Life Sciences Commons^™

A Reliable Diabetic Retinopathy Grading Via Transfer Learning And Ensemble Learning With Quadratic Weighted Kappa Metric, Sai Venkatesh Chilukoti, Liqun Shan, Vijay Srinivas Tida, Anthony S. Maida, Xiali Hei

Computer Science Faculty Publications

The most common eye infection in people with diabetes is diabetic retinopathy (DR). It might cause blurred vision or even total blindness. Therefore, it is essential to promote early detection to prevent or alleviate the impact of DR. However, due to the possibility that symptoms may not be noticeable in the early stages of DR, it is difficult for doctors to identify them. Therefore, numerous predictive models based on machine learning (ML) and deep learning (DL) have been developed to determine all stages of DR. However, existing DR classification models cannot classify every DR stage or use a computationally heavy …

Foodmask: Real-Time Food Instance Counting, Segmentation And Recognition, Huu-Thanh Nguyen, Yu Cao, Chong-Wah Ngo, Wing-Kwong Chan

Research Collection School Of Computing and Information Systems

Food computing has long been studied and deployed to several applications. Understanding a food image at the instance level, including recognition, counting and segmentation, is essential to quantifying nutrition and calorie consumption. Nevertheless, existing techniques are limited to either category-specific instance detection, which does not reflect precisely the instance size at the pixel level, or category-agnostic instance segmentation, which is insufficient for dish recognition. This paper presents a compact and fast multi-task network, namely FoodMask, for clustering-based food instance counting, segmentation and recognition. The network learns a semantic space simultaneously encoding food category distribution and instance height at pixel basis. …

Machine Learning As A Tool For Early Detection: A Focus On Late-Stage Colorectal Cancer Across Socioeconomic Spectrums, Hadiza Galadima, Rexford Anson-Dwamena, Ashley Johnson, Ghalib Bello, Georges Adunlin, James Blando

Community & Environmental Health Faculty Publications

Purpose: To assess the efficacy of various machine learning (ML) algorithms in predicting late-stage colorectal cancer (CRC) diagnoses against the backdrop of socio-economic and regional healthcare disparities. Methods: An innovative theoretical framework was developed to integrate individual- and census tract-level social determinants of health (SDOH) with sociodemographic factors. A comparative analysis of the ML models was conducted using key performance metrics such as AUC-ROC to evaluate their predictive accuracy. Spatio-temporal analysis was used to identify disparities in late-stage CRC diagnosis probabilities. Results: Gradient boosting emerged as the superior model, with the top predictors for late-stage CRC diagnosis being anatomic site, …

Software Jimenae Allows Efficient Dynamic Simulations Of Boolean Networks, Centrality And System State Analysis, Martin Kaltdorf, Tim Breitenbach, Stefan Karl, Maximilian Fuchs, David Komla Kessie, Eric Psota, Martina Prelog, Edita Sarukhanyan, Regina Ebert, Franz Jakob, Gudrun Dandekar, Muhammad Naseem, Chunguang Liang, Thomas Dandekar

All Works

The signal modelling framework JimenaE simulates dynamically Boolean networks. In contrast to SQUAD, there is systematic and not just heuristic calculation of all system states. These specific features are not present in CellNetAnalyzer and BoolNet. JimenaE is an expert extension of Jimena, with new optimized code, network conversion into different formats, rapid convergence both for system state calculation as well as for all three network centralities. It allows higher accuracy in determining network states and allows to dissect networks and identification of network control type and amount for each protein with high accuracy. Biological examples demonstrate this: (i) High plasticity …

Regulation Of Regeneration In Arabidopsis Thaliana, Md Khairul Islam, Sai Teja Mummadi, Sanzhen Liu, Hairong Wei

Michigan Tech Publications, Part 2

We employed several algorithms with high efficacy to analyze the public transcriptomic data, aiming to identify key transcription factors (TFs) that regulate regeneration in Arabidopsis thaliana. Initially, we utilized CollaborativeNet, also known as TF-Cluster, to construct a collaborative network of all TFs, which was subsequently decomposed into many subnetworks using the Triple-Link and Compound Spring Embedder (CoSE) algorithms. Functional analysis of these subnetworks led to the identification of nine subnetworks closely associated with regeneration. We further applied principal component analysis and gene ontology (GO) enrichment analysis to reduce the subnetworks from nine to three, namely subnetworks 1, 12, and 17. …

An Overview Of Elements And Relations: Aspects Of A Scientific Metaphysics, Martin Zwick

Systems Science Faculty Publications and Presentations

A talk on my book, Elements and Relations: Aspects of a Scientific Metaphysics. Book description:

