Life Sciences Commons^™

Overlap Matrix Completion For Predicting Drug-Associated Indications, Menhyun Yang, Huimin Luo, Yaohang Li, Fang-Xiang Wu, Jianxin Wang

Computer Science Faculty Publications

Identification of potential drug-associated indications is critical for either approved or novel drugs in drug repositioning. Current computational methods based on drug similarity and disease similarity have been developed to predict drug-disease associations. When more reliable drug- or disease-related information becomes available and is integrated, the prediction precision can be continuously improved. However, it is a challenging problem to effectively incorporate multiple types of prior information, representing different characteristics of drugs and diseases, to identify promising drug-disease associations. In this study, we propose an overlap matrix completion (OMC) for bilayer networks (OMC2) and tri-layer networks (OMC3) to predict potential drug-associated …

Enhancing Timeliness Of Drug Overdose Mortality Surveillance: A Machine Learning Approach, Patrick J. Ward, Peter J. Rock, Svetla Slavova, April M. Young, Terry L. Bunn, Ramakanth Kavuluru

Kentucky Injury Prevention and Research Center Faculty Publications

BACKGROUND: Timely data is key to effective public health responses to epidemics. Drug overdose deaths are identified in surveillance systems through ICD-10 codes present on death certificates. ICD-10 coding takes time, but free-text information is available on death certificates prior to ICD-10 coding. The objective of this study was to develop a machine learning method to classify free-text death certificates as drug overdoses to provide faster drug overdose mortality surveillance.

METHODS: Using 2017–2018 Kentucky death certificate data, free-text fields were tokenized and features were created from these tokens using natural language processing (NLP). Word, bigram, and trigram features were created …

Development Of An Autonomous Aerial Toolset For Agricultural Applications, Terrance Life

Mahurin Honors College Capstone Experience/Thesis Projects

According to the United Nations, the world population is expected to grow from its current 7 billion to 9.7 billion by the year 2050. During this time, global food demand is also expected to increase by between 59% and 98% due to the population increase, accompanied by an increasing demand for protein due to a rising standard of living throughout developing countries. [1] Meeting this increase in required food production using present agricultural practices would necessitate a similar increase in farmland; a resource which does not exist in abundance. Therefore, in order to meet growing food demands, new methods will …

9th Annual Postdoctoral Science Symposium, University Of Texas Md Anderson Cancer Center Postdoctoral Association

Annual Postdoctoral Science Symposium Abstracts

The mission of the Annual Postdoctoral Science Symposium (APSS) is to provide a platform for talented postdoctoral fellows throughout the Texas Medical Center to present their work to a wider audience. The MD Anderson Postdoctoral Association convened its inaugural Annual Postdoctoral Science Symposium (APSS) on August 4, 2011.

The APSS provides a professional venue for postdoctoral scientists to develop, clarify, and refine their research as a result of formal reviews and critiques of faculty and other postdoctoral scientists. Additionally, attendees discuss current research on a broad range of subjects while promoting academic interactions and enrichment and developing new collaborations.

Deep Machine Learning Techniques For The Detection And Classification Of Sperm Whale Bioacoustics, Peter C. Bermant, Michael M. Bronstein, Robert J. Wood, Shane Gero, David F. Gruber

Publications and Research

We implemented Machine Learning (ML) techniques to advance the study of sperm whale (Physeter macrocephalus) bioacoustics. This entailed employing Convolutional Neural Networks (CNNs) to construct an echolocation click detector designed to classify spectrograms generated from sperm whale acoustic data according to the presence or absence of a click. The click detector achieved 99.5% accuracy in classifying 650 spectrograms. The successful application of CNNs to clicks reveals the potential of future studies to train CNN-based architectures to extract finer-scale details from cetacean spectrograms. Long short-term memory and gated recurrent unit recurrent neural networks were trained to perform classification tasks, including (1) …

Iamhappy: Towards An Iot Knowledge-Based Cross-Domain Well-Being Recommendation System For Everyday Happiness, Amelia Gyrard, Amit Sheth

Kno.e.sis Publications

Nowadays, healthy lifestyle, fitness, and diet habits have become central applications in our daily life. Positive psychology such as well-being and happiness is the ultimate dream of everyday people’s feelings (even without being aware of it). Wearable devices are being increasingly employed to support well-being and fitness. Those devices produce physiological signals that are analyzed by machines to understand emotions and physical state. The Internetof Things (IoT) technology connects (wearable) devices to the Internet to easily access and process data, even using Web technologies (aka Web of Things).

