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Selected Papers from the International Conference on Intelligent Biology and Medicine (ICIBM 2022)

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

Deadline for manuscript submissions: closed (15 September 2022) | Viewed by 24989

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Prof. Dr. Yan Guo

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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. Jinchuan Xing

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Human Genetics Institute of New Jersey, Piscataway, NJ, USA
Interests: human genomic variation; mobile DNA elements; human disease
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Special Issue Information

Dear Colleagues,

The 2022 International Conference on Intelligent Biology and Medicine (ICIBM 2022) will be held on August 7–9, 2022 in Philadelphia, PA, USA. The webpage for this event is https://icibm2022.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The current Special Issue invites submissions on unpublished original work describing recent advances in all aspects of bioinformatics, systems biology, intelligent computing, and medical informatics, including but not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:

Genomics and genetics, including integrative and functional genomics, and genome evolution.
Next-generation sequencing data analysis, applications, and software and tools.
Big data science including storage, analysis, modeling, visualization, and cloud.
Precision medicine, translational bioinformatics, and medical informatics.
Drug discovery, design, and repurposing.
Single-cell sequencing data analysis.
Microbiome and metagenomics.

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

Prof. Dr. Yan Guo
Prof. Dr. Jinchuan Xing
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
integrative and functional genomics
genome evolution
NGS analysis
precision medicine
translational research
drug discovery
single cell sequencing data analysis
microbiome and metagenomics
synthetic biological systems
biological processes, pathways and networks
EHR-based phenotyping

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Published Papers (10 papers)

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Research

28 pages, 3822 KiB

Open AccessArticle

PPIGCF: A Protein–Protein Interaction-Based Gene Correlation Filter for Optimal Gene Selection

by Soumen Kumar Pati, Manan Kumar Gupta, Ayan Banerjee, Saurav Mallik and Zhongming Zhao

Genes 2023, 14(5), 1063; https://doi.org/10.3390/genes14051063 - 10 May 2023

Cited by 4 | Viewed by 2027

Abstract

Biological data at the omics level are highly complex, requiring powerful computational approaches to identifying significant intrinsic characteristics to further search for informative markers involved in the studied phenotype. In this paper, we propose a novel dimension reduction technique, protein–protein interaction-based gene correlation filtration (PPIGCF), which builds on gene ontology (GO) and protein–protein interaction (PPI) structures to analyze microarray gene expression data. PPIGCF first extracts the gene symbols with their expression from the experimental dataset, and then, classifies them based on GO biological process (BP) and cellular component (CC) annotations. Every classification group inherits all the information on its CCs, corresponding to the BPs, to establish a PPI network. Then, the gene correlation filter (regarding gene rank and the proposed correlation coefficient) is computed on every network and eradicates a few weakly correlated genes connected with their corresponding networks. PPIGCF finds the information content (IC) of the other genes related to the PPI network and takes only the genes with the highest IC values. The satisfactory results of PPIGCF are used to prioritize significant genes. We performed a comparison with current methods to demonstrate our technique’s efficiency. From the experiment, it can be concluded that PPIGCF needs fewer genes to reach reasonable accuracy (~99%) for cancer classification. This paper reduces the computational complexity and enhances the time complexity of biomarker discovery from datasets. Full article

(This article belongs to the Special Issue Selected Papers from the International Conference on Intelligent Biology and Medicine (ICIBM 2022))

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13 pages, 4552 KiB

Open AccessArticle

DelInsCaller: An Efficient Algorithm for Identifying Delins and Estimating Haplotypes from Long Reads with High Level of Sequencing Errors

