Browsing by Author "BioHealth Informatics, School of Informatics and Computing"
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Item A Putative long-range RNA-RNA interaction between ORF8 and Spike of SARS-CoV-2(Public Library of Science, 2022-09-01) Omoru, Okiemute Beatrice; Pereira, Filipe; Janga, Sarath Chandra; Manzourolajdad, Amirhossein; BioHealth Informatics, School of Informatics and ComputingSARS-CoV-2 has affected people worldwide as the causative agent of COVID-19. The virus is related to the highly lethal SARS-CoV-1 responsible for the 2002-2003 SARS outbreak in Asia. Research is ongoing to understand why both viruses have different spreading capacities and mortality rates. Like other beta coronaviruses, RNA-RNA interactions occur between different parts of the viral genomic RNA, resulting in discontinuous transcription and production of various sub-genomic RNAs. These sub-genomic RNAs are then translated into other viral proteins. In this work, we performed a comparative analysis for novel long-range RNA-RNA interactions that may involve the Spike region. Comparing in-silico fragment-based predictions between reference sequences of SARS-CoV-1 and SARS-CoV-2 revealed several predictions amongst which a thermodynamically stable long-range RNA-RNA interaction between (23660-23703 Spike) and (28025-28060 ORF8) unique to SARS-CoV-2 was observed. The patterns of sequence variation using data gathered worldwide further supported the predicted stability of the sub-interacting region (23679-23690 Spike) and (28031-28042 ORF8). Such RNA-RNA interactions can potentially impact viral life cycle including sub-genomic RNA production rates.Item ADGRG1 enriches for functional human hematopoietic stem cells following ex vivo expansion-induced mitochondrial oxidative stress(The American Society for Clinical Investigation, 2021) Chen, Yandan; Fang, Shuyi; Ding, Qingwei; Jiang, Rongzhen; He, Jiefeng; Wang, Qin; Jin, Yuting; Huang, Xinxin; Liu, Sheng; Capitano, Maegan L.; Trinh, Thao; Teng, Yincheng; Meng, Qingyou; Wan, Jun; Broxmeyer, Hal E.; Guo, Bin; BioHealth Informatics, School of Informatics and ComputingThe heterogeneity of human hematopoietic stem cells (HSCs) and hematopoietic progenitor cells (HPCs) under stress conditions such as ex vivo expansion is poorly understood. Here, we report that the frequencies of SCID-repopulating cells were greatly decreased in cord blood (CB) CD34+ HSCs and HPCs upon ex vivo culturing. Transcriptomic analysis and metabolic profiling demonstrated that mitochondrial oxidative stress of human CB HSCs and HPCs notably increased, along with loss of stemness. Limiting dilution analysis revealed that functional human HSCs were enriched in cell populations with low levels of mitochondrial ROS (mitoROS) during ex vivo culturing. Using single-cell RNA-Seq analysis of the mitoROS low cell population, we demonstrated that functional HSCs were substantially enriched in the adhesion GPCR G1-positive (ADGRG1+) population of CD34+CD133+ CB cells upon ex vivo expansion stress. Gene set enrichment analysis revealed that HSC signature genes including MSI2 and MLLT3 were enriched in CD34+CD133+ADGRG1+ CB HSCs. Our study reveals that ADGRG1 enriches for functional human HSCs under oxidative stress during ex vivo culturing, which can be a reliable target for drug screening of agonists of HSC expansion.Item Advanced Functions Embedded in the Second Version of Database, Global Evaluation of SARS-CoV-2/hCoV-19 Sequences 2(Frontiers Media, 2022-04-11) Li, Kailing; Wang, Audrey K.Y.; Liu, Sheng; Fang, Shuyi; Lu, Alex Z.; Shen, Jikui; Yang, Lei; Hu, Chang-Deng; Yang, Kai; Wan, Jun; BioHealth Informatics, School of Informatics and ComputingThe Global Evaluation of SARS-CoV-2/hCoV-19 Sequences 2 (GESS v2 https://shiny.ph.iu.edu/GESS_v2/) is an updated version of GESS, which has offered a handy query platform to analyze single-nucleotide variants (SNVs) on millions of high coverages and high-quality severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) complete genomes provided by the Global Initiative on Sharing Avian Influenza Data (GISAID). Including the tools in the first version, the GESS v2 is embedded with new functions, which allow users to search SNVs, given the viral nucleotide or amino acid sequence. The GESS v2 helps users to identify SNVs or SARS-CoV-2 lineages enriched in countries of user's interest and show the migration path of a selected lineage on a world map during specific time periods chosen by the users. In addition, the GESS v2 can recognize the dynamic variations of