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- Creators: Computer Science and Engineering Program
Description
Mr. Green has stage 4 prostate cancer which has spread to the bones and liver and has become resistant to radiation and standard chemotherapy treatment. After 3 rounds of chemotherapy, his primary oncologist recommends that he participate in a clinical trial. He went to Dr. Red at the Saguaro Clinic after reading on the internet about a new Phase 1 clinical trial that the clinic is hosting, which is designed to target a specific receptor called AB-111 that may be present in malignant prostate, cervical, ovarian, and breast cells. After signing consent and completing the blood screens in the morning at the clinic, Mr. Green is told his liver enzymes are too high and the ranges specified in the protocol prohibit him from enrolling. Mr. Green is noticeably affected and distressed at this news, and Dr. Red recommends end-of-life care. Behind the scenes, this event is noted on official medical documents and trial study rosters as a "screen fail." This narrative, while fictional, is realistic because similar events occur in cancer clinical trial sites on a regular basis. I look at the inner "world" and mental journey of possible clinical trial candidates as they seek out information about clinical trials and gain understanding of their function \u2014 specifically in the context of Phase 1 cancer clinical trials. To whom is the language of the term "screen failure" useful? How does excluding individuals from clinical trials protect their health and does the integrity of the trial data supersede the person's curative goals? What is the message that cancer patients (potential research subjects) receive regarding clinical trials from sources outside their oncologists?
ContributorsMcKane, Alexandra (Author) / Maienschein, Jane (Thesis director) / Ellison, Karin (Committee member) / Foy, Joseph (Committee member) / Barrett, The Honors College (Contributor)
Created2013-12
Description
Immunotherapy is an effective treatment for cancer which enables the patient's immune system to recognize tumor cells as pathogens. In order to design an individualized treatment, the t cell receptors (TCR) which bind to a tumor's unique antigens need to be determined. We created a convolutional neural network to predict the binding affinity between a given TCR and antigen to enable this.
ContributorsCai, Michael Ray (Author) / Lee, Heewook (Thesis director) / Meuth, Ryan (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2020-12
Description
Cancer is the second leading cause of death in the United States. Cancer is a serious, complex disease which causes cells to grow uncontrollably, causing millions of deaths per year [1]. Cancer is usually caused by a combination of environmental variables and biological pathways. The pathways have a very robust structure normally, but are altered because of cancer, resulting in a loss of connectivity between pathways. In order detect these pathways, a PageRank-based method called Pathways of Topological Rank Analysis (PoTRA) was created, which measures the relative rankings of the genes in each pathway. Applying this algorithm will allow us to figure out what pathways differed significantly in areas with cancer and areas without cancer. This would allow scientists to focus on specific pathways in order to learn more about the cancer and find more effective ways to treat it. So far, analysis using PoTRA has been successfully conducted on hepatocellular carcinoma (HCC) and its subtypes, resulting in all significant pathways found being cancer-associated. Now, using the TCGA data stored in Google Cloud's BigQuery, we created a pipeline to apply PoTRA to other cancer data sets and see how well it cross-applies to other cancers. The results show that even though some modification may need to be made to adapt to other datasets, many significant pathways were found for both HCC and breast cancer.
ContributorsMahesh, Sunny Nishant (Author) / Valentin, Dinu (Thesis director) / Liu, Li (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
The spread of fake news (rumors) has been a growing problem on the internet in the past few years due to the increase of social media services. People share fake news articles on social media sometimes without knowing that those articles contain false information. Not knowing whether an article is fake or real is a problem because it causes social media news to lose credibility. Prior research on fake news has focused on how to detect fake news, but efforts towards controlling fake news articles on the internet are still facing challenges. Some of these challenges include; it is hard to collect large sets of fake news data, it is hard to collect locations of people who are spreading fake news, and it is difficult to study the geographic distribution of fake news. To address these challenges, I am examining how fake news spreads in the United States (US) by developing a geographic visualization system for misinformation. I am collecting a set of fake news articles from a website called snopes.com. After collecting these articles I am extracting the keywords from each article and storing them in a file. I then use the stored keywords to search on Twitter in order to find out the locations of users who spread the rumors. Finally, I mark those locations on a map in order to show the geographic distribution of fake news. Having access to large sets of fake news data, knowing the locations of people who are spreading fake news, and being able to understand the geographic distribution of fake news will help in the efforts towards addressing the fake news problem on the internet by providing target areas.
ContributorsNgweta, Lilian Mathias (Author) / Liu, Huan (Thesis director) / Wu, Liang (Committee member) / Software Engineering (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Imagining Climate (www.imaginingclimate.com) is a social media project that gauges how the public thinks about climate change in their community. Users will view climate data from 2017, view projected data for 2050, and then be given a prompt to imagine what the future looks like to them and write a short narrative story about their vision. Imagining Climate hopes to provide a public source of data for all and use imaginative writing to help users understand how other members of their communities think about climate change.
ContributorsLeung, Ellery Hermes (Author) / Popova, Laura (Thesis director) / Tarrant, Philip (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Prostate cancer is the second most common kind of cancer in men. Fortunately, it has a 99% survival rate. To achieve such a survival rate, a variety of aggressive therapies are used to treat prostate cancers that are caught early. Androgen deprivation therapy (ADT) is a therapy that is given in cycles to patients. This study attempted to analyze what factors in a group of 79 patients caused them to stick with or discontinue the treatment. This was done using naïve Bayes classification, a machine-learning algorithm. The usage of this algorithm identified high testosterone as an indicator of a patient persevering with the treatment, but failed to produce statistically significant high rates of prediction.