by Shenjie Wang, Xuanping Zhang, Geng Qiang and Jiayin Wang

Genes 2023, 14(1), 4; https://doi.org/10.3390/genes14010004 - 20 Dec 2022

Cited by 3 | Viewed by 1873

Abstract

Delins, as known as complex indel, is a combined genomic structural variation formed by deleting and inserting DNA fragments at a common genomic location. Recent studies emphasized the importance of delins in cancer diagnosis and treatment. Although the long reads from PacBio CLR sequencing significantly facilitate delins calling, the existing approaches still encounter computational challenges from the high level of sequencing errors, and often introduce errors in genotyping and phasing delins. In this paper, we propose an efficient algorithmic pipeline, named delInsCaller, to identify delins on haplotype resolution from the PacBio CLR sequencing data. delInsCaller design a fault-tolerant method by calculating a variation density score, which helps to locate the candidate mutational regions under a high-level of sequencing errors. It adopts a base association-based contig splicing method, which facilitates contig splicing in the presence of false-positive interference. We conducted a series of experiments on simulated datasets, and the results showed that delInsCaller outperformed several state-of-the-art approaches, e.g., SVseq3, across a wide range of parameter settings, such as read depth, sequencing error rates, etc. delInsCaller often obtained higher f-measures than other approaches; specifically, it was able to maintain advantages at ~15% sequencing errors. delInsCaller was able to significantly improve the N50 values with almost no loss of haplotype accuracy compared with the existing approach as well. Full article

(This article belongs to the Special Issue Selected Papers from the International Conference on Intelligent Biology and Medicine (ICIBM 2022))

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15 pages, 1628 KiB

Open AccessArticle

Characterizing Macrophages Diversity in COVID-19 Patients Using Deep Learning

by Mario A. Flores, Karla Paniagua, Wenjian Huang, Ricardo Ramirez, Leonardo Falcon, Andy Liu, Yidong Chen, Yufei Huang and Yufang Jin

Genes 2022, 13(12), 2264; https://doi.org/10.3390/genes13122264 - 1 Dec 2022

Cited by 1 | Viewed by 2479

Abstract

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiological agent responsible for coronavirus disease 2019 (COVID-19), has affected the lives of billions and killed millions of infected people. This virus has been demonstrated to have different outcomes among individuals, with some of them presenting a mild infection, while others present severe symptoms or even death. The identification of the molecular states related to the severity of a COVID-19 infection has become of the utmost importance to understanding the differences in critical immune response. In this study, we computationally processed a set of publicly available single-cell RNA-Seq (scRNA-Seq) data of 12 Bronchoalveolar Lavage Fluid (BALF) samples diagnosed as having a mild, severe, or no infection, and generated a high-quality dataset that consists of 63,734 cells, each with 23,916 genes. We extended the cell-type and sub-type composition identification and our analysis showed significant differences in cell-type composition in mild and severe groups compared to the normal. Importantly, inflammatory responses were dramatically elevated in the severe group, which was evidenced by the significant increase in macrophages, from 10.56% in the normal group to 20.97% in the mild group and 34.15% in the severe group. As an indicator of immune defense, populations of T cells accounted for 24.76% in the mild group and decreased to 7.35% in the severe group. To verify these findings, we developed several artificial neural networks (ANNs) and graph convolutional neural network (GCNN) models. We showed that the GCNN models reach a prediction accuracy of the infection of 91.16% using data from subtypes of macrophages. Overall, our study indicates significant differences in the gene expression profiles of inflammatory response and immune cells of severely infected patients. Full article

(This article belongs to the Special Issue Selected Papers from the International Conference on Intelligent Biology and Medicine (ICIBM 2022))

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17 pages, 3949 KiB

Open AccessArticle

Time-Varying Gene Expression Network Analysis Reveals Conserved Transition States in Hematopoietic Differentiation between Human and Mouse

by Shouguo Gao, Ye Chen, Zhijie Wu, Sachiko Kajigaya, Xujing Wang and Neal S. Young

