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The International Conference On Intelligent Biology And Medicine (Icibm) 2018: Bioinformatics Towards Translational Applications, Xiaoming Liu, Lei Xie, Zhijin Wu, Kai Wang, Zhongming Zhao, Jianhuan Ruan, Degui Zhi

Publications and Research

The 2018 International Conference on Intelligent Biology and Medicine (ICIBM 2018) was held on June 10–12, 2018, in Los Angeles, California, USA. The conference consisted of a total of eleven scientific sessions, four tutorials, one poster session, four keynote talks and four eminent scholar talks, which covered a wild range of aspects of bioinformatics, medical informatics, systems biology and intelligent computing. Here, we summarize nine research articles selected for publishing in BMC Bioinformatics.

Discerning Novel Splice Junctions Derived From Rna-Seq Alignment: A Deep Learning Approach, Yi Zhang, Xinan Liu, James N. Macleod, Jinze Liu

Computer Science Faculty Publications

Background: Exon splicing is a regulated cellular process in the transcription of protein-coding genes. Technological advancements and cost reductions in RNA sequencing have made quantitative and qualitative assessments of the transcriptome both possible and widely available. RNA-seq provides unprecedented resolution to identify gene structures and resolve the diversity of splicing variants. However, currently available ab initio aligners are vulnerable to spurious alignments due to random sequence matches and sample-reference genome discordance. As a consequence, a significant set of false positive exon junction predictions would be introduced, which will further confuse downstream analyses of splice variant discovery and abundance estimation.

Results: …

Pasnet: Pathway-Associated Sparse Deepneural Network For Prognosis Prediction From High-Throughput Data, Jie Hao, Youngsoon Kim, Tae-Kyung Kim, Mingon Kang

Faculty and Research Publications

Background: Predicting prognosis in patients from large-scale genomic data is a fundamentally challenging problem in genomic medicine. However, the prognosis still remains poor in many diseases. The poor prognosis maybe caused by high complexity of biological systems, where multiple biological components and their hierarchical relationships are involved. Moreover, it is challenging to develop robust computational solutions with high-dimension, low-sample size data. Results: In this study, we propose a Pathway-Associated Sparse Deep Neural Network (PASNet) that not only predicts patients’ prognoses but also describes complex biological processes regarding biological pathways for prognosis. PASNet models a multilayered, hierarchical biological system of genes …

Integrating Node Embeddings And Biological Annotations For Genes To Predict Disease-Gene Associations, Sezin Kircali Ata, Le Ou-Yang, Yuan Fang, Chee-Keong Kwoh, Min Wu, Xiao-Li Li

Research Collection School Of Computing and Information Systems

Background: Predicting disease causative genes (or simply, disease genes) has played critical roles in understandingthe genetic basis of human diseases and further providing disease treatment guidelines. While various computationalmethods have been proposed for disease gene prediction, with the recent increasing availability of biologicalinformation for genes, it is highly motivated to leverage these valuable data sources and extract useful information foraccurately predicting disease genes. Results: We present an integrative framework called N2VKO to predict disease genes. Firstly, we learn the nodeembeddings from protein-protein interaction (PPI) network for genes by adapting the well-known representationlearning method node2vec. Secondly, we combine the learned node …

Seqothello: Querying Rna-Seq Experiments At Scale, Ye Yu, Jinpeng Liu, Xinan Liu, Yi Zhang, Eamonn Magner, Erik Lehnert, Chen Qian, Jinze Liu

Computer Science Faculty Publications

We present SeqOthello, an ultra-fast and memory-efficient indexing structure to support arbitrary sequence query against large collections of RNA-seq experiments. It takes SeqOthello only 5 min and 19.1 GB memory to conduct a global survey of 11,658 fusion events against 10,113 TCGA Pan-Cancer RNA-seq datasets. The query recovers 92.7% of tier-1 fusions curated by TCGA Fusion Gene Database and reveals 270 novel occurrences, all of which are present as tumor-specific. By providing a reference-free, alignment-free, and parameter-free sequence search system, SeqOthello will enable large-scale integrative studies using sequence-level data, an undertaking not previously practicable for many individual labs.

