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Mining Gene Expression Data Focusing Cancer Therapeutics: A Digest
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Recommended
MRI in biomedical research
This presentation focuses on the role of MRI in biomedical engineering research with examples of a few global studies and the workflow associated with a MR academic project
KCA Big Data and Immunotherapeutics Symposium, August 31st, 2018, Sydney
Kids Cancer Alliance warmly invites you to this symposium featuring the latest developments in big data analytics, bioinformatics and, immunotherapeutic targeting.
An all-day symposium on the genomics of childhood cancer and germline predisposition, immune epitope targeting and CAR T cell therapeutics, and, single-cell transcriptomic and proteomic diagnostics in cancer.
For more details please contact Dr Michael Evtushenko MEvtushenko@ccia.unsw.edu.au
Twenty Years of Whole Slide Imaging - the Coming Phase Change
Presentation at Pathology Visions 2017 - https://digitalpathologyassociation.org/2017-pathology-visions-agenda
I will survey the development of Digital Pathology methodology beginning with the 1997 virtual microscope prototype at Hopkins (PMC2233368) to current tools, methods and algorithms designed to display, analyze and classify whole slide imaging data. I will describe the capabilities of current methods, describe how these methods are likely to evolve and how they will be likely to impact Pathology research and practice.
Mining Gene Expression Data Focusing Cancer Therapeutics: A Digest
An understanding towards genetics and epigenetics is essential to cope up with the paradigm shift which is underway. Personalized medicine and gene therapy will confluence the days to come.
This review highlights traditional approaches as well as current advancements in the analysis of the gene expression data from cancer perspective.
Due to improvements in biometric instrumentation and automation, it has become easier to collect a lot of experimental data in molecular biology.
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While differences in efficiency do exist, none of the well-established approaches is uniformly superior to others. The quality of algorithm is important, but is not in itself a guarantee of the quality of a specific data analysis.
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Performance and Evaluation of Data Mining Techniques in Cancer Diagnosis
Abstract: We analyze the breast Cancer data available from the WBC, WDBC from UCI machine learning with
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Several data mining classification techniques were used on the proposed data. There are many classification
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data with a total 286 rows and 10 columns will be used to test and justify the different between the classification
methods and algorithm.
Keywords - Machine learning, data mining Weka, classification, breast cancer
On Predicting and Analyzing Breast Cancer using Data Mining Approach
Breast Cancer is one of the crucial and burning diseases that has invaded women. Predicting breast cancer manually takes a lot of time and it is difficult for the physician to classification. So, detecting cancer through various automatic diagnostic techniques is very necessary. Data mining is the process of running powerful classification techniques that extract useful information from data. The uses and potentials of these techniques have found its scope in medical data. Classification techniques tend to simplify the prediction segment.
Breast Cancer Detection using Convolution Neural Network
https://irjet.net/archives/V4/i12/IRJET-V4I1258.pdf
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cancers’ have already been diagnosed is gathered and they are arranged, and then whether the other
patients are in trouble with breast cancer is tried to be predicted under cover of those data. Predictions of
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Performance and Evaluation of Data Mining Techniques in Cancer Diagnosis
Abstract: We analyze the breast Cancer data available from the WBC, WDBC from UCI machine learning with
the aim of developing accurate prediction models for breast cancer using data mining techniques. Data mining
has, for good reason, recently attracted a lot of attention, it is a new Technology, tackling new problem, with
great potential for valuable commercial and scientific discoveries. The experiments are conducted in WEKA.
Several data mining classification techniques were used on the proposed data. There are many classification
techniques in data mining such as Decision Tree, Rules NNge, Tree random forest, Random Tree, lazy IBK. The
aim of this paper is to investigate the performance of different classification techniques. The data breast cancer
data with a total 286 rows and 10 columns will be used to test and justify the different between the classification
methods and algorithm.
Keywords - Machine learning, data mining Weka, classification, breast cancer
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Breast Cancer is one of the crucial and burning diseases that has invaded women. Predicting breast cancer manually takes a lot of time and it is difficult for the physician to classification. So, detecting cancer through various automatic diagnostic techniques is very necessary. Data mining is the process of running powerful classification techniques that extract useful information from data. The uses and potentials of these techniques have found its scope in medical data. Classification techniques tend to simplify the prediction segment.
Breast Cancer Detection using Convolution Neural Network
https://irjet.net/archives/V4/i12/IRJET-V4I1258.pdf
Breast cancer diagnosis via data mining performance analysis of seven differe...
According to World Health Organization (WHO), breast cancer is the top cancer in women both in the
developed and the developing world. Increased life expectancy, urbanization and adoption of western
lifestyles trigger the occurrence of breast cancer in the developing world. Most cancer events are
diagnosed in the late phases of the illness and so, early detection in order to improve breast cancer
outcome and survival is very crucial.
In this study, it is intended to contribute to the early diagnosis of breast cancer. An analysis on breast
cancer diagnoses for the patients is given. For the purpose, first of all, data about the patients whose
cancers’ have already been diagnosed is gathered and they are arranged, and then whether the other
patients are in trouble with breast cancer is tried to be predicted under cover of those data. Predictions of
the other patients are realized through seven different algorithms and the accuracies of those have been
given. The data about the patients have been taken from UCI Machine Learning Repository thanks to Dr.
William H. Wolberg from the University of Wisconsin Hospitals, Madison. During the prediction process,
RapidMiner 5.0 data mining tool is used to apply data mining with the desired algorithms.
