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Selected Papers from the International Conference on Intelligent Biology and...
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Selected Papers from the International Conference on Intelligent Biology and Medicine (ICIBM 2020)

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Technologies and Resources for Genetics".

Deadline for manuscript submissions: closed (15 September 2020) | Viewed by 14930

Special Issue Editors

Prof. Dr. Yan Guo

grade E-Mail Website1 Website2
Guest Editor
Department of Internal Medicine, University of New Mexico, Albuquerque, NM 87131, USA
Interests: genomics; genetics; bioinformatics; mitochondria; data mining; machine learning; high throughput genomic data
Special Issues, Collections and Topics in MDPI journals
Dr. Sergey Ivanov

E-Mail Website
Guest Editor
Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
Interests: genome evolution; cancer genetics and metabolism; mitochondria; diabetes
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The 2020 International Conference on Intelligent Biology and Medicine (ICIBM 2020) will be held on August 9–11, 2020 in Philadelphia, PA, USA. The webpage for this event is https://icibm2020.iaibm.org/

The ICIBM conference series has two main aims: 1) to foster interdisciplinary and multidisciplinary research in bioinformatics-related fields, and 2) to provide an educational program for trainees and young investigators across a range of scientific disciplines to learn about the frontier research in these areas and to build a network among both established and junior investigators.

The current Special Issue invites submissions on unpublished original work describing recent advances in all aspects of bioinformatics, systems biology, intelligent computing, and medical informatics, including but not restricted to the following topics:

  1. Genomics and genetics, including integrative and functional genomics, and genome evolution.
  2. Next-generation sequencing data analysis, applications, and software and tools.
  3. Big data science including storage, analysis, modeling, visualization, and cloud.
  4. Precision medicine, translational bioinformatics, and medical informatics.
  5. Drug discovery, design, and repurposing.
  6. Proteomics and protein structure prediction, molecular simulation, and evolution.
  7. Single-cell sequencing data analysis.
  8. Microbiome and metagenomics.

A full list of topics is available on the conference website.

Please check 2021 edition

Prof. Dr. Yan Guo
Dr. Sergey Ivanov
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Genes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Bioinformatics
  • Systems biology
  • Intelligent computing
  • Medical informatics
  • Integrative genomics
  • Functional genomics
  • Genome evolution
  • NGS analysis
  • Precision medicine
  • Translational research
  • Drug discovery
  • Molecular simulations
  • Single cell sequencing data analysis
  • Microbiome and metagenomics
  • Artificial intelligence
  • Machine learning
  • Data mining
  • Synthetic biological systems
  • Mathematical models
  • Biological processes, pathways and networks
  • EHR-based phenotyping

Published Papers (4 papers)