Linking Gait Dynamics To Mechanical Cost Of Legged Locomotion, David V. Lee, Sarah L. Harris

Life Sciences Faculty Research

For millenia, legged locomotion has been of central importance to humans for hunting, agriculture, transportation, sport, and warfare. Today, the same principal considerations of locomotor performance and economy apply to legged systems designed to serve, assist, or be worn by humans in urban and natural environments. Energy comes at a premium not only for animals, wherein suitably fast and economical gaits are selected through organic evolution, but also for legged robots that must carry sufficient energy in their batteries. Although a robot's energy is spent at many levels, from control systems to actuators, we suggest that the mechanical cost of …

A Dexterous, Glove-Based Teleoperable Low-Power Soft Robotic Arm For Delicate Deep-Sea Biological Exploration, Brennan T. Phillips, Kaitlyn P. Becker, Shunichi Kurumaya, Kevin C. Galloway, Griffin Whittredge, Daniel M. Vogt, Clark B. Teeple, Michelle H. Rosen, Vincent A. Pieribone, David F. Gruber, Robert J. Wood

Publications and Research

Modern marine biologists seeking to study or interact with deep-sea organisms are confronted with few options beyond industrial robotic arms, claws, and suction samplers. This limits biological interactions to a subset of “rugged” and mostly immotile fauna. As the deep sea is one of the most biologically diverse and least studied ecosystems on the planet, there is much room for innovation in facilitating delicate interactions with a multitude of organisms. The biodiversity and physiology of shallow marine systems, such as coral reefs, are common study targets due to the easier nature of access; SCUBA diving allows for in situ delicate …

Amino Acid Pop-Set: Model File Name: Amino-Acid-Wgrp-Pop_Sc3.Stl, Michelle Howell, Rebecca Roston

3-D Printed Model Structural Files

This is a teaching model for protein primary structure. It consists of four amino acids (tryptophan, proline, arginine, and glycine) depicted in stick and space-fill representations, five peptide bonds depicted in space-fill, and an N-terminus and a C-terminus depicted in space-fill. It is designed so that students can make various peptides to explore the amount of space of the electron clouds of the amino acids and bonds, and explore the psi and phi angles for the peptides. The printable model is already uploaded to Shapeways.com in the MacroMolecules shop under the name “Amino acid pop-set”. This model has …

Lipoprotein Signal Peptidase Ii: Model File Name: 5dir-Lipoii-Reps_Sc1-5.Stl, Michelle Howell, Rebecca Roston

3-D Printed Model Structural Files

This is a teaching model of lipoprotein signal peptidase II (PDB: 5DIR). It is designed with different regions of the protein depicted in space-filling, ribbon, stick, and backbone-only representations to explore protein secondary structure and illustrate how much space the protein takes up. The printable model is already uploaded to Shapeways.com in the MacroMolecules shop under the name “Lipoprotein signal peptidase II” and is intended to accompany the “Crambin”, “Cytochrome c” and “3 water molecules” models. This model has been printed successfully using these parameters on Shapeways’ laser sintering printer in …

3 Water Molecules: Model File Name: 3hoh-Final.Stl, Michelle Howell, Rebecca Roston

3-D Printed Model Structural Files

This is a teaching model of 3 water molecules depicted in space-fill. It is designed to the same scale as the “Lipoprotein signal peptidase II”, “Crambin”, and “Cytochrome c” models to illustrate the amount of space taken up by proteins. The printable model is already uploaded to Shapeways.com in the MacroMolecules shop under the name “3 water molecules” and is intended to accompany the “Lipoprotein signal peptidase II”, “Crambin”, and “Cytochrome c” models. This model has been printed successfully using these parameters on Shapeways’ laser sintering …

Crambin: Model File Name: 2fd7-Crambin-Stick_Sc1-5.Stl, Michelle Howell, Rebecca Roston

3-D Printed Model Structural Files

This is a teaching model of cytochrome c (PDB: 2FD7). It is designed in a stick representation to explore protein secondary structure and how much space the protein takes up. The printable model is already uploaded to Shapeways.com in the MacroMolecules shop under the name “Crambin” and is intended to accompany the “Lipoprotein signal peptidase II”, “Cytochrome c”, and “3 water molecules” models. This model has been printed successfully using these parameters on Shapeways’ laser sintering printer in the following material: Processed Versatile Plastic (Strong & Flexible Plastic).

Cytochrome C: Model File Name: 1b7v-Cytc-Stick_Sc1-5.Stl, Michelle Howell, Rebecca Roston

3-D Printed Model Structural Files

This is a teaching model of cytochrome c (PDB: 1B7V). It is designed in a stick representation to explore protein secondary structure and how much space the protein takes up. The printable model is already uploaded to Shapeways.com in the MacroMolecules shop under the name “Cytochrome c” and is intended to accompany the “Lipoprotein signal peptidase II”, “Crambin”, and “3 water molecules” models. This model has been printed successfully using these parameters on Shapeways’ laser sintering printer in the following material: Processed Versatile Plastic (Strong & Flexible Plastic).

