Data Science Commons^™

Analyzing Tortuosity In Patterns Formed By Colonies Of Embryonic Stem Cells Using Topological Data Analysis, Jackie Driscoll

Master's Theses

Pluripotent stem cells have been observed to segregate into Turing-like patterns during the early stages of Dox-inducible hiPSC differentiation. In this thesis, we de- velop a tool to quantify the tortuosity in the patterns formed by colonies of pluripo- tent stem cells using methods from topological data analysis. We use clustering techniques and the mapper algorithm to create simplicial complexes representing samples of cells and detail a method of evaluating the tortuosity of these complexes. We use the resulting persistence landscapes and their associated norms to evaluate experimental data and simulated data from an agent based model. This thesis finds …

The Sociolinguistics Of Code-Switching In Hong Kong’S Digital Landscape: A Mixed-Methods Exploration Of Cantonese-English Alternation Patterns On Whatsapp, Wilkinson Daniel Wong Gonzales, Yuen Man Tsang

Journal of English and Applied Linguistics

This paper examines the prevalence of Cantonese-English code mixing in Hong Kong through an under-researched digital medium. Prior research on this code-alternation practice has often been limited to exploring either the social or linguistic constraints of code-switching in spoken or written communication. Our study takes a holistic approach to analyzing code-switching in a hybrid medium that exhibits features of both spoken and written discourse. We specifically analyze the code-switching patterns of 24 undergraduates from a Hong Kong university on WhatsApp and examine how both social and linguistic factors potentially constrain these patterns. Utilizing a self-compiled sociolinguistic corpus as well as …

Rapid-Query For Fast Identity By Descent Search And Genealogical Analysis, Yuan Wei, Ardalan Naseri, Degui Zhi, Shaojie Zhang

Faculty, Staff and Student Publications

MOTIVATION: Due to the rapid growth of the genetic database size, genealogical search, a process of inferring familial relatedness by identifying DNA matches, has become a viable approach to help individuals finding missing family members or law enforcement agencies locating suspects. A fast and accurate method is needed to search an out-of-database individual against millions of individuals. Most existing approaches only offer all-versus-all within panel match. Some prototype algorithms offer one-versus-all query from out-of-panel individual, but they do not tolerate errors.

RESULTS: A new method, random projection-based identity-by-descent (IBD) detection (RaPID) query, is introduced to make fast genealogical search possible. …

Population Modeling With Machine Learning Can Enhance Measures Of Mental Health - Open-Data Replication, Ty Easley, Ruiqi Chen, Kayla Hannon, Rosie Dutt, Janine Bijsterbosch

Statistical and Data Sciences: Faculty Publications

Efforts to predict trait phenotypes based on functional MRI data from large cohorts have been hampered by low prediction accuracy and/or small effect sizes. Although these findings are highly replicable, the small effect sizes are somewhat surprising given the presumed brain basis of phenotypic traits such as neuroticism and fluid intelligence. We aim to replicate previous work and additionally test multiple data manipulations that may improve prediction accuracy by addressing data pollution challenges. Specifically, we added additional fMRI features, averaged the target phenotype across multiple measurements to obtain more accurate estimates of the underlying trait, balanced the target phenotype's distribution …

A Guide To The Brain Initiative Cell Census Network Data Ecosystem, Michael Hawrylycz, Maryann E Martone, Giorgio A Ascoli, Jan G Bjaalie, Hong-Wei Dong, Satrajit S Ghosh, Jesse Gillis, Ronna Hertzano, David R Haynor, Patrick R Hof, Yongsoo Kim, Ed Lein, Yufeng Liu, Jeremy A Miller, Partha P Mitra, Eran Mukamel, Lydia Ng, David Osumi-Sutherland, Hanchuan Peng, Patrick L Ray, Raymond Sanchez, Aviv Regev, Alex Ropelewski, Richard H Scheuermann, Shawn Zheng Kai Tan, Carol L Thompson, Timothy Tickle, Hagen Tilgner, Merina Varghese, Brock Wester, Owen White, Hongkui Zeng, Brian Aevermann, David Allemang, Seth Ament, Thomas L Athey, Cody Baker, Katherine S Baker, Pamela M Baker, Anita Bandrowski, Samik Banerjee, Prajal Bishwakarma, Ambrose Carr, Min Chen, Roni Choudhury, Jonah Cool, Heather Creasy, Florence D'Orazi, Kylee Degatano, Benjamin Dichter, Song-Lin Ding, Tim Dolbeare, Joseph R Ecker, Rongxin Fang, Jean-Christophe Fillion-Robin, Timothy P Fliss, James Gee, Tom Gillespie, Nathan Gouwens, Guo-Qiang Zhang, Yaroslav O Halchenko, Nomi L Harris, Brian R Herb, Houri Hintiryan, Gregory Hood, Sam Horvath, Bingxing Huo, Dorota Jarecka, Shengdian Jiang, Farzaneh Khajouei, Elizabeth A Kiernan, Huseyin Kir, Lauren Kruse, Changkyu Lee, Boudewijn Lelieveldt, Yang Li, Hanqing Liu, Lijuan Liu, Anup Markuhar, James Mathews, Kaylee L Mathews, Chris Mezias, Michael I Miller, Tyler Mollenkopf, Shoaib Mufti, Christopher J Mungall, Joshua Orvis, Maja A Puchades, Lei Qu, Joseph P Receveur, Bing Ren, Nathan Sjoquist, Brian Staats, Daniel Tward, Cindy T J Van Velthoven, Quanxin Wang, Fangming Xie, Hua Xu, Zizhen Yao, Zhixi Yun, Yun Renee Zhang, W Jim Zheng, Brian Zingg