We design IAMHAPPY, an innovative IoT-based well-being recommendation system to encourage every …

"Flagella Base Model" And "Flagellin Monomer", Brandon Lasalle, Rebecca Roston

3-D Printed Model Structural Files

"Flagella Base Model" and "Flagellin monomer"

Description: This is a teaching model of the proteins that make a bacterial flagella. All models are depicted in space-fill. The Flagellin monomer and the Flagella base can slot together to show protein quaternary structure and filamentous protein assembly.

Printable models are already uploaded to Shapeways.com in the MacroMolecules shop under the names "Flagella Base Model" and "Flagellin monomer".

This model has been printed successfully using these parameters on Shapeways’ laser sintering printer in the following material: Processed Versatile Plastic (Strong & Flexible Plastic).

Model designer: Brandon Lasalle Authors: Brandon Lasalle and Rebecca Roston …

Integration Of Random Forest Classifiers And Deep Convolutional Neural Networks For Classification And Biomolecular Modeling Of Cancer Driver Mutations, Steve Agajanian, Odeyemi Oluyemi, Gennady M. Verkhivker

Mathematics, Physics, and Computer Science Faculty Articles and Research

Development of machine learning solutions for prediction of functional and clinical significance of cancer driver genes and mutations are paramount in modern biomedical research and have gained a significant momentum in a recent decade. In this work, we integrate different machine learning approaches, including tree based methods, random forest and gradient boosted tree (GBT) classifiers along with deep convolutional neural networks (CNN) for prediction of cancer driver mutations in the genomic datasets. The feasibility of CNN in using raw nucleotide sequences for classification of cancer driver mutations was initially explored by employing label encoding, one hot encoding, and embedding to …

Simplicity Diffexpress: A Bespoke Cloud-Based Interface For Rna-Seq Differential Expression Modeling And Analysis, Cintia C. Palu, Marcelo Ribeiro-Alves, Yanxin Wu, Brendan Lawlor, Pavel V. Baranov, Brian Kelly, Paul Walsh

Department of Computer Science Publications

One of the key challenges for transcriptomics-based research is not only the processing of large data but also modeling the complexity of features that are sources of variation across samples, which is required for an accurate statistical analysis. Therefore, our goal is to foster access for wet lab researchers to bioinformatics tools, in order to enhance their ability to explore biological aspects and validate hypotheses with robust analysis. In this context, user-friendly interfaces can enable researchers to apply computational biology methods without requiring bioinformatics expertise. Such bespoke platforms can improve the quality of the findings by allowing the researcher to …

Draft Genome Sequences Of Three Monokaryotic Isolates Of The White-Rot Basidiomycete Fungus Dichomitus Squalens, Sara Casado López, Mao Peng, Paul Daly, Bill Andreopoulos, Jasmyn Pangilinan, Anna Lipzen, Robert Riley, Steven Ahrendt, Vivian Ng, Kerrie Barry, Chris Daum, Igor Grigoriev, Kristiina Hildén, Miia Mäkelä, Ronald De Vries

Faculty Publications, Computer Science

Here, we report the draft genome sequences of three isolates of the wood-decaying white-rot basidiomycete fungus Dichomitus squalens. The genomes of these monokaryons were sequenced to provide more information on the intraspecies genomic diversity of this fungus and were compared to the previously sequenced genome of D. squalens LYAD-421 SS1.

Designing Computational Biology Workflows With Perl - Part 1, Esma Yildirim

Open Educational Resources

This material introduces Linux File System structures and demonstrates how to use commands to communicate with the operating system through a Terminal program. Basic program structures and system() function of Perl are discussed. A brief introduction to gene-sequencing terminology and file formats are given.