Building Iot Based Applications For Smart Cities: How Can Ontology Catalogs Help?, Amelia Gyrard, Antoine Zimmermann, Amit P. Sheth

Kno.e.sis Publications

The Internet of Things (IoT) plays an ever-increasing role in enabling smart city applications. An ontology-based semantic approach can help improve interoperability between a variety of IoT-generated as well as complementary data needed to drive these applications. While multiple ontology catalogs exist, using them for IoT and smart city applications require significant amount of work. In this paper, we demonstrate how can ontology catalogs be more effectively used to design and develop smart city applications? We consider four ontology catalogs that are relevant for IoT and smart cities: 1) READY4SmartCities; 2) linked open vocabulary (LOV); 3) OpenSensingCity (OSC); and 4) …

Using Electronic Health Records To Characterize Prescription Patterns: Focus On Antidepressants In Nonpsychiatric Outpatient Settings, Joseph J. Deferio, Tomer T. Levin, Judith Cukor, Samprit Banerjee, Rozan Abdulrahman, Amit P. Sheth, Neel Mehta, Jyotishman Pathak

Kno.e.sis Publications

Objective

To characterize nonpsychiatric prescription patterns of antidepressants according to drug labels and evidence assessments (on-label, evidence-based, and off-label) using structured outpatient electronic health record (EHR) data. Methods

A retrospective analysis was conducted using deidentified EHR data from an outpatient practice at a New York City-based academic medical center. Structured “medication–diagnosis” pairs for antidepressants from 35 325 patients between January 2010 and December 2015 were compared to the latest drug product labels and evidence assessments. Results

Of 140 929 antidepressant prescriptions prescribed by primary care providers (PCPs) and nonpsychiatry specialists, 69% were characterized as “on-label/evidence-based uses.” Depression diagnoses were associated …

Poster: Privacy-Preserving Boosting With Random Linear Classifiers, Sagar Sharma, Keke Chen

Kno.e.sis Publications

We propose SecureBoost, a privacy-preserving predictive modeling framework, that allows service providers (SPs) to build powerful boosting models over encrypted or randomly masked user submit- ted data. SecureBoost uses random linear classifiers (RLCs) as the base classifiers. A Cryptographic Service Provider (CSP) manages keys and assists the SP’s processing to reduce the complexity of the protocol constructions. The SP learns only the base models (i.e., RLCs) and the CSP learns only the weights of the base models and a limited leakage function. This separated parameter holding avoids any party from abusing the final model or conducting model-based attacks. We evaluate …

Human Hexokinase I - Allosteric Regulation: Model File Name: 1dgk-Editb22-Allostery_Sc06.Stl, Michelle Howell, Rebecca Roston

3-D Printed Model Structural Files

This is a teaching model of human Hexokinase I in a surface representation with small molecules ADP and G6P included (PDB: 1DGK). It is designed to be hollow with a lever to mimic allosteric regulation. The printable model is already uploaded to Shapeways.com in the MacroMolecules shop under the name “Human Hexokinase I - Allosteric regulation model”. This model has been printed successfully using these parameters on Shapeways’ laser sintering printer in the following material: Processed Versatile Plastic (Strong & Flexible Plastic).

Imapsplice: Alleviating Reference Bias Through Personalized Rna-Seq Alignment, Xinan Liu, James N. Macleod, Jinze Liu

Computer Science Faculty Publications

Genomic variants in both coding and non-coding sequences can have functionally important and sometimes deleterious effects on exon splicing of gene transcripts. For transcriptome profiling using RNA-seq, the accurate alignment of reads across exon junctions is a critical step. Existing algorithms that utilize a standard reference genome as a template sometimes have difficulty in mapping reads that carry genomic variants. These problems can lead to allelic ratio biases and the failure to detect splice variants created by splice site polymorphisms. To improve RNA-seq read alignment, we have developed a novel approach called iMapSplice that enables personalized mRNA transcriptome profiling. The …

Creating Real-Time Dynamic Knowledge Graphs, Swati Padhee, Sarasi Lalithsena, Amit P. Sheth

Kno.e.sis Publications

No abstract provided.