This book develops the core proposition that systems theory is an attempt to construct an “exact and scientific metaphysics,” a system of general ideas central to science that can be expressed mathematically. Collectively, these ideas would constitute a non-reductionist “theory of everything” unlike what is being sought in physics. Inherently transdisciplinary, systems theory offers ideas and methods that are relevant to all of the sciences and also to professional fields such as systems engineering, public policy, business, and social work. To demonstrate the generality …

Motif-Cluster: A Spatial Clustering Package For Repetitive Motif Binding Patterns, Mengyuan Zhou

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

Previous efforts in using genome-wide analysis of transcription factor binding sites (TFBSs) have overlooked the importance of ranking potential significant regulatory regions, especially those with repetitive binding within a local region. Identifying these homogenous binding sites is critical because they have the potential to amplify the binding affinity and regulation activity of transcription factors, impacting gene expression and cellular functions. To address this issue, we developed an open-source tool Motif-Cluster that prioritizes and visualizes transcription factor regulatory regions by incorporating the idea of local motif clusters. Motif-Cluster can rank the significant transcription factor regulatory regions without the need for experimental …

Deep Learning For Plant Bioinformatics: An Explainable Gradient-Based Approach For Disease Detection, Muhammad Shoaib, Babar Shah, Nasir Sayed, Farman Ali, Rafi Ullah, Irfan Hussain

All Works

Emerging in the realm of bioinformatics, plant bioinformatics integrates computational and statistical methods to study plant genomes, transcriptomes, and proteomes. With the introduction of high-throughput sequencing technologies and other omics data, the demand for automated methods to analyze and interpret these data has increased. We propose a novel explainable gradient-based approach EG-CNN model for both omics data and hyperspectral images to predict the type of attack on plants in this study. We gathered gene expression, metabolite, and hyperspectral image data from plants afflicted with four prevalent diseases: powdery mildew, rust, leaf spot, and blight. Our proposed EG-CNN model employs a …

Intelligence Versus Inferno: How Artificial Intelligence Can Be Used To Monitor And Manage Wildfires In Europe, Maxwell Feldman

Independent Study Project (ISP) Collection

Escalating wildfire occurrences in Europe, particularly in the southern Mediterranean region, are presenting significant challenges to socioeconomic, environmental, and ecosystem services. The increasing frequency and severity of these wildfires are straining resources and emphasizing the need for a better understanding of the relationship between suppression capacity and fire behavior in wildfire management. The following research addresses the urgent need for more proactive, knowledge-based, and technologically driven fire management strategies, throughout all four stages of wildfire response – fuel and land management, fire preparedness, fire suppression, and post-fire management. Artificial Intelligence (AI) is becoming increasingly significant in climate change adaptation, especially …

Reconstructing 42 Years (1979–2020) Of Great Lakes Surface Temperature Through A Deep Learning Approach, Miraj Kayastha, Tao Liu, Daniel Titze, Timothy C. Havens, Chenfu Huang, Pengfei Xue

Michigan Tech Publications, Part 2

Accurate estimates for the lake surface temperature (LST) of the Great Lakes are critical to understanding the regional climate. Dedicated lake models of various complexity have been used to simulate LST but they suffer from noticeable biases and can be computationally expensive. Additionally, the available historical LST datasets are limited by either short temporal coverage (<30 >years) or lower spatial resolution (0.25° × 0.25°). Therefore, in this study, we employed a deep learning model based on Long Short-Term Memory (LSTM) neural networks to produce a daily LST dataset for the Great Lakes that spans an unparalleled 42 years (1979–2020) at …

Sctiger: A Deep-Learning Method For Inferring Gene Regulatory Networks From Case Versus Control Scrna-Seq Datasets., Madison Dautle, Shaoqiang Zhang, Yong Chen

Faculty Scholarship for the College of Science & Mathematics

Inferring gene regulatory networks (GRNs) from single-cell RNA-seq (scRNA-seq) data is an important computational question to find regulatory mechanisms involved in fundamental cellular processes. Although many computational methods have been designed to predict GRNs from scRNA-seq data, they usually have high false positive rates and none infer GRNs by directly using the paired datasets of case-versus-control experiments. Here we present a novel deep-learning-based method, named scTIGER, for GRN detection by using the co-differential relationships of gene expression profiles in paired scRNA-seq datasets. scTIGER employs cell-type-based pseudotiming, an attention-based convolutional neural network method and permutation-based significance testing for inferring GRNs among …

Non-Binary Evaluation Of Next-Basket Food Recommendation, Yue Liu, Palakorn Achananuparp, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Next-basket recommendation (NBR) is a recommendation task that predicts a basket or a set of items a user is likely to adopt next based on his/her history of basket adoption sequences. It enables a wide range of novel applications and services from predicting next basket of items for grocery shopping to recommending food items a user is likely to consume together in the next meal. Even though much progress has been made in the algorithmic NBR research over the years, little research has been done to broaden knowledge about the evaluation of NBR methods, which is largely based on the …

Vitreoscilla Globin Promoter Cloning And Testing In Escherichia Coli, Lauren J. Coffey

Rose-Hulman Undergraduate Research Publications

No abstract provided.