Faculty, Staff and Student Publications

Characterizing cellular diversity at different levels of biological organization and across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also essential to manipulate cell types in controlled ways and to understand their variation and vulnerability in brain disorders. The BRAIN Initiative Cell Census Network (BICCN) is an integrated network of data-generating centers, data archives, and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain with demonstration of prototype feasibility for human and nonhuman …

Community Perspectives On Ai/Ml And Health Equity: Aim-Ahead Nationwide Stakeholder Listening Sessions, Jamboor K Vishwanatha, Allison Christian, Usha Sambamoorthi, Erika L Thompson, Katie Stinson, Toufeeq Ahmed Syed

Faculty, Staff and Student Publications

Artificial intelligence and machine learning (AI/ML) tools have the potential to improve health equity. However, many historically underrepresented communities have not been engaged in AI/ML training, research, and infrastructure development. Therefore, AIM-AHEAD (Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity) seeks to increase participation and engagement of researchers and communities through mutually beneficial partnerships. The purpose of this paper is to summarize feedback from listening sessions conducted by the AIM-AHEAD Coordinating Center in February 2022, titled the "AIM-AHEAD Community Building Convention (ACBC)." A total of six listening sessions were held over three days. A total of 977 …

A Novel Integrated Approach To Predicting Cancer Immunotherapy Efficacy, Ruihan Luo, Jacqueline Chyr, Jianguo Wen, Yanfei Wang, Weiling Zhao, Xiaobo Zhou

Faculty, Staff and Student Publications

Immunotherapies have revolutionized cancer treatment modalities; however, predicting clinical response accurately and reliably remains challenging. Neoantigen load is considered as a fundamental genetic determinant of therapeutic response. However, only a few predicted neoantigens are highly immunogenic, with little focus on intratumor heterogeneity (ITH) in the neoantigen landscape and its link with different features in the tumor microenvironment. To address this issue, we comprehensively characterized neoantigens arising from nonsynonymous mutations and gene fusions in lung cancer and melanoma. We developed a composite NEO2IS to characterize interplays between cancer and CD8+ T-cell populations. NEO2IS improved prediction accuracy of patient responses to immune-checkpoint …

Inferring Personalized Treatment Effect Of Antihypertensives On Alzheimer's Disease Using Deep Learning, Pulakesh Upadhyaya, Yaobin Ling, Luyao Chen, Yejin Kim, Xiaoqian Jiang

Faculty, Staff and Student Publications

Alzheimer's disease (AD) is one of the leading causes of death in the United States, especially among the elderly. Recent studies have shown how hypertension is related to cognitive decline in elderly patients, which in turn leads to increased mortality as well as morbidity. There have been various studies that have looked at the effect of antihypertensive drugs in reducing cognitive decline, and their results have proved inconclusive. However, most of these studies assume the treatment effect is similar for all patients, thus considering only the average treatment effects of antihypertensive drugs. In this paper, we assume that the effect …

Predicting The Risk Of Alzheimer's Disease And Related Dementia In Patients With Mild Cognitive Impairment Using A Semi-Competing Risk Approach, Zhaoyi Chen, Yuchen Yang, Dazheng Zhang, Jingchuan Guo, Yi Guo, Xia Hu, Yong Chen, Jiang Bian

Faculty, Staff and Student Publications

Alzheimer's disease (AD) and AD-related dementias (AD/ADRD) are a group of progressive neurodegenerative diseases. The progression of AD can be conceptualized as a continuum in which patients progress from normal cognition to preclinical AD (i.e., no symptoms but biological changes in the brain) to mild cognitive impairment (MCI) due to AD (i.e., mild symptoms but not interfere with daily activities), followed by increasing severity of dementia due to AD. Early detection and prediction models for the transition of MCI to AD/ADRD are needed, and efforts have been made to build predictions of MCI conversion to AD/ADRD. However, most existing studies …