Augmented Personalized Health: Using Semantically Integrated Multimodal Data For Patient Empowered Health Management Strategies, Amit P. Sheth, Hong Y. Yip, Utkarshani Jaimini, Dipesh Kadariya, Vaikunth Sridharan, R. Venkataramanan, Tanvi Banerjee, Krishnaprasad Thirunarayan, Maninder Kalra

Kno.e.sis Publications

Healthcare as we know it is in the process of going through a massive change from:

1. Episodic to continuous

2. Disease-focused to wellness and quality of life focused

3. Clinic-centric to anywhere a patient is

4. Clinician controlled to patient empowered

5. Being driven by limited data to 360-degree, multimodal personal-public-population physical-cyber-social big data-driven URL: https://mhealth.md2k.org/2018-tech-showcase-home

“Musical Exercise” For People With Visual Impairments: A Preliminary Study With The Blindfolded, Ridwan Ahmed Khan, Myounghoon Jeon, Tejin Yoon

Michigan Tech Publications

Performing independent physical exercise is critical to maintain one's good health, but it is specifically hard for people with visual impairments. To address this problem, we have developed a Musical Exercise platform for people with visual impairments so that they can perform exercise in a good form consistently. We designed six different conditions, including blindfolded or visual without audio conditions, and blindfolded or visual with two different types of audio feedback (continuous vs. discrete) conditions. Eighteen sighted participants participated in the experiment, by doing two exercises - squat and wall sit with all six conditions. The results show that Musical …

Tgmi: An Efficient Algorithm For Identifying Pathway Regulators Through Evaluation Of Triple-Gene Mutual Interaction, Chathura J. Gunasekara, Kui Zhang, Wenping Deng, Laura E. Brown, Hairong Wei

Michigan Tech Publications

Despite their important roles, the regulators for most metabolic pathways and biological processes remain elusive. Presently, the methods for identifying metabolic pathway and biological process regulators are intensively sought after. We developed a novel algorithm called triple-gene mutual interaction (TGMI) for identifying these regulators using high-throughput gene expression data. It first calculated the regulatory interactions among triple gene blocks (two pathway genes and one transcription factor (TF)), using conditional mutual information, and then identifies significantly interacted triple genes using a newly identified novel mutual interaction measure (MIM), which was substantiated to reflect strengths of regulatory interactions within each triple gene …

Heterogeneous Activity Causes A Nonlinear Increase In The Group Energy Use Of Ant Workers Isolated From Queen And Brood, Nolan Ferral, Kyara Holloway, Mingzhong Li, Zhaozheng Yin, Chen Hou

Computer Science Faculty Research & Creative Works

Increasing evidence has shown that the energy use of ant colonies increases sublinearly with colony size so that large colonies consume less per capita energy than small colonies. It has been postulated that social environment (e.g., in the presence of queen and brood) is critical for the sublinear group energetics, and a few studies of ant workers isolated from queens and brood observed linear relationships between group energetics and size. In this paper, we hypothesize that the sublinear energetics arise from the heterogeneity of activity in ant groups, that is, large groups have relatively more inactive members than small groups. …

Recta: Regulon Identification Based On Comparative Genomics And Transcriptomics Analysis, Xin Chen, Anjun Ma, Adam Mcdermaid, Hanyuan Zhang, Chao Liu, Huansheng Cao, Qin Ma

School of Computing: Faculty Publications

Regulons, which serve as co-regulated gene groups contributing to the transcriptional regulation of microbial genomes, have the potential to aid in understanding of underlying regulatory mechanisms. In this study, we designed a novel computational pipeline, regulon identification based on comparative genomics and transcriptomics analysis (RECTA), for regulon prediction related to the gene regulatory network under certain conditions. To demonstrate the effectiveness of this tool, we implemented RECTA on Lactococcus lactis MG1363 data to elucidate acid-response regulons. A total of 51 regulons were identified, 14 of which have computational-verified significance. Among these 14 regulons, five of them were computationally predicted to …

Ai-Human Collaboration Via Eeg, Adam Noack

All College Thesis Program, 2016-2019

As AI becomes ever more competent and integrated into our lives, the issue of AI-human goal misalignment looms larger. This is partially because there is often a rift between what humans explicitly command and what they actually mean. Most contemporary AI systems cannot bridge this gap. In this study we attempted to reconcile the goals of human and machine by using EEG signals from a human to help a simulated agent complete a task.

Similarity Based Classification Of Adhd Using Singular Value Decomposition, Taban Eslami, Fahad Saeed

Parallel Computing and Data Science Lab Technical Reports

Attention deficit hyperactivity disorder (ADHD) is one of the most common brain disorders among children. This disorder is considered as a big threat for public health and causes attention, focus and organizing difficulties for children and even adults. Since the cause of ADHD is not known yet, data mining algorithms are being used to help discover patterns which discriminate healthy from ADHD subjects. Numerous efforts are underway with the goal of developing classification tools for ADHD diagnosis based on functional and structural magnetic resonance imaging data of the brain. In this paper, we used Eros, which is a technique for …

