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Intelligent Biology and Medicine (ICIBM 2021)
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Intelligent Biology and Medicine (ICIBM 2021)

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 2021) | Viewed by 13271

Special Issue Editor

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

Special Issue Information

Dear Colleagues,

The 2021 International Conference on Intelligent Biology and Medicine (ICIBM 2021) will be held August 8–10, 2021 either in Philadelphia, Pennsylvania, USA or in a virtual format, which will be determined at a later date. The webpage for this event is https://icibm2021.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 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The current Special Issue invites submissions on unpublished original work describing recent advances in all aspects of bioinformatics, systems biology, intelligent computing, and medical informatics, including but not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.
Prof. Dr. Yan Guo
Guest Editor

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

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Published Papers (4 papers)

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Research

13 pages, 4174 KiB  
Article
Revealing the Viral Community in the Hadal Sediment of the New Britain Trench
by Hui Zhou, Ping Chen, Mengjie Zhang, Jiawang Chen, Jiasong Fang and Xuan Li
Genes 2021, 12(7), 990; https://doi.org/10.3390/genes12070990 - 29 Jun 2021
Cited by 4 | Viewed by 2936
Abstract
Marine viruses are widely distributed and influence matter and energy transformation in ecosystems by modulating hosts’ metabolism. The hadal trenches represent the deepest marine habitat on Earth, for which the viral communities and related biogeochemical functions are least explored and poorly understood. Here, [...] Read more.
Marine viruses are widely distributed and influence matter and energy transformation in ecosystems by modulating hosts’ metabolism. The hadal trenches represent the deepest marine habitat on Earth, for which the viral communities and related biogeochemical functions are least explored and poorly understood. Here, using the sediment samples (8720 m below sea level) collected from the New Britain Trench (NBT), we investigated the viral community, diversity, and genetic potentials in the hadal sediment habitat for the first time by deep shotgun metagenomic sequencing. We found the NBT sediment viral community was dominated by Siphoviridae, Myoviridae, Podoviridae, Mimiviridae, and Phycodnaviridae, which belong to the dsDNA viruses. However, the large majority of them remained uncharacterized. We found the hadal sediment virome had some common components by comparing the hadal sediment viruses with those of hadal aquatic habitats and those of bathypelagic and terrestrial habitats. It was also distinctive in community structure and had many novel viral clusters not associated with the other habitual virome included in our analyses. Further phylogenetic analysis on its Caudovirales showed novel diversities, including new clades specially evolved in the hadal sediment habitat. Annotation of the NBT sediment viruses indicated the viruses might influence microbial hydrocarbon biodegradation and carbon and sulfur cycling via metabolic augmentation through auxiliary metabolic genes (AMGs). Our study filled in the knowledge gaps on the virome of the hadal sediment habitats and provided insight into the evolution and the potential metabolic functions of the hadal sediment virome. Full article
(This article belongs to the Special Issue Intelligent Biology and Medicine (ICIBM 2021))
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18 pages, 2458 KiB  
Article
kESVR: An Ensemble Model for Drug Response Prediction in Precision Medicine Using Cancer Cell Lines Gene Expression
by Abhishek Majumdar, Yueze Liu, Yaoqin Lu, Shaofeng Wu and Lijun Cheng
Genes 2021, 12(6), 844; https://doi.org/10.3390/genes12060844 - 30 May 2021
Cited by 6 | Viewed by 3258
Abstract
Background: Cancer cell lines are frequently used in research as in-vitro tumor models. Genomic data and large-scale drug screening have accelerated the right drug selection for cancer patients. Accuracy in drug response prediction is crucial for success. Due to data-type diversity and big [...] Read more.
Background: Cancer cell lines are frequently used in research as in-vitro tumor models. Genomic data and large-scale drug screening have accelerated the right drug selection for cancer patients. Accuracy in drug response prediction is crucial for success. Due to data-type diversity and big data volume, few methods can integrative and efficiently find the principal low-dimensional manifold of the high-dimensional cancer multi-omics data to predict drug response in precision medicine. Method: A novelty k-means Ensemble Support Vector Regression (kESVR) is developed to predict each drug response values for single patient based on cell-line gene expression data. The kESVR is a blend of supervised and unsupervised learning methods and is entirely data driven. It utilizes embedded clustering (Principal Component Analysis and k-means clustering) and local regression (Support Vector Regression) to predict drug response and obtain the global pattern while overcoming missing data and outliers’ noise. Results: We compared the efficiency and accuracy of kESVR to 4 standard machine learning regression models: (1) simple linear regression, (2) support vector regression (3) random