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Similar to 08 Vannucci, Marina
AI in translational medicine webinar
Dr. Dennis Wang discusses possible ways to enable ML methods to be more powerful for discovery and to reduce ambiguity within translational medicine, allowing data-informed decision-making to deliver the next generation of diagnostics and therapeutics to patients quicker, at lowered costs, and at scale.
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Math, Stats and CS in Public Health and Medical Research
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Presentation for the Data Science Luxembourg Meetup.
12 September 2018.
By Francisco Azuaje, PhD.
Luxembourg Institute of Health (LIH)
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The Clinical Genome Conference 2014
This year's 3rd Annual TCGC: The Clinical Genome Conference, held June 10-12, 2014 in San Francisco, is a three-day event that weaves together the science of sequencing and the business of implementing genomics in the clinic. It uniquely illustrates the mutual influence of those areas and the need to therefore consider the needs, challenges and opportunities of both - from next-generation sequencing and variant interpretation to insurance reimbursement and electronic health records - throughout the entire research process.Learn more at http://www.clinicalgenomeconference.com
DOCTORAL STUDY ORAL DEFENSE - MEDICAL IDENTITY THEFT AND PALM VEIN AUTHENTICA...
The Federal Bureau of Investigation reported that cyber actors will likely increase cyber intrusions against health care systems and their concomitant medical devices because of the mandatory transition from paper to electronic health records, lax cyber security standards, and a higher financial payout for medical records in the deep web. The problem addressed in this quantitative correlational study was uncertainty surrounding the benefits of palm vein authentication adoption relative to the growing crime of medical identity theft. The purpose of this quantitative correlational study was to understand healthcare managers’ and doctors’ perceptions of the effectiveness of palm vein authentication technology. The research questions were designed to investigate the relationship between intention to adopt palm vein authentication technology and perceived usefulness, complexity, security, peer influence, and relative advantage. The unified theory of acceptance and use of technology was the theoretical basis for this quantitative study. Data were gathered through an anonymous online survey of 109 healthcare managers and doctors, and analyzed using principal axis factoring, Pearson's product moment correlation, multiple linear regression, and one-way analysis of variance.
The data in the current study contributes to the field of management by providing to healthcare leaders and policymakers the daily perceptions of healthcare managers and doctors about palm vein authentication systems. The results of this study may help leaders of hospitals and other healthcare providers understand the perspectives of healthcare managers, and therefore, enable them to shape policies and procedures that guide the adoption of palm vein authentication systems to mitigate the risk of medical fraud, improve patient identification, and increase patient safety. (Preview)
Advancing Convergence and Innovation in Cancer Research: Seminar at Universit...
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MseqDR consortium: a grass-roots effort to establish a global resource aimed ...
MseqDR consortium: a grass-roots effort to establish a global resource aimed ...Human Variome Project
The success of whole exome sequencing (WES) for highly heterogeneous disorders, such as mitochondrial disease, is limited by substantial technical and bioinformatics challenges to correctly identify and prioritize the extensive number of sequence variants present in each patient. The likelihood of success can be greatly improved if a large cohort of patient data is assembled in which sequence variants can be systematically analysed, annotated, and interpreted relative to known phenotype. This effort has engaged and united more than 100 international mitochondrial clinicians, researchers, and bioinformaticians in the Mitochondrial Disease Sequence Data Resource (MSeqDR) consortium that formed in June 2012 to identify and prioritize the specific WES data analysis needs of the global mitochondrial disease community. Through regular web-based meetings, we have familiarized ourselves with existing strengths and gaps facing integration of MSeqDR with public resources, as well as the major practical, technical, and ethical challenges that must be overcome to create a sustainable data resource. We have now moved forward toward our common goal by establishing a central data resource (http://mseqdr.org/) that has both public access and secure web-based features that allow the coherent compilation, organization, annotation, and analysis of WES and mtDNA genome data sets generated in both clinical- and research-based settings of suspected mitochondrial disease patients. The most important aims of the MSeqDR consortium are summarized in the MSeqDR portal within the Consortium overview sections. Consortium participants are organized in 3 working groups that include (1) Technology and Bioinformatics; (2) Phenotyping, databasing, IRB concerns and access; and (3) Mitochondrial DNA specific concerns. The online MSeqDR resource is organized into discrete sections to facilitate data deposition and common reannotation, data visualization, data set mining, and access management. With the support of the United Mitochondrial Disease Foundation (UMDF) and the NINDS/NICHD U54 supported North American Mitochondrial Disease Consortium (NAMDC), the MSeqDR prototype has been built. Current major components include common data upload and reannotation using a novel HBCR based annotation tool that has also been made publicly available through the website, MSeqDR GBrowse that allows ready visualization of all public and MSeqDR specific data including labspecific aggregate data visualization tracks, MSeqDR-LSDB instance of nearly 1250 mitochondrial disease and mitochodnrial localized genes that is based on the Locus Specific Database model, exome data set mining in individuals or families using the GEM.app tool, and Account & Access Management. Within MSeqDR GBrowse it is now possible to explore data derived from MitoMap, HmtDB, ClinVar, UCSC-NumtS, ENCODE, 1000 genomes, and many other resources that bioinformaticians recruited to the project are organizing.Similar to 08 Vannucci, Marina (20)
Math, Stats and CS in Public Health and Medical Research
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Twenty Years of Whole Slide Imaging - the Coming Phase Change
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DOCTORAL STUDY ORAL DEFENSE - MEDICAL IDENTITY THEFT AND PALM VEIN AUTHENTICA...
Advancing Convergence and Innovation in Cancer Research: Seminar at Universit...
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08 Vannucci, Marina
- 1. Marina Vannucci Professor of Statistics Co-Director, Joint MDA-RICE Graduate Program in Biostatistics