Designing Computational Biology Workflows With Perl - Part 1, Esma Yildirim

Open Educational Resources

This material introduces the AWS console interface, describes how to create an instance on AWS with the VMI provided, connect to that machine instance using the SSH protocol. Once connected, it requires the students to write a script to enter the data folder, which includes gene-sequencing input files and print the first five line of each file remotely. The same exercise can be applied if the VMI is installed on a local machine using virtualization software (e.g. Oracle VirtualBox). In this case, the Terminal program of the VMI can be used to do the exercise.

Designing Computational Biology Workflows With Perl - Part 2, Esma Yildirim

Open Educational Resources

This material introduces the AWS console interface, describes how to create an instance on AWS with the VMI provided and connect to that machine instance using the SSH protocol. Once connected, it requires the students to write a script to automate the tasks to create VCF files from two different sample genomes belonging to E.coli microorganisms by using the FASTA and FASTQ files in the input folder of the virtual machine. The same exercise can be applied if the VMI is installed on a local machine using virtualization software (e.g. Oracle VirtualBox). In this case, the Terminal program of the …

Designing Computational Biology Workflows With Perl - Part 2, Esma Yildirim

Open Educational Resources

This material briefly reintroduces the DNA double Helix structure, explains SNP and INDEL mutations in genes and describes FASTA, FASTQ, BAM and VCF file formats. It also explains the index creation, alignment, sorting, marking duplicates and variant calling steps of a simple preprocessing workflow and how to write a Perl script to automate the execution of these steps on a Virtual Machine Image.

Designing Computational Biology Workflows With Perl - Part 1 & 2, Esma Yildirim

Open Educational Resources

This manual guides the instructor to combine the partial files of the virtual machine image and construct sequencer.ova file. It is accompanied by the partial files of the virtual machine image.

A Data-Driven Approach For Detecting Stress In Plants Using Hyperspectral Imagery, Suraj Gampa

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

A phenotype is an observable characteristic of an individual and is a function of its genotype and its growth environment. Individuals with different genotypes are impacted differently by exposure to the same environment. Therefore, phenotypes are often used to understand morphological and physiological changes in plants as a function of genotype and biotic and abiotic stress conditions. Phenotypes that measure the level of stress can help mitigate the adverse impacts on the growth cycle of the plant. Image-based plant phenotyping has the potential for early stress detection by means of computing responsive phenotypes in a non-intrusive manner. A large number …

Discrete-Position Solar Tracking For Photovoltaic System, Shengnan Hong, Zheng Fu, Richard E. Stamper

Rose-Hulman Undergraduate Research Publications

The purpose of this research is to design a new tracking system for solar panels using the idea of discrete-position tracking. Compared with the traditional fixed solar panel, discrete-position trackers have a higher gain of harvesting solar radiation with smaller misalignment angles. Also, since we are trying to design the a passive tracker with solely mechanical structure to do the kinetics, a discrete-position tracker can decrease the cost of the maintenance to a huge extent in contrast to both one-axis and two-axis continuous tracking systems. The majority of the cost of maintaining a continuous tracker is the motor or hydraulic …

Lab Practicum For Bias In Algorithms, Ameet Soni, Krista Karbowski Thomason

Digital Humanities Curricular Development

This is a course assignment to demonstrate potential biases encoded in algorithms (this can be linked more specifically to natural language processing, machine learning, or artificial intelligence) using the Word Embedding Association Test. In lab, students will work with programs that demonstrate the usefulness of word embedding algorithms in finding relationships between words. Then, students will use an implementation of the algorithm in "Semantics derived automatically from language corpora contain human-like biases" by Caliskan et al. to detect gender and racial bias encoded in word embeddings. The assignment has students design and run an experiment using the WEAT algorithm to …

Fys: Ethics And Technology (Phil 07/Cpsc 15) Syllabus, Ameet Soni, Krista Karbowski Thomason

Digital Humanities Curricular Development

There has been an accelerated shift in the influence of computing technology and the use of algorithms in our daily lives. With this technology comes serious ethical questions. Philosophers are often well-equipped to wrestle with ethical questions, but less well-equipped to wrestle with questions of technology itself. Computer scientists are well-equipped to deal with the problems and challenges of technology, but less well-equipped to deal with the ethical problems and challenges that technology can pose. In this co-taught course, we bring together the two fields to address ethical questions involving social media, data mining, self-driving cars, artificial intelligence, and other …

