Bioinformatics Research Research Articles

Decision tree classifier based on topological characteristics of subgraph for the mining of protein complexes from large scale PPI networks

The growing accessibility of large-scale protein interaction data demands extensive research to understand cell organization and its functioning at the network level. Bioinformatics and data mining researchers have extensively studied network clustering to examine the structural and operational features of protein protein interaction (PPI) networks. Clustering PPI networks has proven useful in numerous research over the past two decades for identifying functional modules, understanding the roles of previously unknown proteins, and other purposes. Protein complexes represent one of the essential cellular components for creating biological activities. Inferring protein complexes has been made more accessible by experimental approaches. We offer a novel method that integrates the classification model with local topological data, making it more reliable and efficient. This article describes a decision tree classifier based on topological characteristics of the subgraph for mining protein complexes. The proposed graph-based algorithm is an effective and efficient way to identify protein complexes from large-scale PPI networks. The performance of the proposed algorithm is observed in protein–protein interaction networks of yeast and human in the Database of Interacting Proteins (DIP) and the Biological General Repository for Interaction Datasets (BioGRID) using widely accepted benchmark protein complexes from the comprehensive resource of mammalian protein complexes (CORUM) and the comprehensive catalogue of yeast protein complexes (CYC2008). The outcomes demonstrate that our method can outperform the best-performing supervised, semi-supervised, and unsupervised approaches to detecting protein complexes.

Special Issue, Part I 18th International Symposium on Bioinformatics Research and Applications (ISBRA 2022).

Special Issue, Part I 18th International Symposium on Bioinformatics Research and Applications (ISBRA 2022).

A data science practicum to introduce undergraduate students to bioinformatics for research.

An explosion of data available in the life sciences has shifted the discipline toward genomics and quantitative data science research. Institutions of higher learning have been addressing this shift by modifying undergraduate curriculums resulting in an increasing number of bioinformatics courses and research opportunities for undergraduates. The goal of this study was to explore how a newly designed introductory bioinformatics seminar could leverage the combination of in-class instruction and independent research to build the practical skill sets of undergraduate students beginning their careers in the life sciences. Participants were surveyed to assess learning perceptions toward the dual curriculum. Most students had a neutral or positive interest in these topics before the seminar and reported increased interest after the seminar. Students had increases in confidence level in their bioinformatic proficiency and understanding of ethical principles for data/genomic science. By combining undergraduate research with directed bioinformatics skills, classroom seminars facilitated a connection between student's life sciences knowledge and emerging research tools in computational biology.

Developing approaches for search and analysis of CRISPR-Cas systems on the example of <i>Klebsiella pneumoniae</i> strains as a basis for creating personalized bacteriophage therapy

This paper proposes an algorithm for searching and analyzing the structures of CRISPR-Cas systems of bacteria and screening bacteriophages through spacers in CRISPR cassettes using bioinformatic research methods in the genomes of Klebsiella pneumoniae strains. The aim was to determine and study the structure of CRISPR-Cas systems of bacteria on the example of Klebsiella pneumoniae strains using bioinformatic research methods in order to develop approaches for the selection of target bacteriophages. The research object included 150 genome-wide sequences downloaded from the GenBank database. Of these sequences, CRISPR-Cas systems were detected in 52 strains, which amounted to 34.7%. Using several search algorithms in the CRISPR-Cas systems of the studied strains, the presence of one and two CRISPR cassette was determined in 46.2 and 53.8% of cases, respectively. In all the cases, a complete set of Cas genes characteristic of Type-I Subtype-I-E systems was identified next to the cassettes. The total number of the identified spacers was 1659, of which 281 spacers were repeated in two or more CRISPR loci, while 505 spacers had no repeats. The number of spacers in the cassettes ranged from 4 to 64. The analysis of the spacer composition in CRISPR cassettes of antibiotic-resistant and hospital strains provided information on their evolutionary history and on the bacteriophages which are targeted by their CRISPR systems. The developed bioinformatic analysis algorithm enables creating a platform for the development of personalized bacteriophage therapy technologies.

Leveraging transformers-based language models in proteome bioinformatics.