newly emerging SNVs in each month to help users monitor SNVs, which will potentially become dominant soon. More importantly, multiple sets of analyzed results about SNVs can be downloaded directly from the GESS v2 by which users can conduct their own independent research. With these significant updates, the GESS v2 will continue to serve as a public open platform for researchers to explore SARS-CoV-2 evolutionary patterns from the perspectives of the prevalence and impact of SNVs.Item AI recognition of patient race in medical imaging: a modelling study(Elsevier, 2022-06) Gichoya, Judy Wawira; Banerjee, Imon; Bhimireddy, Ananth Reddy; Burns, John L.; Celi, Leo Anthony; Chen, Li-Ching; Correa, Ramon; Dullerud, Natalie; Ghassemi, Marzyeh; Huang, Shih-Cheng; Kuo, Po-Chih; Lungren, Matthew P.; Palmer, Lyle J.; Price, Brandon J.; Purkayastha, Saptarshi; Pyrros, Ayis T.; Oakden-Rayner, Lauren; Okechukwu, Chima; Seyyed-Kalantari, Laleh; Trivedi, Hari; Wang, Ryan; Zaiman, Zachary; Zhang, Haoran; BioHealth Informatics, School of Informatics and ComputingBackground Previous studies in medical imaging have shown disparate abilities of artificial intelligence (AI) to detect a person's race, yet there is no known correlation for race on medical imaging that would be obvious to human experts when interpreting the images. We aimed to conduct a comprehensive evaluation of the ability of AI to recognise a patient's racial identity from medical images. Methods Using private (Emory CXR, Emory Chest CT, Emory Cervical Spine, and Emory Mammogram) and public (MIMIC-CXR, CheXpert, National Lung Cancer Screening Trial, RSNA Pulmonary Embolism CT, and Digital Hand Atlas) datasets, we evaluated, first, performance quantification of deep learning models in detecting race from medical images, including the ability of these models to generalise to external environments and across multiple imaging modalities. Second, we assessed possible confounding of anatomic and phenotypic population features by assessing the ability of these hypothesised confounders to detect race in isolation using regression models, and by re-evaluating the deep learning models by testing them on datasets stratified by these hypothesised confounding variables. Last, by exploring the effect of image corruptions on model performance, we investigated the underlying mechanism by which AI models can recognise race. Findings In our study, we show that standard AI deep learning models can be trained to predict race from medical images with high performance across multiple imaging modalities, which was sustained under external validation conditions (x-ray imaging [area under the receiver operating characteristics curve (AUC) range 0·91-0·99], CT chest imaging [0·87-0·96], and mammography [0·81]). We also showed that this detection is not due to proxies or imaging-related surrogate covariates for race (eg, performance of possible confounders: body-mass index [AUC 0·55], disease distribution [0·61], and breast density [0·61]). Finally, we provide evidence to show that the ability of AI deep learning models persisted over all anatomical regions and frequency spectrums of the images, suggesting the efforts to control this behaviour when it is undesirable will be challenging and demand further study. Interpretation The results from our study emphasise that the ability of AI deep learning models to predict self-reported race is itself not the issue of importance. However, our finding that AI can accurately predict self-reported race, even from corrupted, cropped, and noised medical images, often when clinical experts cannot, creates an enormous risk for all model deployments in medical imaging. Funding National Institute of Biomedical Imaging and Bioengineering, MIDRC grant of National Institutes of Health, US National Science Foundation, National Library of Medicine of the National Institutes of Health, and Taiwan Ministry of Science and Technology.Item An answer recommendation framework for an online cancer community forum(Springer Nature, 2023-05-15) Athira, B.; Idicula, Sumam Mary; Jones, Josette; Kulanthaivel, Anand; BioHealth Informatics, School of Informatics and ComputingHealth community forums are a kind of online platform to discuss various matters related to management of illness. People are increasingly searching for answers online, particularly when they are diagnosed with cancer like life-threatening diseases. People seek suggestions or advice through these platforms to make decisions during their treatments. However, locating the correct information or similar