Comparative Study of Classification Techniques on Breast Cancer FNA Biopsy Data

Accurate diagnostic detection of the cancerous cells in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Bayesian classifier and other Artificial neural network classifiers (Backpropagation, linear programming, Learning vector quantization, and K nearest neighborhood) on the Wisconsin breast cancer classification problem

Detection of Breast Cancer using AI Techniques – A Survey

Cancer refers to any one of a large number of diseases characterized by the development of abnormal cells that divide uncontrollably and have the ability to infiltrate and destroy normal body tissue.Without treatment, it can cause serious health issues andresult in a loss of life. Breast cancer is the most common cancer among women around the world. Despite enormous medical progress, breast cancer has still remained the second leading cause of death worldwide. Early detection of cancer may reduce mortality and morbidity. This paper presents a review of the detection methods for cancer through Artificial Intelligence (AI) in different ways. Previously Microscopic reviews of tissues on glass slides are used for cancer diagnostics to improve diagnostic accuracy. We can use different techniques such as digital imaging and artificial intelligence algorithm. Cancer care is also advancing thanks to AI’s ability to collect and process data. Due to the nature of processing this information, the task is often a time-consuming and tedious job for doctors. This process may be made much easier, quicker and efficient through the advancement as well as by using modified technologies

Resistance exercise in men receiving androgen deprivation therapy for prostate cancer

This thesis encompasses two literature reviews (chapter 2 & 3) and two experimental chapters (4 and 5) examining the available literature on exercise and cancer, resistance training and its anabolic responses in older men and women, the side effects of Androgen Deprivation Therapy (ADT) for prostate cancer and finally, the role of resistance exercise as a clinical intervention to counteract such changes as an adjuvant therapy

The isolation and characterisation of MHC-presented peptides from CML-derived cell-lines, with a focus on post-translational modification

Phosphorylation is a key regulator of protein function and activity, and aberrant kinase activity is implicated in a wide range of malignancies, of which the bcr:abl fusion kinase found in chronic myeloid leukaemia is a classic example. As phosphopeptides are known to be presented by both the MHC class-I and class- II pathways, against which specific CD4+ and CD8+ T cell responses may be generated, study of MHC-presented phosphopeptides may reveal unique cancer antigens with direct links to the neoplastic state. Mild acid cell-surface elution is a rapid and effective method for MHC class-I peptide capture, though complicated by contamination with non-MHC peptides and poor downstream compatibility, especially with IMAC, a popular method for phosphopeptide enrichment. As an alternative to the citrate-phosphate elution buffer, a TMA-formate elution buffer is proposed. This was developed for IMAC compatibility, and osmotically balanced and supplemented to minimise cell lysis, (assessed by several assays) and used with a pH 5.5 prewash to reduce non- MHC peptide contamination. MALDI-MS/MS of MHC class-I peptides from K562- A3 cells found a sequence with high homology to a known cancer antigen as the common peak for both citrate-phosphate and TMA-formate eluted cells

Discriminative Representations for Heterogeneous Images and Multimodal Data

Histology images of tumor tissue are an important diagnostic and prognostic tool for pathologists. Recently developed molecular methods group tumors into subtypes to further guide treatment decisions, but they are not routinely performed on all patients. A lower cost and repeatable method to predict tumor subtypes from histology could bring benefits to more cancer patients. Further, combining imaging and genomic data types provides a more complete view of the tumor and may improve prognostication and treatment decisions. While molecular and genomic methods capture the state of a small sample of tumor, histological image analysis provides a spatial view and can identify multiple subtypes in a single tumor. This intra-tumor heterogeneity has yet to be fully understood and its quantification may lead to future insights into tumor progression. In this work, I develop methods to learn appropriate features directly from images using dictionary learning or deep learning. I use multiple instance learning to account for intra-tumor variations in subtype during training, improving subtype predictions and providing insights into tumor heterogeneity. I also integrate image and genomic features to learn a projection to a shared space that is also discriminative. This method can be used for cross-modal classification or to improve predictions from images by also learning from genomic data during training, even if only image data is available at test time.Doctor of Philosoph