In recent years, the rapid growth of biological data has increased interest in using bioinformatics to analyze and interpret this data. Proteomics, which studies the structure, function, and interactions of proteins, is a crucial area of bioinformatics. Using natural language processing (NLP) techniques in proteomics is an emerging field that combines machine learning and text mining to analyze biological data. Recently, transformer-based NLP models have gained significant attention for their ability to process variable-length input sequences in parallel, using self-attention mechanisms to capture long-range dependencies. In this review paper, we discuss the recent advancements in transformer-based NLP models in proteome bioinformatics and examine their advantages, limitations, and potential applications to improve the accuracy and efficiency of various tasks. Additionally, we highlight the challenges and future directions of using these models in proteome bioinformatics research. Overall, this review provides valuable insights into the potential of transformer-based NLP models to revolutionize proteome bioinformatics.

Bioinformatics analysis and in vivo validation study of Ophiocordyceps sinensis (Berk.)G.H.Sungetal against lung adenocarcinoma

Ethnopharmacological relevanceLung adenocarcinoma (LUAD) is one of the main types of lung cancer. Ophiocordyceps sinensis has many potentially useful pharmacologic features, such as lung protection, and both anti-inflammatory and antioxidant activities. Aim of the studyThis study was conducted to investigate—using bioinformatics and in vivo experimental validation—the possible role of O. sinensis against LUAD. Materials and methodsWe obtained important targets of O. sinensis for the treatment of LUAD using network pharmacology techniques and deep mining of the TCGA database, and validated them by molecular docking techniques and in vivo experiments. ResultsThrough bioinformatics analysis and research, we screened BRCA1 and CCNE1 as important biomarkers for LUAD and as core targets of O. sinensis against LUAD. The non-small cell lung cancer signaling pathway, PI3K-Akt signaling pathway, and HIF-1 signaling pathway are potentially important pathways of O. sinensis against LUAD. The molecular docking results showed good binding between the active components in O. sinensis and the two core targets, and the in vivo experimental validation results indicated that O. sinensis had good inhibitory effects in the Lewis lung cancer (LLC) model. ConclusionsBRCA1 and CCNE1 are crucial biomarkers for LUAD and are important targets for O. sinensis to exert anti-LUAD effects.

Properties of INDETERMINATE DOMAIN Proteins from Physcomitrium patens: DNA-Binding, Interaction with GRAS Proteins, and Transcriptional Activity.

INDETERMINATE DOMAIN (IDD) proteins are plant-specific transcription factors that interact with GRAS proteins, such as DELLA and SHORT ROOT (SHR), to regulate target genes. The combination of IDD and DELLA proteins regulates genes involved in gibberellic acid (GA) synthesis and GA signaling, whereas the combination of IDD with the complex of SHR and SCARECROW, another GRAS protein, regulates genes involved in root tissue formation. Previous bioinformatic research identified seven IDDs, two DELLA, and two SHR genes in Physcomitrium patens, a model organism for non-vascular plants (bryophytes), which lack a GA signaling pathway and roots. In this study, DNA-binding properties and protein-protein interaction of IDDs from P. patens (PpIDD) were analyzed. Our results showed that the DNA-binding properties of PpIDDs were largely conserved between moss and seed plants. Four PpIDDs showed interaction with Arabidopsis DELLA (AtDELLA) proteins but not with PpDELLAs, and one PpIDD showed interaction with PpSHR but not with AtSHR. Moreover, AtIDD10 (JACKDAW) interacted with PpSHR but not with PpDELLAs. Our results indicate that DELLA proteins have modified their structure to interact with IDD proteins during evolution from moss lineage to seed plants, whereas the interaction of IDD and SHR was already present in moss lineage.

Design of crRNA to Regulate MicroRNAs Related to Metastasis in Colorectal Cancer Using CRISPR-C2c2 (Cas13a) Technique.

Colorectal cancer (CRC) is the third most prevalent cancer with the second-highest mortality rate worldwide. microRNAs (miRNAs) of cancer-derived exosomes have shown promising diagnosis potential. Recent studies have shown the metastatic potential of a specific group of microRNAs called metastasis. Therefore, down-regulation of miRNAs at the transcriptional level can reduce metastasis probability. The aim of this bioinformatics research is targeting of miRNAs precursors using CRISPR-C2c2 (Cas13a) technique. The C2c2 (Cas13a) enzyme structure was downloaded from the RCSB database, the sequence miRNAs and their precursors were collected from miRbase. The crRNAs were designed and evaluated for their specificity by using CRISPR-RT server. The modeling 3D structure of the designed crRNA was performed by RNAComposer server. Finally, HDOCK server was used to perform molecular docking to evaluate docked molecules' energy level and position. The crRNAs designed for miR-1280, miR-206, miR-195, miR- 371a, miR-34a, miR-27a, miR-224, miR-99b, miR-877, miR-495 and miR-384 that showed high structural similarity with the situation observed in normal and appropriate orientation was obtained. Despite high specificity, the correct orientation was not established in the case of crRNAs that designed to target miR-145, miR-378a, miR-199a, miR- 320a and miR-543. The predicted interactions between crRNAs and Cas13a enzyme showed that crRNAs have a strong potential to inhibit metastasis. Therefore, crRNAs may be considered as an effective anticancer agent for further research in drug development.