people is often a great challenge for them. In this scenario, this paper proposes an answer recommendation system in an online breast cancer community forum that provide guidance and valuable references to users while making decisions. The answer is the summary of already discussed topic in the forum, so that they do not need to go through all the answer posts which spans over multiple pages or initiate a thread once again. There are three phases for the answer recommendation system, including query similarity model to retrieve the past similar query, query-answer pair generation and answer recommendation. Query similarity model is employed by a Siamese network with Bi-LSTM architecture which could achieve an F1-score of 85.5%. Also, the paper shows the efficacy of transfer learning technique to generalize the model well in our breast cancer query-query pair data set. The query-answer pairs are generated by an extractive summarization technique that is based on an optimization algorithm. The effectiveness of the generated summary is evaluated based on a manually generated summary, and the result shows a ROUGE-1 score of 49%.Item Analyzing Patterns of Literature-Based Phenotyping Definitions for Text Mining Applications(IEEE, 2018-06) Binkheder, Samar; Wu, Heng-Yi; Quinney, Sara; Li, Lang; BioHealth Informatics, School of Informatics and ComputingPhenotyping definitions are widely used in observational studies that utilize population data from Electronic Health Records (EHRs). Biomedical text mining supports biomedical knowledge discovery. Therefore, we believe that mining phenotyping definitions from the literature can support EHR-based clinical research. However, information about these definitions presented in the literature is inconsistent, diverse, and unknown, especially for text mining usage. Therefore, we aim to analyze patterns of phenotyping definitions as a first step toward developing a text mining application to improve phenotype definition. A set random of observational studies was used for this analysis. Term frequency-inverse document frequency (TF-IDF) and Term Frequency (TF) were used to rank the terms in the 3958 sentences. Finally, we present preliminary results analyzing phenotyping definitions patterns.Item Annotating and Detecting Topics in Social Media Forum and Modelling the Annotation to Derive Directions-A Case Study(Research Square, 2021) B., Athira; Jones, Josette; Idicula, Sumam Mary; Kulanthaivel, Anand; Zhang, Enming; BioHealth Informatics, School of Informatics and ComputingThe widespread influence of social media impacts every aspect of life, including the healthcare sector. Although medics and health professionals are the final decision makers, the advice and recommendations obtained from fellow patients are significant. In this context, the present paper explores the topics of discussion posted by breast cancer patients and survivors on online forums. The study examines an online forum, Breastcancer.org, maps the discussion entries to several topics, and proposes a machine learning model based on a classification algorithm to characterize the topics. To explore the topics of breast cancer patients and survivors, approximately 1000 posts are selected and manually labeled with annotations. In contrast, millions of posts are available to build the labels. A semi-supervised learning technique is used to build the labels for the unlabeled data; hence, the large data are classified using a deep learning algorithm. The deep learning algorithm BiLSTM with BERT word embedding technique provided a better f1-score of 79.5%. This method is able to classify the following topics: medication reviews, clinician knowledge, various treatment options, seeking and providing support, diagnostic procedures, financial issues and implications for everyday life. What matters the most for the patients is coping with everyday living as well as seeking and providing emotional and informational support. The approach and findings show the potential of studying social media to provide insight into patients' experiences with cancer like critical health problems.Item Artificial Intelligence for Global Health: Learning From a Decade of Digital Transformation in Health Care(arXiv, 2020) Mathur, Varoon; Purkayastha, Saptarshi; Gichoya, Judy Wawira; BioHealth Informatics, School of Informatics and ComputingThe health needs of those living in resource-limited settings are a vastly overlooked and understudied area in the intersection of machine learning (ML) and health care. While the use of ML in health