From Deep Mutational Mapping Of Allosteric Protein Landscapes To Deep Learning Of Allostery And Hidden Allosteric Sites: Zooming In On “Allosteric Intersection” Of Biochemical And Big Data Approaches, Gennady M. Verkhivker, Mohammed Alshahrani, Grace Gupta, Sian Xiao, Peng Tao

Mathematics, Physics, and Computer Science Faculty Articles and Research

The recent advances in artificial intelligence (AI) and machine learning have driven the design of new expert systems and automated workflows that are able to model complex chemical and biological phenomena. In recent years, machine learning approaches have been developed and actively deployed to facilitate computational and experimental studies of protein dynamics and allosteric mechanisms. In this review, we discuss in detail new developments along two major directions of allosteric research through the lens of data-intensive biochemical approaches and AI-based computational methods. Despite considerable progress in applications of AI methods for protein structure and dynamics studies, the intersection between allosteric …

Volume 14, Ireland Seagle, Dalton C. Whitby, Cassandra Poole, Rachel Cannon, Heidi Parker-Combes, Devon G. Shifflett, Antonio Harvey

Incite: The Journal of Undergraduate Scholarship

Table of Contents:

• Introduction: Dr. Amorette Barber
• From the Editor: Dr. Larissa "Kat" Tracy
• From the Designers: Rachel English, Rachel Hanson
• Hungry Like the Wolf: The Wolf as Metaphor in Paramount Network’s Yellowstone: Ireland Seagle
• “Floating Cities”: Illustrating the Commercial and Conservation Conflict of Alaskan Cruise Ship Tourism: Dalton C. Whitby
• What Can You Do When Your Genes are the Enemy? Current Applications of Gene Manipulation and the Associated Ethical Considerations: Cassandra Poole
• La doble cara: un tema romántico en las obras de Larra y Hawthorne: Rachel Cannon
• Resolving a Conflict: How to …

An Advanced Deep Learning Models-Based Plant Disease Detection: A Review Of Recent Research, Muhammad Shoaib, Babar Shah, Shaker Ei-Sappagh, Akhtar Ali, Asad Ullah, Fayadh Alenezi, Tsanko Gechev, Tariq Hussain, Farman Ali

All Works

Plants play a crucial role in supplying food globally. Various environmental factors lead to plant diseases which results in significant production losses. However, manual detection of plant diseases is a time-consuming and error-prone process. It can be an unreliable method of identifying and preventing the spread of plant diseases. Adopting advanced technologies such as Machine Learning (ML) and Deep Learning (DL) can help to overcome these challenges by enabling early identification of plant diseases. In this paper, the recent advancements in the use of ML and DL techniques for the identification of plant diseases are explored. The research focuses on …

A Fog Computing Framework For Intrusion Detection Of Energy-Based Attacks On Uav-Assisted Smart Farming, Junaid Sajid, Kadhim Hayawi, Asad Waqar Malik, Zahid Anwar, Zouheir Trabelsi

All Works

Precision agriculture and smart farming have received significant attention due to the advancements made in remote sensing technology to support agricultural efficiency. In large-scale agriculture, the role of unmanned aerial vehicles (UAVs) has increased in remote monitoring and collecting farm data at regular intervals. However, due to an open environment, UAVs can be hacked to malfunction and report false data. Due to limited battery life and flight times requiring frequent recharging, a compromised UAV wastes precious energy when performing unnecessary functions. Furthermore, it impacts other UAVs competing for charging times at the station, thus disrupting the entire data collection mechanism. …

Ambient Electromagnetic Radiation As A Predictor Of Honey Bee (Apis Mellifera) Traffic In Linear And Non-Linear Regression: Numerical Stability, Physical Time And Energy Efficiency, Vladimir Kulyukin, Daniel Coster, Anastasiia Tkachenko, Daniel Hornberger, Aleksey V. Kulyukin

