adverse Drug Reaction Signal Pairs Abstract Discovering infrequent causal relationships can help us prevent or correct negative outcomes caused by their antecedents. In this paper, we propose an innovative data mining framework and apply it to mine potential causal associations in electronic patient datasets where the drug-related events of interest occur infrequently. Specifically, we created a novel interestingness measure, exclusive causal-leverage, based on a computational, fuzzy recognition-primed decision (RPD) model that we previously developed. On the basis of this new measure, a data mining algorithm was developed to mine the causal relationship between drugs and their associated adverse drug reactions (ADRs). The exclusive causal-leverage was employed to rank the potential causal associations between each of the three selected drugs (i.e., enalapril, pravastatin and rosuvastatin) and 3,954 recorded symptoms, each of which corresponded to a potential ADR. The top 10 drug-symptom pairs for each drug were evaluated by the physicians on our project team. The numbers of symptoms considered as likely real ADRs for enalapril, pravastatin and rosuvastatin were 8, 7, and 6, respectively. These preliminary results indicate the usefulness of our method in finding potential ADR signal pairs for further analysis (e.g., epidemiology study) and investigation (e.g., case review) by drug safety professionals. Your Own Ideas or Any project from any company can be Implemented at Better price (All Projects can be done in Java or DotNet whichever the student wants) 1