Comparative Analyses Define Differences Between Bhd-Associated Renal Tumour And Sporadic Chromophobe Renal Cell Carcinoma, Ryosuke Jikuya, Todd A Johnson, Kazuhiro Maejima, Jisong An, Young-Seok Ju, Hwajin Lee, Kyungsik Ha, Woojeung Song, Youngwook Kim, Yuki Okawa, Shota Sasagawa, Yuki Kanazashi, Masashi Fujita, Seiya Imoto, Taku Mitome, Shinji Ohtake, Go Noguchi, Sachi Kawaura, Yasuhiro Iribe, Kota Aomori, Tomoyuki Tatenuma, Mitsuru Komeya, Hiroki Ito, Yusuke Ito, Kentaro Muraoka, Mitsuko Furuya, Ikuma Kato, Satoshi Fujii, Haruka Hamanoue, Tomohiko Tamura, Masaya Baba, Toshio Suda, Tatsuhiko Kodama, Kazuhide Makiyama, Masahiro Yao, Brian M Shuch, Christopher J Ricketts, Laura S Schmidt, W Marston Linehan, Hidewaki Nakagawa, Hisashi Hasumi

Faculty, Staff and Student Publications

BACKGROUND: Birt-Hogg-Dubé (BHD) syndrome, caused by germline alteration of folliculin (FLCN) gene, develops hybrid oncocytic/chromophobe tumour (HOCT) and chromophobe renal cell carcinoma (ChRCC), whereas sporadic ChRCC does not harbor FLCN alteration. To date, molecular characteristics of these similar histological types of tumours have been incompletely elucidated.

METHODS: To elucidate renal tumourigenesis of BHD-associated renal tumours and sporadic renal tumours, we conducted whole genome sequencing (WGS) and RNA-sequencing (RNA-seq) of sixteen BHD-associated renal tumours from nine unrelated BHD patients, twenty-one sporadic ChRCCs and seven sporadic oncocytomas. We then compared somatic mutation profiles with FLCN variants and RNA expression profiles between BHD-associated …

The Integrative Studies On The Functional A-To-I Rna Editing Events In Human Cancers, Sijia Wu, Zhiwei Fan, Pora Kim, Liyu Huang, Xiaobo Zhou

Faculty, Staff and Student Publications

Adenosine-to-inosine (A-to-I) RNA editing, constituting nearly 90% of all RNA editing events in humans, has been reported to contribute to the tumorigenesis in diverse cancers. However, the comprehensive map for functional A-to-I RNA editing events in cancers is still insufficient. To fill this gap, we systematically and intensively analyzed multiple tumorigenic mechanisms of A-to-I RNA editing events in samples across 33 cancer types from The Cancer Genome Atlas. For individual candidate among ∼ 1,500,000 quantified RNA editing events, we performed diverse types of downstream functional annotations. Finally, we identified 24,236 potentially functional A-to-I RNA editing events, including the cases …

Statistical And Biological Analyses Of Acoustic Signals In Estrildid Finches, Moises Rivera

Dissertations, Theses, and Capstone Projects

Acoustic communication is a process that involves auditory perception and signal processing. Discrimination and recognition further require cognitive processes and supporting mechanisms in order to successfully identify and appropriately respond to signal senders. Although acoustic communication is common across birds, classical research has largely disregarded the perceptual abilities of perinatal altricial taxa. Chapter 1 reviews the literature of perinatal acoustic stimulation in birds, highlighting the disproportionate focus on precocial birds (e.g., chickens, ducks, quails). The long-held belief that altricial birds were incapable of acoustic perception in ovo was only recently overturned, as researchers began to find behavioral and physiological evidence …

Phantom Shootings, Allan Ambris

Dissertations, Theses, and Capstone Projects

This capstone is a website designed to critique NYC Open Data reporting with respect to shootings through a series of visualizations and discoveries. The NYPD Shooting Incidents datasets (Historic and Year to Date) introduce themselves to the user by claiming to be a “list of every shooting incident that occurred in NYC.” The supplied documentation reveals that this is not the case.