Genes 2022, 13(10), 1890; https://doi.org/10.3390/genes13101890 - 18 Oct 2022

Viewed by 2175

Abstract

(1) Background: analyses of gene networks can elucidate hematopoietic differentiation from single-cell gene expression data, but most algorithms generate only a single, static network. Because gene interactions change over time, it is biologically meaningful to examine time-varying structures and to capture dynamic, even transient states, and cell-cell relationships. (2) Methods: a transcriptomic atlas of hematopoietic stem and progenitor cells was used for network analysis. After pseudo-time ordering with Monocle 2, LOGGLE was used to infer time-varying networks and to explore changes of differentiation gene networks over time. A range of network analysis tools were used to examine properties and genes in the inferred networks. (3) Results: shared characteristics of attributes during the evolution of differentiation gene networks showed a “U” shape of network density over time for all three branches for human and mouse. Differentiation appeared as a continuous process, originating from stem cells, through a brief transition state marked by fewer gene interactions, before stabilizing in a progenitor state. Human and mouse shared hub genes in evolutionary networks. (4) Conclusions: the conservation of network dynamics in the hematopoietic systems of mouse and human was reflected by shared hub genes and network topological changes during differentiation. Full article

(This article belongs to the Special Issue Selected Papers from the International Conference on Intelligent Biology and Medicine (ICIBM 2022))

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13 pages, 1092 KiB

Open AccessArticle

Combination of Serum and Plasma Biomarkers Could Improve Prediction Performance for Alzheimer’s Disease

by Fan Zhang, Melissa Petersen, Leigh Johnson, James Hall and Sid E. O’Bryant

Genes 2022, 13(10), 1738; https://doi.org/10.3390/genes13101738 - 27 Sep 2022

Cited by 7 | Viewed by 2042

Abstract

Alzheimer’s disease (AD) can be predicted either by serum or plasma biomarkers, and a combination may increase predictive power, but due to the high complexity of machine learning, it may also incur overfitting problems. In this paper, we investigated whether combining serum and plasma biomarkers with feature selection could improve prediction performance for AD. 150 D patients and 150 normal controls (NCs) were enrolled for a serum test, and 100 patients and 100 NCs were enrolled for the plasma test. Among these, 79 ADs and 65 NCs had serum and plasma samples in common. A 10 times repeated 5-fold cross-validation model and a feature selection method were used to overcome the overfitting problem when serum and plasma biomarkers were combined. First, we tested to see if simply adding serum and plasma biomarkers improved prediction performance but also caused overfitting. Then we employed a feature selection algorithm we developed to overcome the overfitting problem. Lastly, we tested the prediction performance in a 10 times repeated 5-fold cross validation model for training and testing sets. We found that the combined biomarkers improved AD prediction but also caused overfitting. A further feature selection based on the combination of serum and plasma biomarkers solved the problem and produced an even higher prediction performance than either serum or plasma biomarkers on their own. The combined feature-selected serum–plasma biomarkers may have critical implications for understanding the pathophysiology of AD and for developing preventative treatments. Full article

(This article belongs to the Special Issue Selected Papers from the International Conference on Intelligent Biology and Medicine (ICIBM 2022))

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25 pages, 4208 KiB

Open AccessArticle

mintRULS: Prediction of miRNA–mRNA Target Site Interactions Using Regularized Least Square Method

by Sushil Shakyawar, Siddesh Southekal and Chittibabu Guda

Genes 2022, 13(9), 1528; https://doi.org/10.3390/genes13091528 - 25 Aug 2022

Cited by 3 | Viewed by 2469

Abstract

Identification of miRNA–mRNA interactions is critical to understand the new paradigms in gene regulation. Existing methods show suboptimal performance owing to inappropriate feature selection and limited integration of intuitive biological features of both miRNAs and mRNAs. The present regularized least square-based method, mintRULS, employs features of miRNAs and their target sites using pairwise similarity metrics based on free energy, sequence and repeat identities, and target site accessibility to predict miRNA-target site interactions. We hypothesized that miRNAs sharing similar structural and functional features are more likely to target the same mRNA, and conversely, mRNAs with similar features can be targeted by the same miRNA. Our prediction model achieved an impressive AUC of 0.93 and 0.92 in LOOCV and LmiTOCV settings, respectively. In comparison, other popular tools such as miRDB, TargetScan, MBSTAR, RPmirDIP, and STarMir scored AUCs at 0.73, 0.77, 0.55, 0.84, and 0.67, respectively, in LOOCV setting. Similarly, mintRULS outperformed other methods using metrics such as accuracy, sensitivity, specificity, and MCC. Our method also demonstrated high accuracy when validated against experimentally derived data from condition- and cell-specific studies and expression studies of miRNAs and target genes, both in human and mouse. Full article

