Progressive system engineer with 8 years of hands-on experience developing and implementing innovative software
products and solutions that significantly increase productivity and profitability. Adept at delivering high-quality products
while establishing solid analytical and problem solving abilities. Skilled using Core Java, PHP, OOP, Design Patterns,
SOA, Data Structure / Algorithms, JavaScript, jQuery, CSS, XML, HTML, JSON, MySQL, Oracle, and Informix while
leading comprehensive software development. Experienced in implementing application through entire Software
Development Life Cycle.
The assistant, ll cplanningofproductsandservicestrevisedcopyRenee Renesa Green
Here are additional details about our products and services you will find interesting and matched services for your needs. Enjoy, because we care about you.
Progressive system engineer with 8 years of hands-on experience developing and implementing innovative software
products and solutions that significantly increase productivity and profitability. Adept at delivering high-quality products
while establishing solid analytical and problem solving abilities. Skilled using Core Java, PHP, OOP, Design Patterns,
SOA, Data Structure / Algorithms, JavaScript, jQuery, CSS, XML, HTML, JSON, MySQL, Oracle, and Informix while
leading comprehensive software development. Experienced in implementing application through entire Software
Development Life Cycle.
The assistant, ll cplanningofproductsandservicestrevisedcopyRenee Renesa Green
Here are additional details about our products and services you will find interesting and matched services for your needs. Enjoy, because we care about you.
The slides from the Adaptive & Responsive Design Lab at Elliot Masie's Learning 2015 conference. Co-presented with Bob Mosher and Bernadette (Lawler) Sciarabba. These were used to support a discovery session that broke the audience into teams. The teams used the links in the presentation (slide 3) to compare examples across different devices and share their findings on a Twitter wall,
Toward Integrated Clinical and Gene Expression Profiles for Breast Cancer Pro...CSCJournals
Breast cancer patients with the same diagnostic and clinical prognostic profile can have markedly different clinical outcome. This difference is possibly caused by the limitation of current breast cancer prognostic indices, which group molecularly distinct patients into similar clinical classes based mainly on morphological of disease. Traditional clinical based prognosis models were discovered contain some restriction to address the heterogeneity of breast cancer. The invention of microarray technology and its ability to simultaneously interrogate thousands genes has changed the paradigm of molecular classification of human cancers as well as it shifted clinical prognosis model to broader prospect. Numerous studies have revealed the potential value of gene expression signatures in examining the risk of disease recurrence. However, currently most of these studies attempted to implement genetic marker based prognostic models to replace the traditional clinical markers, yet neglecting the rich information contain in clinical information. Therefore, this research took an effort to integrate both clinical and microarray data in order to obtain accurate breast cancer prognosis, by taking into account that these data complements each other. This article presents a review of the development of breast cancer prognosis models, concentrating precisely on clinical and gene expression profiles. The literature is reviewed in an explicit machine learning framework, which include the elements of feature selection and classification techniques.
The slides from the Adaptive & Responsive Design Lab at Elliot Masie's Learning 2015 conference. Co-presented with Bob Mosher and Bernadette (Lawler) Sciarabba. These were used to support a discovery session that broke the audience into teams. The teams used the links in the presentation (slide 3) to compare examples across different devices and share their findings on a Twitter wall,
Toward Integrated Clinical and Gene Expression Profiles for Breast Cancer Pro...CSCJournals
Breast cancer patients with the same diagnostic and clinical prognostic profile can have markedly different clinical outcome. This difference is possibly caused by the limitation of current breast cancer prognostic indices, which group molecularly distinct patients into similar clinical classes based mainly on morphological of disease. Traditional clinical based prognosis models were discovered contain some restriction to address the heterogeneity of breast cancer. The invention of microarray technology and its ability to simultaneously interrogate thousands genes has changed the paradigm of molecular classification of human cancers as well as it shifted clinical prognosis model to broader prospect. Numerous studies have revealed the potential value of gene expression signatures in examining the risk of disease recurrence. However, currently most of these studies attempted to implement genetic marker based prognostic models to replace the traditional clinical markers, yet neglecting the rich information contain in clinical information. Therefore, this research took an effort to integrate both clinical and microarray data in order to obtain accurate breast cancer prognosis, by taking into account that these data complements each other. This article presents a review of the development of breast cancer prognosis models, concentrating precisely on clinical and gene expression profiles. The literature is reviewed in an explicit machine learning framework, which include the elements of feature selection and classification techniques.