ContributorsMillea, Timothy Michael (Author) / Kostelich, Eric (Thesis director) / Kuang, Yang (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Big Data Network Analysis of Genetic Variation and Gene Expression in Individuals with Breast Cancer
Description
The advent of big data analytics tools and frameworks has allowed for a plethora of new approaches to research and analysis, making data sets that were previously too large or complex more accessible and providing methods to collect, store, and investigate non-traditional data. These tools are starting to be applied in more creative ways, and are being used to improve upon traditional computation methods through distributed computing. Statistical analysis of expression quantitative trait loci (eQTL) data has classically been performed using the open source tool PLINK - which runs on high performance computing (HPC) systems. However, progress has been made in running the statistical analysis in the ecosystem of the big data framework Hadoop, resulting in decreased run time, reduced storage footprint, reduced job micromanagement and increased data accessibility. Now that the data can be more readily manipulated, analyzed and accessed, there are opportunities to use the modularity and power of Hadoop to further process the data. This project focuses on adding a component to the data pipeline that will perform graph analysis on the data. This will provide more insight into the relation between various genetic differences in individuals with breast cancer, and the resulting variation - if any - in gene expression. Further, the investigation will look to see if there is anything to be garnered from a perspective shift; applying tools used in classical networking contexts (such as the Internet) to genetically derived networks.
ContributorsRandall, Jacob Christopher (Author) / Buetow, Kenneth (Thesis director) / Meuth, Ryan (Committee member) / Almalih, Sara (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Molecular pathology makes use of estimates of tumor content (tumor percentage) for pre-analytic and analytic purposes, such as molecular oncology testing, massive parallel sequencing, or next-generation sequencing (NGS), assessment of sample acceptability, accurate quantitation of variants, assessment of copy number changes (among other applications), determination of specimen viability for testing (since many assays require a minimum tumor content to report variants at the limit of detection) may all be improved with more accurate and reproducible estimates of tumor content. Currently, tumor percentages of samples submitted for molecular testing are estimated by visual examination of Hematoxylin and Eosin (H&E) stained tissue slides under the microscope by pathologists. These estimations can be automated, expedited, and rendered more accurate by applying machine learning methods on digital whole slide images (WSI).
ContributorsCirelli, Claire (Author) / Yang, Yezhou (Thesis director) / Yalim, Jason (Committee member) / Velu, Priya (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-05
Description
The Difference Engine at Arizona State University developed the Women’s Power and Influence Index (WPI) in order to combat the systemic inequality faced by women in the workplace. It aims to analyze data, such as Equal Employment Opportunity data, from various Fortune 500 companies to provide a measure of workplace inequality as well as encourage these institutions to adopt more equitable policies. By rating companies based on what truly matters to women, ASU’s Difference Engine hopes to help both women in existing career paths as well as women seeking a new career or position in companies.
However, in order for the WPI to become a relevant scoring metric of gender equality within the workplace, we must raise awareness about the issue of gender equality and of the index itself. By raising awareness about gender inequality as well as inspiring companies to further equality within their workplaces, the WPI will serve to have an integral role in increasing gender equality in the workplace. Our approach for raising awareness utilizes two different strategies: (1) establishing a new version of the WPI website that is both informative and aesthetically pleasing and (2) generating social media content on TikTok that appeal to a variety of audiences and introduce them to the WPI and our mission.
ContributorsTieu, Lienna (Author) / Howard, Brooke (Co-author) / Thomas, Elisa (Co-author) / Zaffar, Ehsan (Thesis director) / Gel, Esma (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of Geographical Sciences and Urban Planning (Contributor)
Created2022-05
Description
SparkUp! is a solution that was created by Jose Montes and Ninad Kulkarni in September of 2021. The pair noticed a few needs that they could help solve within the ASU community. Due to the Covid-19 pandemic, the average students' college experience was completely uprooted and replaced with asynchronous learning and interactions which made it difficult for students to engage with other fellow students and make new friends. This also caused students to develop sedentary lifestyles since they no longer had to walk to campus, and they developed a routine of staying confined to their dorms throughout the day.
SparkUp! is a Social Media app concept that solves these issues by connecting ASU students with other fellow students by helping them engage with one another in outdoor physical activities. Members can create and host their own hiking, cycling, kayaking, or other outdoor activity and they can set them for private or open use. Users can request to join an event by RSVPing through the app, and they also can connect with their new connections by utilizing the social media aspect of the app. Lastly, the app also tracks and maintains activity metrics such as miles traveled, steps taken, and overall time spent engaging in an activity.
Through the needs discovery phase which took part from September-December 2021, the solutions that SparkUp! offers were validated. This prompted further analysis which led to an overall PESTLE analysis of SparkUp!’s overall potential ecosystem, the creation of a marketing strategy and the creation of an Alpha version of the app so that potential users could test the initial designs of the concept. This testing was done during April of 2022 which is aiding in gathering the data necessary to create a Minimal Value Product for future release.
ContributorsKulkarni, Ninad (Author) / Montes, Jose (Co-author) / Byrne, Jared (Thesis director) / Satpathy, Asish (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-05