Computer Science Faculty and Staff Publications

Since bee traffic is a contributing factor to hive health and electromagnetic radiation has a growing presence in the urban milieu, we investigate ambient electromagnetic radiation as a predictor of bee traffic in the hive’s vicinity in an urban environment. To that end, we built two multi-sensor stations and deployed them for four and a half months at a private apiary in Logan, Utah, U.S.A. to record ambient weather and electromagnetic radiation. We placed two non-invasive video loggers on two hives at the apiary to extract omnidirectional bee motion counts from videos. The time-aligned datasets were used to evaluate 200 …

Teloportwrapper: A New Tool For Understanding The Dynamic World Of Fungal Telomere Ends, Trey Stansfield

Mahurin Honors College Capstone Experience/Thesis Projects

Telomeres are repetitive DNA sequence motifs found at eukaryote chromosome ends. Telomeres help protect chromosome ends from DNA damage and promote chromosome stability. Chromosomes play important roles in aging, mutation, and cancer. Eukaryotic pathogens also use telomeres to mutate and manage virulence genes. In response to chromosome end breakage newly formed telomeres, called de novo telomeres, are formed to recreate the lost telomere and sub-telomeric regions.

Magnaporthe oryzae is a fungal pathogen which causes wheat blast, a deadly plant disease in wheat. Magnaporthe oryzae is also known for its highly variable sub-regions which show high amounts of induced variability due …

Towards A Novel Approach For Smart Agriculture Predictability, Rima Grati, Myriam Aloulou, Khouloud Boukadi

All Works

No abstract provided.

Cellbrf: A Feature Selection Method For Single-Cell Clustering Using Cell Balance And Random Forest, Yunpei Xu, Hong-Dong Li, Cui-Xiang Lin, Ruiqing Zheng, Yaohang Li, Jinhui Xu, Jianxin Wang

Computer Science Faculty Publications

Motivation

Single-cell RNA sequencing (scRNA-seq) offers a powerful tool to dissect the complexity of biological tissues through cell sub-population identification in combination with clustering approaches. Feature selection is a critical step for improving the accuracy and interpretability of single-cell clustering. Existing feature selection methods underutilize the discriminatory potential of genes across distinct cell types. We hypothesize that incorporating such information could further boost the performance of single cell clustering. Results

We develop CellBRF, a feature selection method that considers genes’ relevance to cell types for single-cell clustering. The key idea is to identify genes that are most important for discriminating …

Dfhic: A Dilated Full Convolution Model To Enhance The Resolution Of Hi-C Data, Bin Wang, Kun Liu, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

Motivation: Hi-C technology has been the most widely used chromosome conformation capture(3C) experiment that measures the frequency of all paired interactions in the entire genome, which is a powerful tool for studying the 3D structure of the genome. The fineness of the constructed genome structure depends on the resolution of Hi-C data. However, due to the fact that high-resolution Hi-C data require deep sequencing and thus high experimental cost, most available Hi-C data are in low-resolution. Hence, it is essential to enhance the quality of Hi-C data by developing the effective computational methods.

Results: In this work, we propose …

A Review Of Methods For Detecting Rats At Low Densities, With Implications For Surveillance, Robert A. Davis, Philip J. Seddon, Michael D. Craig, James C. Russell

Research outputs 2022 to 2026

Invasive rats are the biggest threat to island biodiversity world-wide. Though the ecological impacts of rats on insular biota are well documented, introduced rats present a difficult problem for detection and management. In recent decades, improved approaches have allowed for island-wide eradications of invasive rats on small-medium sized islands and suppression on large islands, although both these still represent a formidable logistical and financial challenge. A key aspect of eradication or suppression and ongoing management is the ability to detect the presence of rats, especially at low densities. Here we review recent developments in the field of rat surveillance and …

An Approach To Developing Benchmark Datasets For Protein Secondary Structure Segmentation From Cryo-Em Density Maps, Thu Nguyen, Yongcheng Mu, Jiangwen Sun, Jing He

Computer Science Faculty Publications

More and more deep learning approaches have been proposed to segment secondary structures from cryo-electron density maps at medium resolution range (5--10Å). Although the deep learning approaches show great potential, only a few small experimental data sets have been used to test the approaches. There is limited understanding about potential factors, in data, that affect the performance of segmentation. We propose an approach to generate data sets with desired specifications in three potential factors - the protein sequence identity, structural contents, and data quality. The approach was implemented and has generated a test set and various training sets to study …

Intergenic Transcription In In Vivo Developed Bovine Oocytes And Pre-Implantation Embryos, Saurav Ranjitkar, Mohammad Shiri, Jiangwen Sun, Xiuchun Tian

Computer Science Faculty Publications

Background

Intergenic transcription, either failure to terminate at the transcription end site (TES), or transcription initiation at other intergenic regions, is present in cultured cells and enhanced in the presence of stressors such as viral infection. Transcription termination failure has not been characterized in natural biological samples such as pre-implantation embryos which express more than 10,000 genes and undergo drastic changes in DNA methylation.

