UMBC ebiquity
Masters Thesis Research Update: Aniket Bochare and Rohit Kugaonkar
Tuesday, November 22, 2011, 10:30am - Tuesday, November 22, 2011, 11:30am
ITE 325 - B
In this week's lab meeting we will have Aniket Bochare and
Rohit Kugaonkar from the MC2 lab will talk about the research they will be pursuing for their Masters Thesis and will update us on the progress they have been making.
Aniket Bochare will present "Supervised Learning Techniques for Predicting risk of Breast Cancer using Genetic Information"
Abstract: Breast cancer is the most common type of cancer and cause of highest number of deaths in women. Today’s world the treatment is based on just evidence and clinical parameters ignoring the genetic and environmental factors. Our study takes into consideration the socio-demographic, pedigree and Single nucleotide polymorphism (SNP) information of individuals to assess the risk of developing breast cancer in women with mutations in certain genes. We use statistical methods and supervised machine learning techniques to come up with a decision support system. Our model will help to replace costly procedures like lumpectomy and mastectomy in women with just regular screening. In conjunction with clinical studies our model will assist physicians to provide personalized treatment to women.
Rohit Kugaonkar will present "Prostate Cancer prognosis using genomic data".
Prostate cancer is the most common type of cancer found in American men. It is the second leading cause of cancer death. Existing clinical tests such as PSA (Prostate Specific Antigen) blood test, DRE (Digital rectal examination) are sometimes inaccurate. Prostate cancer is the slowly growing cancer. Sometimes surgery treatment is not recommended for non-aggressive prostate cancer patients. We will analyze SNPs based genomic data to predict the susceptibility for prostate cancer. We will also try to predict the aggressiveness of prostate cancer.
Aniket Bochare will present "Supervised Learning Techniques for Predicting risk of Breast Cancer using Genetic Information"
Abstract: Breast cancer is the most common type of cancer and cause of highest number of deaths in women. Today’s world the treatment is based on just evidence and clinical parameters ignoring the genetic and environmental factors. Our study takes into consideration the socio-demographic, pedigree and Single nucleotide polymorphism (SNP) information of individuals to assess the risk of developing breast cancer in women with mutations in certain genes. We use statistical methods and supervised machine learning techniques to come up with a decision support system. Our model will help to replace costly procedures like lumpectomy and mastectomy in women with just regular screening. In conjunction with clinical studies our model will assist physicians to provide personalized treatment to women.
Rohit Kugaonkar will present "Prostate Cancer prognosis using genomic data".
Prostate cancer is the most common type of cancer found in American men. It is the second leading cause of cancer death. Existing clinical tests such as PSA (Prostate Specific Antigen) blood test, DRE (Digital rectal examination) are sometimes inaccurate. Prostate cancer is the slowly growing cancer. Sometimes surgery treatment is not recommended for non-aggressive prostate cancer patients. We will analyze SNPs based genomic data to predict the susceptibility for prostate cancer. We will also try to predict the aggressiveness of prostate cancer.