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Research

13 pages, 671 KiB  
Article
Genetic-Based Hypertension Subtype Identification Using Informative SNPs
by Yuanjing Ma, Hongmei Jiang, Sanjiv J Shah, Donna Arnett, Marguerite R Irvin and Yuan Luo
Genes 2020, 11(11), 1265; https://doi.org/10.3390/genes11111265 - 27 Oct 2020
Cited by 5 | Viewed by 2730
Abstract
In this work, we proposed a process to select informative genetic variants for identifying clinically meaningful subtypes of hypertensive patients. We studied 575 African American (AA) and 612 Caucasian hypertensive participants enrolled in the Hypertension Genetic Epidemiology Network (HyperGEN) study and analyzed each [...] Read more.
In this work, we proposed a process to select informative genetic variants for identifying clinically meaningful subtypes of hypertensive patients. We studied 575 African American (AA) and 612 Caucasian hypertensive participants enrolled in the Hypertension Genetic Epidemiology Network (HyperGEN) study and analyzed each race-based group separately. All study participants underwent GWAS (Genome-Wide Association Studies) and echocardiography. We applied a variety of statistical methods and filtering criteria, including generalized linear models, F statistics, burden tests, deleterious variant filtering, and others to select the most informative hypertension-related genetic variants. We performed an unsupervised learning algorithm non-negative matrix factorization (NMF) to identify hypertension subtypes with similar genetic characteristics. Kruskal–Wallis tests were used to demonstrate the clinical meaningfulness of genetic-based hypertension subtypes. Two subgroups were identified for both African American and Caucasian HyperGEN participants. In both AAs and Caucasians, indices of cardiac mechanics differed significantly by hypertension subtypes. African Americans tend to have more genetic variants compared to Caucasians; therefore, using genetic information to distinguish the disease subtypes for this group of people is relatively challenging, but we were able to identify two subtypes whose cardiac mechanics have statistically different distributions using the proposed process. The research gives a promising direction in using statistical methods to select genetic information and identify subgroups of diseases, which may inform the development and trial of novel targeted therapies. Full article
(This article belongs to the Special Issue Selected Papers from the International Conference on Intelligent Biology and Medicine (ICIBM 2020))
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13 pages, 1105 KiB  
Article
Enhanced Co-Expression Extrapolation (COXEN) Gene Selection Method for Building Anti-Cancer Drug Response Prediction Models
by Yitan Zhu, Thomas Brettin, Yvonne A. Evrard, Fangfang Xia, Alexander Partin, Maulik Shukla, Hyunseung Yoo, James H. Doroshow and Rick L. Stevens
Genes 2020, 11(9), 1070; https://doi.org/10.3390/genes11091070 - 11 Sep 2020
Cited by 9 | Viewed by 2849
Abstract
The co-expression extrapolation (COXEN) method has been successfully used in multiple studies to select genes for predicting the response of tumor cells to a specific drug treatment. Here, we enhance the COXEN method to select genes that are predictive of the efficacies of [...] Read more.
The co-expression extrapolation (COXEN) method has been successfully used in multiple studies to select genes for predicting the response of tumor cells to a specific drug treatment. Here, we enhance the COXEN method to select genes that are predictive of the efficacies of multiple drugs for building general drug response prediction models that are not specific to a particular drug. The enhanced COXEN method first ranks the genes according to their prediction power for each individual drug and then takes a union of top predictive genes of all the drugs, among which the algorithm further selects genes whose co-expression patterns are well preserved between cancer cases for building prediction models. We apply the proposed method on benchmark in vitro drug screening datasets and compare the performance of prediction models built based on the genes selected by the enhanced COXEN method to that of models built on genes selected by the original COXEN method and randomly picked genes. Models built with the enhanced COXEN method always present a statistically significantly improved prediction performance (adjusted p-value ≤ 0.05). Our results demonstrate the enhanced COXEN method can dramatically increase the power of gene expression data for predicting drug response. Full article
(This article belongs to the Special Issue Selected Papers from the International Conference on Intelligent Biology and Medicine (ICIBM 2020))
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14 pages, 2341 KiB  
Article
Bioinformatics Analysis Revealed Novel 3′UTR Variants Associated with Intellectual Disability
by Junmeng Yang, Anna Liu, Isabella He and Yongsheng Bai
Genes 2020, 11(9), 998; https://doi.org/10.3390/genes11090998 - 26 Aug 2020
Cited by 9 | Viewed by 3795
Abstract
MicroRNAs (or miRNAs) are short nucleotide sequences (~17–22 bp long) that play important roles in gene regulation through targeting genes in the 3′untranslated regions (UTRs). Variants located in genomic regions might have different biological consequences in changing gene expression. Exonic variants (e.g., coding [...] Read more.