Tracing Actin Filament Bundles In Three-Dimensional Electron Tomography Density Maps Of Hair Cell Stereocilia, Salim Sazzed, Junha Song, Julio Kovacs, Willi Wriggers, Manfred Auer, Jing He

Computer Science Faculty Publications

Cryo-electron tomography (cryo-ET) is a powerful method of visualizing the three-dimensional organization of supramolecular complexes, such as the cytoskeleton, in their native cell and tissue contexts. Due to its minimal electron dose and reconstruction artifacts arising from the missing wedge during data collection, cryo-ET typically results in noisy density maps that display anisotropic XY versus Z resolution. Molecular crowding further exacerbates the challenge of automatically detecting supramolecular complexes, such as the actin bundle in hair cell stereocilia. Stereocilia are pivotal to the mechanoelectrical transduction process in inner ear sensory epithelial hair cells. Given the complexity and dense arrangement of actin …

Evaluating Reproducibility In Computational Biology Research, Morgan Oneka

Honors Projects

For my Honors Senior Project, I read five research papers in the field of computational biology and attempted to reproduce the results. However, for the most part, this proved a challenge, as many details vital to utilizing relevant software and data had been excluded. Using Geir Kjetil Sandve's paper "Ten Simple Rules for Reproducible Computational Research" as a guide, I discuss how authors of these five papers did and did not obey these rules of reproducibility and how this affected my ability to reproduce their results.

Prediction Of Lncrna-Disease Associations Based On Inductive Matrix Completion, Chengqian Lu, Mengyun Yang, Feng Luo, Fang-Xiang Wu, Min Li, Yi Pan, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

Motivation: Accumulating evidences indicate that long non-coding RNAs (lncRNAs) play pivotal roles in various biological processes. Mutations and dysregulations of lncRNAs are implicated in miscellaneous human diseases. Predicting lncRNA–disease associations is beneficial to disease diagnosis as well as treatment. Although many computational methods have been developed, precisely identifying lncRNA–disease associations, especially for novel lncRNAs, remains challenging.

Results: In this study, we propose a method (named SIMCLDA) for predicting potential lncRNA– disease associations based on inductive matrix completion. We compute Gaussian interaction profile kernel of lncRNAs from known lncRNA–disease interactions and functional similarity of diseases based on disease–gene and gene–gene onotology …

Volume 10, Taylor Hogg, Tiffany Carter, Brandyn Johnson, Haleigh James, Josh Baker, Tyler Cernak, Kirsten Bauer, Allie Snavely, Mary Zell Galen, Eric Powell, Thomas Wise, Katie Kinsey, Beth Barbolla, Maeleigh Ferlet, Rebecca Morra, Michala Day, Alexandra Evangelista, Max Flores, Harley Hodges, Clardene Jones, Harrison Samaniego, Jamesha Watson, Abby Gargiulo, Heather Green, Haley Klepatzki, Juan Guevara, Dani Bondurant, Michael Joseph Link Jr., Pamela Dahl, Maeve Losen, Charlotte Murphey

Incite: The Journal of Undergraduate Scholarship

Introduction Dr. Roger A. Byrne

An Analysis of Media Framing in Cases of Violence Against Women by Taylor Hogg

Writing in the Discipline of Nursing by Tiffany Carter

Photography by Brandyn Johnson

The Hidden Life of Beef Cattle: A Study of Cattle Welfare on Traditional Ranches and Industrial Farms by Haleigh James

Bloodworth's by Josh Baker and Tyler Cernak

Prosimians: Little Bodies, Big Significance by Kirsten Bauer

Skinformed by Allie Snavely

Coopertition and Gracious Professionalism: The Effects of First Robotics Folklore and Culture on the Stem Community by Mary Zell Galen

Tilt by Eric Powell And Thomas Wise

The Millennial …

Seed Dormancy-Life Form Profile For 358 Species From The Xishuangbanna Seasonal Tropical Rainforest, Yunnan Province, China Compared To World Database, Qinying Lan, Shouhua Yin, Huiyin He, Yunhong Tan, Qiang Liu, Yongmei Xia, Bin Wen, Carol C. Baskin, Jerry M. Baskin

Biology Faculty Publications

Seed dormancy profiles are available for the major vegetation regions/types on earth. These were constructed using a composite of data from locations within each region. Furthermore, the proportion of species with nondormant (ND) seeds and the five classes of dormancy is available for each life form in each region. Using these data, we asked: will the results be the same if many species from a specific area as opposed to data compiled from many locations are considered? Germination was tested for fresh seeds of 358 species in 95 families from the Xishuangbanna seasonal tropical rainforest (XSTRF): 177 trees, 66 shrubs, …