After understanding the supporting materials, there are still undisclosed truths. My exploration of the data revealed that a single victim may be represented across multiple entries. Additionally, multiple victims may be represented by a single entry. It is …

Improving The Efficiency Of Exponential Ratio-Type Estimator For Population Median: A Calibration Weight Adjustment Approach, Mathew J. Iseh, Kufre J. Bassey

CBN Journal of Applied Statistics (JAS)

This paper modifies the Bahl and Tuteja exponential ratio-type estimator for population median under simple random and stratified sampling schemes using calibration weight adjustment technique with supplementary information to vary the stratum weights. The bias and mean square error of the modified estimator were obtained up to the second-order approximation, which satisfies the necessary conditions for efficiency. The findings show that the new estimator surpasses existing estimators in efficiency gain. This suggests the appropriateness of calibration weight modification in boosting the efficiency of a population parameter estimator under stratified random sampling especially where the population parameter of the auxiliary variable …

Mosaic: Spatially-Multiplexed Edge Ai Optimization Over Multiple Concurrent Video Sensing Streams, Ila Gokarn, Hemanth Sabbella, Yigong Hu, Tarek Abdelzaher, Archan Misra

Research Collection School Of Computing and Information Systems

Sustaining high fidelity and high throughput of perception tasks over vision sensor streams on edge devices remains a formidable challenge, especially given the continuing increase in image sizes (e.g., generated by 4K cameras) and complexity of DNN models. One promising approach involves criticality-aware processing, where the computation is directed selectively to "critical" portions of individual image frames. We introduce MOSAIC, a novel system for such criticality-aware concurrent processing of multiple vision sensing streams that provides a multiplicative increase in the achievable throughput with negligible loss in perception fidelity. MOSAIC determines critical regions from images received from multiple vision …

Trustworthy Machine Learning Through The Lens Of Privacy And Security, Thi Kim Phung Lai

Dissertations

Nowadays, machine learning (ML) becomes ubiquitous and it is transforming society. However, there are still many incidents caused by ML-based systems when ML is deployed in real-world scenarios. Therefore, to allow wide adoption of ML in the real world, especially in critical applications such as healthcare, finance, etc., it is crucial to develop ML models that are not only accurate but also trustworthy (e.g., explainable, privacy-preserving, secure, and robust). Achieving trustworthy ML with different machine learning paradigms (e.g., deep learning, centralized learning, federated learning, etc.), and application domains (e.g., computer vision, natural language, human study, malware systems, etc.) is challenging, …

Ai Approaches To Understand Human Deceptions, Perceptions, And Perspectives In Social Media, Chih-Yuan Li

Dissertations

Social media platforms have created virtual space for sharing user generated information, connecting, and interacting among users. However, there are research and societal challenges: 1) The users are generating and sharing the disinformation 2) It is difficult to understand citizens' perceptions or opinions expressed on wide variety of topics; and 3) There are overloaded information and echo chamber problems without overall understanding of the different perspectives taken by different people or groups.

This dissertation addresses these three research challenges with advanced AI and Machine Learning approaches. To address the fake news, as deceptions on the facts, this dissertation presents Machine …

Deep Hybrid Modeling Of Neuronal Dynamics Using Generative Adversarial Networks, Soheil Saghafi

Dissertations

Mechanistic modeling and machine learning methods are powerful techniques for approximating biological systems and making accurate predictions from data. However, when used in isolation these approaches suffer from distinct shortcomings: model and parameter uncertainty limit mechanistic modeling, whereas machine learning methods disregard the underlying biophysical mechanisms. This dissertation constructs Deep Hybrid Models that address these shortcomings by combining deep learning with mechanistic modeling. In particular, this dissertation uses Generative Adversarial Networks (GANs) to provide an inverse mapping of data to mechanistic models and identifies the distributions of mechanistic model parameters coherent to the data.

Chapter 1 provides background information on …

Data-Optimized Spatial Field Predictions For Robotic Adaptive Sampling: A Gaussian Process Approach, Zachary Nathan

Computer Science Senior Theses

We introduce a framework that combines Gaussian Process models, robotic sensor measurements, and sampling data to predict spatial fields. In this context, a spatial field refers to the distribution of a variable throughout a specific area, such as temperature or pH variations over the surface of a lake. Whereas existing methods tend to analyze only the particular field(s) of interest, our approach optimizes predictions through the effective use of all available data. We validated our framework on several datasets, showing that errors can decline by up to two-thirds through the inclusion of additional colocated measurements. In support of adaptive sampling, …

A Survey On Online Matching And Ad Allocation, Ryan Lee

Theses

One of the classical problems in graph theory is matching. Given an undirected graph, find a matching which is a set of edges without common vertices. In 1990s, Richard Karp, Umesh Vazirani, and Vijay Vazirani would be the first computer scientists to use matchings for online algorithms [8]. In our domain, an online algorithm operates in the online setting where a bipartite graph is given. On one side of the graph there is a set of advertisers and on the other side we have a set of impressions. During the online phase, multiple impressions will arrive and the objective of …