No presente trabalho foi desenvolvida uma ferramenta para tornar o ensino do câncer na graduação mais efetivo. Tal ferramenta foi criada utilizando simulações multi-agente na plataforma NetLogo e conceitos gerais do processo de formação do câncer. O modelo que serviu de base para a criação da ferramenta foi o de Hanahan & Weinberg (2011). Inicialmente, para mostrar que a ferramenta é adequada para o ensino do câncer, foram definidos conceitualmente os Sistemas Complexos e o câncer e, em seguida, foi mostrado como estes se relacionam. Para desenvolver o presente trabalho, foram utilizadas pesquisas em dados secundários, entrevistas em profundidade e participação em aulas, palestras e seminários. O resultado desse processo foi uma ferramenta com diversas aplicações capaz de ensinar sobre o câncer através de muita interatividade e experimentação. Foi elaborada uma discussão sobre a problemática do ensino no Brasil e como isso afeta a aceitação de novas metodologias de ensino conforme a que é apresentada neste trabalho. Discutiu-se sobre o ensino do câncer no Brasil e a utilidade de ferramentas da área de sistemas complexos para tal. Para finalizar o trabalho, foram sugeridas algumas formas interessantes de se estender o uso da ferramenta desenvolvida na pesquisa científica, na clínica médica, no melhoramento de exames diagnósticos, entre outras / In the current work was developed a tool to increase the effectiveness of teaching cancer in undergraduate courses. This tool was built through multi-agents simulations in NetLogo platform and using general concepts of the cancer formation process. The tool is based in Hanahan & Weinbergs (2011) model. Initially, in order to justify the adequacy of the tool in teaching cancer, the Complex Systems and the Cancer are conceptualized and then, how they are related to each other. Secondary data research, in-depth interviews and participation in classes, seminars and lectures on relevant subjects were used to develop the current work. The result of this process is a teaching tool with many applications able to teach the student through much interaction and much experimentation. There is a discussion about the education in Brazil and how it affects new methodologies in class as the one presented in this work. There was also a discussion about the teaching of cancer in Brazil and the usefulness of tools from the field of Complex Systems for this teaching. To finish the work, some interesting forms of extension of this tool are suggested

Cancer researchers and oncologists benefit greatly from text mining major knowledge sources in biomedicine such as PubMed. Fundamentally, text mining depends on accurate text classification. In conventional natural language processing (NLP), this requires experts to annotate scientific text, which is costly and time consuming, resulting in small labelled datasets. This leads to extensive feature engineering and handcrafting in order to fully utilise small labelled datasets, which is again time consuming, and not portable between tasks and domains. In this work, we explore emerging neural network methods to reduce the burden of feature engineering while outperforming the accuracy of conventional pipeline NLP techniques. We focus specifically on the cancer domain in terms of applications, where we introduce two NLP classification tasks and datasets: the first task is that of semantic text classification according to the Hallmarks of Cancer (HoC), which enables text mining of scientific literature assisted by a taxonomy that explains the processes by which cancer starts and spreads in the body. The second task is that of the exposure routes of chemicals into the body that may lead to exposure to carcinogens. We present several novel contributions. We introduce two new semantic classification tasks (the hallmarks, and exposure routes) at both sentence and document levels along with accompanying datasets, and implement and investigate a conventional pipeline NLP classification approach for both tasks, performing both intrinsic and extrinsic evaluation. We propose a new approach to classification using multilevel embeddings and apply this approach to several tasks; we subsequently apply deep learning methods to the task of hallmark classification and evaluate its outcome. Utilising our text classification methods, we develop and two novel text mining tools targeting real-world cancer researchers. The first tool is a cancer hallmark text mining tool that identifies association between a search query and cancer hallmarks; the second tool is a new literature-based discovery (LBD) system designed for the cancer domain. We evaluate both tools with end users (cancer researchers) and find they demonstrate good accuracy and promising potential for cancer research.

Ovarian Cancer is the 5th most common cause of cancer death in women and the second most common gynaecological cancer in the UK. Worldwide, around 152,000 women were estimated to have died from ovarian cancer in 2012. Survival rates for women with epithelial ovarian cancer have not significantly changed since platinum-based treatment was introduced over 30 years ago. This is particularly disconcerting considering the fact that there is a less than 5% five year survival rate for patients diagnosed with late stage high grade serous ovarian cancer. This thesis examines the role of BMP signalling in ovarian cancer using in vitro cancer cell models. It builds upon the initial published work by the Inman lab identifying autocrine BMP9 as a promoter of ovarian cancer cell proliferation. The findings of Chapters 3-5 provide strong evidence indicating BMP9 as a context specific modulator of ovarian cancer cell proliferation. This significantly builds upon on the sole pro-proliferative BMP9 growth response previously described. Responding cell lines were subjected to a microarray with and without BMP9 treatment In order to determine early BMP target genes which were subsequently transiently knocked down in order to determine their role in the aetiology of said growth phenotype. ID1 gene expression was found to significantly contribute to the BMP9 proproliferative phenotype. Moreover several other BMP genes identified significantly alter basal cell proliferation. It was subsequently determined that BMP9 implemented a cell growth phenotype by negating apoptosis. .Excitingly, preliminary evidence suggests a marked reduction in detectable levels of a recently described Bax isoform, Bax β that coincide with BMP9 addition and the resultant anti-apoptotic phenotype observed. This is very interesting as no prior evidence correlating the BMP family and Bax β currently exists. These findings provide an enhanced understanding of BMP9s contribution to ovarian cancer pathogenesis that may result in the development of effective and targeted therapeutic interventions upon further stratification of the contextuality of the BMP induced growth response.