forest (quantile regression forest) and (4) back propagation neural network. Our results, which based on drug response across 610 cancer cells from Cancer Cell Line Encyclopedia (CCLE) and Cancer Therapeutics Response Portal (CTRP v2), proved to have the highest accuracy (smallest mean squared error (MSE) measure). We next compared kESVR with existing 17 drug response prediction models based a varied range of methods such as regression, Bayesian inference, matrix factorization and deep learning. After ranking the 18 models based on their accuracy of prediction, kESVR ranks first (best performing) in majority (74%) of the time. As for the remaining (26%) cases, kESVR still ranked in the top five performing models. Conclusion: In this paper we introduce a novel model (kESVR) for drug response prediction using high dimensional cell-line gene expression data. This model outperforms current existing prediction models in terms of prediction accuracy and speed and overcomes overfitting. This can be used in future to develop a robust drug response prediction system for cancer patients using the cancer cell-lines guidance and multi-omics data. Full article
(This article belongs to the Special Issue Intelligent Biology and Medicine (ICIBM 2021))
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15 pages, 1472 KiB  
Article
Rewired Pathways and Disrupted Pathway Crosstalk in Schizophrenia Transcriptomes by Multiple Differential Coexpression Methods
by Hui Yu, Yan Guo, Jingchun Chen, Xiangning Chen, Peilin Jia and Zhongming Zhao
Genes 2021, 12(5), 665; https://doi.org/10.3390/genes12050665 - 29 Apr 2021
Cited by 6 | Viewed by 2914
Abstract
Transcriptomic studies of mental disorders using the human brain tissues have been limited, and gene expression signatures in schizophrenia (SCZ) remain elusive. In this study, we applied three differential co-expression methods to analyze five transcriptomic datasets (three RNA-Seq and two microarray datasets) derived [...] Read more.
Transcriptomic studies of mental disorders using the human brain tissues have been limited, and gene expression signatures in schizophrenia (SCZ) remain elusive. In this study, we applied three differential co-expression methods to analyze five transcriptomic datasets (three RNA-Seq and two microarray datasets) derived from SCZ and matched normal postmortem brain samples. We aimed to uncover biological pathways where internal correlation structure was rewired or inter-coordination was disrupted in SCZ. In total, we identified 60 rewired pathways, many of which were related to neurotransmitter, synapse, immune, and cell adhesion. We found the hub genes, which were on the center of rewired pathways, were highly mutually consistent among the five datasets. The combinatory list of 92 hub genes was generally multi-functional, suggesting their complex and dynamic roles in SCZ pathophysiology. In our constructed pathway crosstalk network, we found “Clostridium neurotoxicity” and “signaling events mediated by focal adhesion kinase” had the highest interactions. We further identified disconnected gene links underlying the disrupted pathway crosstalk. Among them, four gene pairs (PAK1:SYT1, PAK1:RFC5, DCTN1:STX1A, and GRIA1:MAP2K4) were normally correlated in universal contexts. In summary, we systematically identified rewired pathways, disrupted pathway crosstalk circuits, and critical genes and gene links in schizophrenia transcriptomes. Full article
(This article belongs to the Special Issue Intelligent Biology and Medicine (ICIBM 2021))
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11 pages, 1705 KiB  
Article
Novel lincRNA Discovery and Tissue-Specific Gene Expression across 30 Normal Human Tissues
by Xianfeng Chen and Zhifu Sun
Genes 2021, 12(5), 614; https://doi.org/10.3390/genes12050614 - 21 Apr 2021
Cited by 6 | Viewed by 2727
Abstract
Long non-coding RNAs (lncRNAs) are a large class of gene transcripts that do not code proteins; however, their functions are largely unknown and many new lncRNAs are yet to be discovered. Taking advantage of our previously developed, super-fast, novel lncRNA discovery pipeline, UClncR, [...] Read more.
Long non-coding RNAs (lncRNAs) are a large class of gene transcripts that do not code proteins; however, their functions are largely unknown and many new lncRNAs are yet to be discovered. Taking advantage of our previously developed, super-fast, novel lncRNA discovery pipeline, UClncR, and rich resources of GTEx RNA-seq data, we performed systematic novel lincRNA discovery for over 8000 samples across 30 tissue types. We conducted novel detection for each major tissue type first and then consolidated the novel discoveries from all tissue types. These novel lincRNs were profiled and analyzed along with known genes to identify tissue-specific genes in 30 major human tissue types. Thirteen sub-brain regions were also analyzed in a similar manner. Our analysis revealed thousands to tens of thousands of novel lincRNAs for each tissue type. These lincRNAs could define each tissue type’s identity and demonstrated their reliability and tissue-specific expression. Tissue-specific genes were identified for each major tissue type and sub-brain region. The tissue-specific genes clearly defined each respective tissue’s unique function and could be used to expand the interpretation of non-coding SNPs from genome-wide association (GWAS) studies. Full article
(This article belongs to the Special Issue Intelligent Biology and Medicine (ICIBM 2021))
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