MicroRNAs (or miRNAs) are short nucleotide sequences (~17–22 bp long) that play important roles in gene regulation through targeting genes in the 3′untranslated regions (UTRs). Variants located in genomic regions might have different biological consequences in changing gene expression. Exonic variants (e.g., coding variant and 3′UTR variant) are often causative of diseases due to their influence on gene product. Variants harbored in the 3′UTR region where miRNAs perform their targeting function could potentially alter the binding relationships for target pairs, which could relate to disease causation. We gathered miRNA–mRNA targeting pairs from published studies and then employed the database of microRNA Target Site single nucleotide variants (SNVs) (dbMTS) to discover novel SNVs within the selected pairs. We identified a total of 183 SNVs for the 114 pairs of accurate miRNA–mRNA targeting pairs selected. Detailed bioinformatics analysis of the three genes with identified variants that were exclusively located in the 3′UTR section indicated their association with intellectual disability (ID). Our result showed an exceptionally high expression of GPR88 in brain tissues based on GTEx gene expression data, while WNT7A expression data were relatively high in brain tissues when compared to other tissues. Motif analysis for the 3′UTR region of WNT7A showed that five identified variants were well-conserved across three species (human, mouse, and rat); the motif that contains the variant identified in GPR88 is significant at the level of the 3′UTR of the human genome. Studies of pathways, protein–protein interactions, and relations to diseases further suggest potential association with intellectual disability of our discovered SNVs. Our results demonstrated that 3′UTR variants could change target interactions of miRNA–mRNA pairs in the context of their association with ID. We plan to automate the methods through developing a bioinformatics pipeline for identifying novel 3′UTR SNVs harbored by miRNA-targeted genes in the future. Full article
(This article belongs to the Special Issue Selected Papers from the International Conference on Intelligent Biology and Medicine (ICIBM 2020))
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15 pages, 385 KiB  
Article
A Linear Regression and Deep Learning Approach for Detecting Reliable Genetic Alterations in Cancer Using DNA Methylation and Gene Expression Data
by Saurav Mallik, Soumita Seth, Tapas Bhadra and Zhongming Zhao
Genes 2020, 11(8), 931; https://doi.org/10.3390/genes11080931 - 12 Aug 2020
Cited by 23 | Viewed by 4730
Abstract
DNA methylation change has been useful for cancer biomarker discovery, classification, and potential treatment development. So far, existing methods use either differentially methylated CpG sites or combined CpG sites, namely differentially methylated regions, that can be mapped to genes. However, such methylation signal [...] Read more.
DNA methylation change has been useful for cancer biomarker discovery, classification, and potential treatment development. So far, existing methods use either differentially methylated CpG sites or combined CpG sites, namely differentially methylated regions, that can be mapped to genes. However, such methylation signal mapping has limitations. To address these limitations, in this study, we introduced a combinatorial framework using linear regression, differential expression, deep learning method for accurate biological interpretation of DNA methylation through integrating DNA methylation data and corresponding TCGA gene expression data. We demonstrated it for uterine cervical cancer. First, we pre-filtered outliers from the data set and then determined the predicted gene expression value from the pre-filtered methylation data through linear regression. We identified differentially expressed genes (DEGs) by Empirical Bayes test using Limma. Then we applied a deep learning method, “nnet” to classify the cervical cancer label of those DEGs to determine all classification metrics including accuracy and area under curve (AUC) through 10-fold cross validation. We applied our approach to uterine cervical cancer DNA methylation dataset (NCBI accession ID: GSE30760, 27,578 features covering 63 tumor and 152 matched normal samples). After linear regression and differential expression analysis, we obtained 6287 DEGs with false discovery rate (FDR) <0.001. After performing deep learning analysis, we obtained average classification accuracy 90.69% (±1.97%) of the uterine cervical cancerous labels. This performance is better than that of other peer methods. We performed in-degree and out-degree hub gene network analysis using Cytoscape. We reported five top in-degree genes (PAIP2, GRWD1, VPS4B, CRADD and LLPH) and five top out-degree genes (MRPL35, FAM177A1, STAT4, ASPSCR1 and FABP7). After that, we performed KEGG pathway and Gene Ontology enrichment analysis of DEGs using tool WebGestalt(WEB-based Gene SeT AnaLysis Toolkit). In summary, our proposed framework that integrated linear regression, differential expression, deep learning provides a robust approach to better interpret DNA methylation analysis and gene expression data in disease study. Full article
(This article belongs to the Special Issue Selected Papers from the International Conference on Intelligent Biology and Medicine (ICIBM 2020))
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