Results

Using Automatic Readthrough Transcription Detection (ARTDeco) and data of in vivo developed bovine oocytes and embryos, we found abundant intergenic transcripts that we termed as read-outs (transcribed from 5 to 15 kb after TES) and …

Sequence-Based Bioinformatics Approaches To Predict Virus-Host Relationships In Archaea And Eukaryotes, Yingshan Li

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

Viral metagenomics is independent of lab culturing and capable of investigating viromes of virtually any given environmental niches. While numerous sequences of viral genomes have been assembled from metagenomic studies over the past years, the natural hosts for the majority of these viral contigs have not been determined. Different computational approaches have been developed to predict hosts of bacteria phages. Nevertheless, little progress has been made in the virus-host prediction, especially for viruses that infect eukaryotes and archaea. In this study, by analyzing all documented viruses with known eukaryotic and archaeal hosts, we assessed the predictive power of four computational …

Using Landsat Satellite Imagery To Estimate Groundcover In The Grainbelt Of Western Australia, Justin Laycock, Nick Middleton, Karen Holmes

Resource management technical reports

Maintaining vegetative groundcover is an important component of sustainable agricultural systems and plays a critical function for soil and land conservation in Western Australia’s (WA) grainbelt (the south-west cropping region). This report describes how satellite imagery can be used to quantitatively and objectively estimate total vegetative groundcover, both in near real time and historically across large areas. We used the Landsat seasonal fractional groundcover products developed by the Joint Remote Sensing Research Program from the extensive archive of Landsat imagery. These products provide an estimate of the percentage of green vegetation, non-green vegetation and bare soil for each 30 m …

Npgreat: Assembly Of The Human Subtelomere Regions With The Use Of Ultralong Nanopore Reads And Linked Reads, Eleni Adam, Desh Ranjan, Harold Riethman

Computer Science Faculty Publications

Background: Human subtelomeric DNA regulates the length and stability of adjacent telomeres that are critical for cellular function, and contains many gene/pseudogene families. Large evolutionarily recent segmental duplications and associated structural variation in human subtelomeres has made complete sequencing and assembly of these regions difficult to impossible for many loci, complicating or precluding a wide range of genetic analyses to investigate their function.

Results: We present a hybrid assembly method, NanoPore Guided REgional Assembly Tool (NPGREAT), which combines Linked-Read data with mapped ultralong nanopore reads spanning subtelomeric segmental duplications to potentially overcome these difficulties. Linked-Read sets of DNA sequences identified …

A Pipeline To Generate Deep Learning Surrogates Of Genome-Scale Metabolic Models, Achilles Rasquinha

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

Genome-Scale Metabolic Models (GEMMs) are powerful reconstructions of biological systems that help metabolic engineers understand and predict growth conditions subjected to various environmental factors around the cellular metabolism of an organism in observation, purely in silico. Applications of metabolic engineering range from perturbation analysis and drug-target discovery to predicting growth rates of biotechnologically important metabolites and reaction objectives within dierent single-cell and multi-cellular organism types. GEMMs use mathematical frameworks for quantitative estimations of flux distributions within metabolic networks. The reasons behind why an organism activates, stuns, or fluctuates between alternative pathways for growth and survival, however, remain relatively unknown. GEMMs …

An Effective Deep Learning Approach For The Classification Of Bacteriosis In Peach Leave, Muneer Akbar, Mohib Ullah, Babar Shah, Rafi Ullah Khan, Tariq Hussain, Farman Ali, Fayadh Alenezi, Ikram Syed, Kyung Sup Kwak

All Works

Bacteriosis is one of the most prevalent and deadly infections that affect peach crops globally. Timely detection of Bacteriosis disease is essential for lowering pesticide use and preventing crop loss. It takes time and effort to distinguish and detect Bacteriosis or a short hole in a peach leaf. In this paper, we proposed a novel LightWeight (WLNet) Convolutional Neural Network (CNN) model based on Visual Geometry Group (VGG-19) for detecting and classifying images into Bacteriosis and healthy images. Profound knowledge of the proposed model is utilized to detect Bacteriosis in peach leaf images. First, a dataset is developed which consists …