(This article belongs to the Special Issue Selected Papers from the International Conference on Intelligent Biology and Medicine (ICIBM 2022))

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19 pages, 19168 KiB

Open AccessArticle

Mining High-Level Imaging Genetic Associations via Clustering AD Candidate Variants with Similar Brain Association Patterns

by Ruiming Wu, Jingxuan Bao, Mansu Kim, Andrew J. Saykin, Jason H. Moore, Li Shen and on behalf of ADNI

Genes 2022, 13(9), 1520; https://doi.org/10.3390/genes13091520 - 24 Aug 2022

Cited by 2 | Viewed by 1941

Abstract

Brain imaging genetics examines associations between imaging quantitative traits (QTs) and genetic factors such as single nucleotide polymorphisms (SNPs) to provide important insights into the pathogenesis of Alzheimer’s disease (AD). The individual level SNP-QT signals are high dimensional and typically have small effect sizes, making them hard to be detected and replicated. To overcome this limitation, this work proposes a new approach that identifies high-level imaging genetic associations through applying multigraph clustering to the SNP-QT association maps. Given an SNP set and a brain QT set, the association between each SNP and each QT is evaluated using a linear regression model. Based on the resulting SNP-QT association map, five SNP–SNP similarity networks (or graphs) are created using five different scoring functions, respectively. Multigraph clustering is applied to these networks to identify SNP clusters with similar association patterns with all the brain QTs. After that, functional annotation is performed for each identified SNP cluster and its corresponding brain association pattern. We applied this pipeline to an AD imaging genetic study, which yielded promising results. For example, in an association study between 54 AD SNPs and 116 amyloid QTs, we identified two SNP clusters with one responsible for amyloid beta clearances and the other regulating amyloid beta formation. These high-level findings have the potential to provide valuable insights into relevant genetic pathways and brain circuits, which can help form new hypotheses for more detailed imaging and genetics studies in independent cohorts. Full article

(This article belongs to the Special Issue Selected Papers from the International Conference on Intelligent Biology and Medicine (ICIBM 2022))

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14 pages, 3165 KiB

Open AccessArticle

Prediction of the Effects of Missense Mutations on Human Myeloperoxidase Protein Stability Using In Silico Saturation Mutagenesis

by Adebiyi Sobitan, William Edwards, Md Shah Jalal, Ayanfeoluwa Kolawole, Hemayet Ullah, Atanu Duttaroy, Jiang Li and Shaolei Teng

Genes 2022, 13(8), 1412; https://doi.org/10.3390/genes13081412 - 8 Aug 2022

Cited by 2 | Viewed by 2727

Abstract

Myeloperoxidase (MPO) is a heme peroxidase with microbicidal properties. MPO plays a role in the host’s innate immunity by producing reactive oxygen species inside the cell against foreign organisms. However, there is little functional evidence linking missense mutations to human diseases. We utilized in silico saturation mutagenesis to generate and analyze the effects of 10,811 potential missense mutations on MPO stability. Our results showed that ~71% of the potential missense mutations destabilize MPO, and ~8% stabilize the MPO protein. We showed that G402W, G402Y, G361W, G402F, and G655Y would have the highest destabilizing effect on MPO. Meanwhile, D264L, G501M, D264H, D264M, and G501L have the highest stabilization effect on the MPO protein. Our computational tool prediction showed the destabilizing effects in 13 out of 14 MPO missense mutations that cause diseases in humans. We also analyzed putative post-translational modification (PTM) sites on the MPO protein and mapped the PTM sites to disease-associated missense mutations for further analysis. Our analysis showed that R327H associated with frontotemporal dementia and R548W causing generalized pustular psoriasis are near these PTM sites. Our results will aid further research into MPO as a biomarker for human complex diseases and a candidate for drug target discovery. Full article

(This article belongs to the Special Issue Selected Papers from the International Conference on Intelligent Biology and Medicine (ICIBM 2022))