Computational Contributions To The Automation Of Agriculture, Micah Nagel

Senior Honors Theses

The purpose of this paper is to explore ways that computational advancements have enabled the complete automation of agriculture from start to finish. With a major need for agricultural advancements because of food and water shortages, some farmers have begun creating their own solutions to these problems. Primarily explored in this paper, however, are current research topics in the automation of agriculture. Digital agriculture is surveyed, focusing on ways that data collection can be beneficial. Additionally, self-driving technology is explored with emphasis on farming applications. Machine vision technology is also detailed, with specific application to weed management and harvesting of …

Computational Analysis Of Large-Scale Trends And Dynamics In Eukaryotic Protein Family Evolution, Joseph Boehm Ahrens

FIU Electronic Theses and Dissertations

The myriad protein-coding genes found in present-day eukaryotes arose from a combination of speciation and gene duplication events, spanning more than one billion years of evolution. Notably, as these proteins evolved, the individual residues at each site in their amino acid sequences were replaced at markedly different rates. The relationship between protein structure, protein function, and site-specific rates of amino acid replacement is a topic of ongoing research. Additionally, there is much interest in the different evolutionary constraints imposed on sequences related by speciation (orthologs) versus sequences related by gene duplication (paralogs). A principal aim of this dissertation is to …

A Theoretical Model Of Underground Dipole Antennas For Communications In Internet Of Underground Things, Abdul Salam, Mehmet C. Vuran, Xin Dong, Christos Argyropoulos, Suat Irmak

Faculty Publications

The realization of Internet of Underground Things (IOUT) relies on the establishment of reliable communication links, where the antenna becomes a major design component due to the significant impacts of soil. In this paper, a theoretical model is developed to capture the impacts of change of soil moisture on the return loss, resonant frequency, and bandwidth of a buried dipole antenna. Experiments are conducted in silty clay loam, sandy, and silt loam soil, to characterize the effects of soil, in an indoor testbed and field testbeds. It is shown that at subsurface burial depths (0.1-0.4m), change in soil moisture impacts …

Allosteric Mechanism Of The Circadian Protein Vivid Resolved Through Markov State Model And Machine Learning Analysis, Hongyu Zhou, Zheng Dong, Gennady M. Verkhivker, Brian D. Zoltowski, Peng Tao

Mathematics, Physics, and Computer Science Faculty Articles and Research

The fungal circadian clock photoreceptor Vivid (VVD) contains a photosensitive allosteric light, oxygen, voltage (LOV) domain that undergoes a large N-terminal conformational change. The mechanism by which a blue-light driven covalent bond formation leads to a global conformational change remains unclear, which hinders the further development of VVD as an optogenetic tool. We answered this question through a novel computational platform integrating Markov state models, machine learning methods, and newly developed community analysis algorithms. Applying this new integrative approach, we provided a quantitative evaluation of the contribution from the covalent bond to the protein global conformational change, and proposed an …

Question Answering For Suicide Risk Assessment Using Reddit, Amanuel Alambo, Usha Lokala, Ugur Kursuncu, Krishnaprasad Thirunarayan, Amelia Gyrard, Randon S. Welton, Jyotishman Pathak, Amit P. Sheth

Kno.e.sis Publications

Mental Health America designed ten questionnaires that are used to determine the risk of mental disorders. They are also commonly used by Mental Health Professionals (MHPs) to assess suicidality. Specifically, the Columbia Suicide Severity Rating Scale (C-SSRS), a widely used suicide assessment questionnaire, helps MHPs determine the severity of suicide risk and offer an appropriate treatment. A major challenge in suicide treatment is the social stigma wherein the patient feels reluctance in discussing his/her conditions with an MHP, which leads to inaccurate assessment and treatment of patients. On the other hand, the same patient is comfortable freely discussing his/her mental …

Timing Is Everything: Temporal Dynamics Of Brain Activity Using The Human Connectome Project, Francesca Lofaro