care is more recently popularized over the last few years from the advancement of deep learning, low-and-middle income countries (LMICs) have already been undergoing a digital transformation of their own in health care over the last decade, leapfrogging milestones due to the adoption of mobile health (mHealth). With the introduction of new technologies, it is common to start afresh with a top-down approach, and implement these technologies in isolation, leading to lack of use and a waste of resources. In this paper, we outline the necessary considerations both from the perspective of current gaps in research, as well as from the lived experiences of health care professionals in resource-limited settings. We also outline briefly several key components of successful implementation and deployment of technologies within health systems in LMICs, including technical and cultural considerations in the development process relevant to the building of machine learning solutions. We then draw on these experiences to address where key opportunities for impact exist in resource-limited settings, and where AI/ML can provide the most benefit.Item The association of COMT genotype with buproprion treatment response in the treatment of major depressive disorder(Wiley, 2020-05-27) Fawver, Jay; Flanagan, Mindy; Smith, Thomas; Drouin, Michelle; Mirro, Michael; BioHealth Informatics, School of Informatics and ComputingBackground Pharmacodynamics and pharmacogenetics are being explored in pharmacological treatment response for major depressive disorder (MDD). Interactions between genotype and treatment response may be dose dependent. In this study, we examined whether MDD patients with Met/Met, Met/Val, and Val/Val COMT genotypes differed in their response to bupropion in terms of depression scores. Methods This study utilized a convenience sample of 241 adult outpatients (≥18 years) who met DSM‐5 criteria for MDD and had visits at a Midwest psychopharmacology clinic between February 2016 and January 2017. Exclusion criteria included various comorbid medical, neurological, and psychiatric conditions and current use of benzodiazepines or narcotics. Participants completed genetic testing and the 9 question patient‐rated Patient Health Questionnaire (PHQ‐9) at each clinic visit (M = 3.8 visits, SD = 1.5) and were prescribed bupropion or another antidepressant drug. All participants were adherent to pharmacotherapy treatment recommendations for >2 months following genetic testing. Results Participants were mostly Caucasian (85.9%) outpatients (154 female and 87 male) who were 44.5 years old, on average (SD = 17.9). For Val carriers, high bupropion doses resulted in significantly lower PHQ‐9 scores than no bupropion (t(868) = 5.04, p < .001) or low dose bupropion (t(868) = 3.29, p = .001). Val carriers differed significantly from Met/Met patients in response to high dose bupropion (t(868) = −2.03, p = .04), but not to low dose bupropion. Conclusion High‐dose bupropion is beneficial for MDD patients with Met/Val or Val/Val COMT genotypes, but not for patients with Met/Met genotype. Prospective studies are necessary to replicate this pharmacodynamic relationship between bupropion and COMT genotypes and explore economic and clinical outcomes.Item AuthN-AuthZ: Integrated, User-Friendly and Privacy-Preserving Authentication and Authorization(IEEE, 2020-10) Phillips, Tyler; Yu, Xiaoyuan; Haakenson, Brandon; Goyal, Shreya; Zou, Xukai; Purkayastha, Saptarshi; Wu, Huanmei; BioHealth Informatics, School of Informatics and ComputingIn this paper, we propose a novel, privacy-preserving, and integrated authentication and authorization scheme (dubbed as AuthN-AuthZ). The proposed scheme can address both the usability and privacy issues often posed by authentication through use of privacy-preserving Biometric-Capsule-based authentication. Each Biometric-Capsule encapsulates a user's biometric template as well as their role within a hierarchical Role-based Access Control model. As a result, AuthN-AuthZ provides novel efficiency by performing both authentication and authorization simultaneously in a single operation. To the best of our knowledge, our scheme's integrated AuthN-AuthZ operation is the first of its kind. The proposed scheme is flexible in design and allows for the secure use of robust deep learning techniques, such as the recently proposed and current state-of-the-art facial feature representation method, ArcFace. We conduct extensive experiments to demonstrate the robust performance of the proposed scheme and its AuthN-AuthZ operation.