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16 pages, 2312 KiB

Open AccessArticle

MicroRNA and MicroRNA-Target Variants Associated with Autism Spectrum Disorder and Related Disorders

by Anthony Wong, Anbo Zhou, Xiaolong Cao, Vaidhyanathan Mahaganapathy, Marco Azaro, Christine Gwin, Sherri Wilson, Steven Buyske, Christopher W. Bartlett, Judy F. Flax, Linda M. Brzustowicz and Jinchuan Xing

Genes 2022, 13(8), 1329; https://doi.org/10.3390/genes13081329 - 26 Jul 2022

Cited by 4 | Viewed by 3071

Abstract

Autism spectrum disorder (ASD) is a childhood neurodevelopmental disorder with a complex and heterogeneous genetic etiology. MicroRNA (miRNA), a class of small non-coding RNAs, could regulate ASD risk genes post-transcriptionally and affect broad molecular pathways related to ASD and associated disorders. Using whole-genome sequencing, we analyzed 272 samples in 73 families in the New Jersey Language and Autism Genetics Study (NJLAGS) cohort. Families with at least one ASD patient were recruited and were further assessed for language impairment, reading impairment, and other associated phenotypes. A total of 5104 miRNA variants and 1,181,148 3′ untranslated region (3′ UTR) variants were identified in the dataset. After applying several filtering criteria, including population allele frequency, brain expression, miRNA functional regions, and inheritance patterns, we identified high-confidence variants in five brain-expressed miRNAs (targeting 326 genes) and 3′ UTR miRNA target regions of 152 genes. Some genes, such as SCP2 and UCGC, were identified in multiple families. Using Gene Ontology overrepresentation analysis and protein–protein interaction network analysis, we identified clusters of genes and pathways that are important for neurodevelopment. The miRNAs and miRNA target genes identified in this study are potentially involved in neurodevelopmental disorders and should be considered for further functional studies. Full article

(This article belongs to the Special Issue Selected Papers from the International Conference on Intelligent Biology and Medicine (ICIBM 2022))

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15 pages, 1293 KiB

Open AccessArticle

Drug-Target Network Study Reveals the Core Target-Protein Interactions of Various COVID-19 Treatments

by Yulin Dai, Hui Yu, Qiheng Yan, Bingrui Li, Andi Liu, Wendao Liu, Xiaoqian Jiang, Yejin Kim, Yan Guo and Zhongming Zhao

Genes 2022, 13(7), 1210; https://doi.org/10.3390/genes13071210 - 6 Jul 2022

Viewed by 2814

Abstract

The coronavirus disease 2019 (COVID-19) pandemic has caused a dramatic loss of human life and devastated the worldwide economy. Numerous efforts have been made to mitigate COVID-19 symptoms and reduce the death rate. We conducted literature mining of more than 250 thousand published works and curated the 174 most widely used COVID-19 medications. Overlaid with the human protein–protein interaction (PPI) network, we used Steiner tree analysis to extract a core subnetwork that grew from the pharmacological targets of ten credible drugs ascertained by the CTD database. The resultant core subnetwork consisted of 34 interconnected genes, which were associated with 36 drugs. Immune cell membrane receptors, the downstream cellular signaling cascade, and severe COVID-19 symptom risk were significantly enriched for the core subnetwork genes. The lung mast cell was most enriched for the target genes among 1355 human tissue-cell types. Human bronchoalveolar lavage fluid COVID-19 single-cell RNA-Seq data highlighted the fact that T cells and macrophages have the most overlapping genes from the core subnetwork. Overall, we constructed an actionable human target-protein module that mainly involved anti-inflammatory/antiviral entry functions and highly overlapped with COVID-19-severity-related genes. Our findings could serve as a knowledge base for guiding drug discovery or drug repurposing to confront the fast-evolving SARS-CoV-2 virus and other severe infectious diseases. Full article

(This article belongs to the Special Issue Selected Papers from the International Conference on Intelligent Biology and Medicine (ICIBM 2022))

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