Summer Research

Most neuroimaging studies produce snapshots of brain activity. The goal of this project is to examine the temporal dynamics of how these areas interact through time, using fear as a case study to assess how regions involved in fear interact. Working with Matlab computer code, I sort through the large fMRI dataset known as the Human Connectome Project to extract neuroimaging data from patients with different NIH Toolbox Fear-Somatic survey scores to assess the temporal dynamics between brain regions. The results will allow an understanding beyond which areas are involved, and instead will provide a picture of how these areas …

A Hybrid Cognitive Architecture With Primal Affect And Physiology, Christopher L. Dancy

Faculty Journal Articles

Though computational cognitive architectures have been used to study several processes associated with human behavior, the study of integration of affect and emotion in these processes has been relatively sparse. Theory from affective science and affective neuroscience can be used to systematically integrate affect into cognitive architectures, particularly in areas where cognitive system behavior is known to be associated with physiological structure and behavior. I introduce a unified theory and model of human behavior that integrates physiology and primal affect with cognitive processes in a cognitive architecture. This new architecture gives a more tractable, mechanistic way to simulate affect-cognition interactions …

Neuroprotective Effects Of Melatonin And Celecoxib Against Ethanol-Induced Neurodegeneration: A Computational And Pharmacological Approach, Lina T. Al Kury, Alam Zeb, Zain Ul Abidin, Nadeem Irshad, Imran Malik, Arooj Mohsin Alvi, Atif Ali Khan Khalil, Sareer Ahmad, Muhammad Faheem, Arif Ullah Khan, Fawad Ali Shah, Shupeng Li

All Works

© 2019 Al Kury et al. This work is published and licensed by Dove Medical Press Limited. Purpose: Melatonin and celecoxib are antioxidants and anti-inflammatory agents that exert protective effects in different experimental models. In this study, the neuroprotective effects of melatonin and celecoxib were demonstrated against ethanol-induced neuronal injury by in silico, morphological, and biochemical approaches. Methods: For the in silico study, 3-D structures were constructed and docking analysis performed. For in vivo studies, rats were treated with ethanol, melatonin, and celecoxib. Brain samples were collected for biochemical and morphological analysis. Results: Homology modeling was performed to build 3-D …

Empathi: An Ontology For Emergency Managing And Planning About Hazard Crisis, Manas Gaur, Kaeedeh Shekarpour, Amelia Gyrard, Amit P. Sheth

Kno.e.sis Publications

In the domain of emergency management during hazard crises, having sufficient situational awareness information is critical. It requires capturing and integrating information from sources such as satellite images, local sensors and social media content generated by local people.
A bold obstacle to capturing, representing and integrating such heterogeneous and diverse information is lack of a proper ontology which properly conceptualizes this domain, aggregates and unifies datasets. Thus, in this paper, we introduce empathi ontology which conceptualizes the core concepts describing the domain of emergency managing and planning of hazard crises.
Although empathi has a coarse-grained view, it considers the necessary …

Adaptive Knowledge Networks: A Time Capsule, Swati Padhee, Anurag Illendula, Amit Sheth, Krishnaprasad Thirunarayan, Valerie L. Shalin

Kno.e.sis Publications

❖ Real world events are dynamic in nature Periodic events e.g. US Presidential Election Non-periodic events e.g. Cyclone Idai

❖ Need for real-time predictive analysis, trend analysis, spatio-temporal decision making, public opinion analysis for events.

❖ Current state-of-the-art curates dynamic knowledge graph from structured text.

❖ We propose creating an Adaptive Knowledge Network from incoming real-time multimodal spatio-temporally evolving data.

End-To-End Learning Via A Convolutional Neural Network For Cancer Cell Line Classification, Darlington A. Akogo, Xavier-Lewis Palmer

Electrical & Computer Engineering Faculty Publications

Purpose: Computer vision for automated analysis of cells and tissues usually include extracting features from images before analyzing such features via various machine learning and machine vision algorithms. The purpose of this work is to explore and demonstrate the ability of a Convolutional Neural Network (CNN) to classify cells pictured via brightfield microscopy without the need of any feature extraction, using a minimum of images, improving work-flows that involve cancer cell identification.

Design/methodology/approach: The methodology involved a quantitative measure of the performance of a Convolutional Neural Network in distinguishing between two cancer lines. In their approach, they trained, validated and …