Identifying Effective Feature Selection Methods for Alzheimer's Disease Biomarker Gene Detection Using Machine Learning.

Alzheimer's disease (AD) is a complex genetic disorder that affects the brain and has been the focus of many bioinformatics research studies. The primary objective of these studies is to identify and classify genes involved in the progression of AD and to explore the function of these risk genes in the disease process. The aim of this research is to identify the most effective model for detecting biomarker genes associated with AD using several feature selection methods. We compared the efficiency of feature selection methods with an SVM classifier, including mRMR, CFS, the Chi-Square Test, F-score, and GA. We calculated the accuracy of the SVM classifier using validation methods such as 10-fold cross-validation. We applied these feature selection methods with SVM to a benchmark AD gene expression dataset consisting of 696 samples and 200 genes. The results indicate that the mRMR and F-score feature selection methods with SVM classifier achieved a high accuracy of around 84%, with a number of genes between 20 and 40. Furthermore, the mRMR and F-score feature selection methods with SVM classifier outperformed the GA, Chi-Square Test, and CFS methods. Overall, these findings suggest that the mRMR and F-score feature selection methods with SVM classifier are effective in identifying biomarker genes related to AD and could potentially lead to more accurate diagnosis and treatment of the disease.

Antagonizing Effects of Chromium Against Iron-Decreased Glucose Uptake by Regulating ROS-Mediated PI3K/Akt/GLUT4 Signaling Pathway in C2C12.

To investigate the effect of chromium and iron on glucose metabolism via the PI3K/Akt/GLUT4 signaling pathway. Skeletal muscle gene microarray data in T2DM (GSE7014) was selected using Gene Expression Omnibus database. Element-gene interaction datasets of chromium and iron were extracted from comparative toxicogenomics database (CTD). Gene ontology (GO)and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using DAVID online tool. Cell viability, insulin-stimulated glucose uptake, intracellular reactive oxygen species (ROS) level, and protein expression level were measured in C2C12 cells. The bioinformatics research indicated that PI3K/Akt signaling pathway participated in the effects of chromium and iron associated with T2DM. Insulin-stimulated glucose uptake level was significantly higher in chromium picolinate (Cr group) and lower in ammonium iron citrate (FA group) than that for the control group (P < 0.05); chromium picolinate + ammonium iron citrate (Cr + FA group) glucose uptake level was higher than that for the FA group (P < 0.05). Intracellular ROS level was significantly higher in the FAC group than that for the control group (P < 0.05), and that for the Cr + FA group was lower than that for the FA group (P < 0.05). p-PI3K/PI3K, p-Akt/Akt, and GLUT4 levels were significantly lower in the FA group than that for the control group (P < 0.05), and the Cr + FA group had higher levels than the FA group (P < 0.05). Chromium might have a protective effect on iron-induced glucose metabolism abnormalities through the ROS-mediated PI3K/Akt/GLUT4 signaling pathway.

Identification and validation of miR-583 and mir-877-5p as biomarkers in patients with breast cancer: an integrated experimental and bioinformatics research

ObjectivesBreast cancer (BC) is one of the most common cancers with a high mortality rate in women worldwide. The advantages of early cancer diagnosis are apparent, and it is a critical factor in increasing the patient’s life and survival. According to mounting evidence, microRNAs (miRNAs) may be crucial regulators of critical biological processes. miRNA dysregulation has been linked to the beginning and progression of various human malignancies, including BC, and can operate as tumor suppressors or oncomiRs. This study aimed to identify novel miRNA biomarkers in BC tissues and non-tumor adjacent tissues of patients with BC. Microarray datasets GSE15852 and GSE42568 for differentially expressed genes (DEGs) and GSE45666, GSE57897, and GSE40525 for differentially expressed miRNAs (DEMs) retrieved from the Gene Expression Omnibus (GEO) database were analyzed using “R” software. A protein-protein interaction (PPI) network was created to identify the hub genes. MirNet, miRTarBase, and MirPathDB databases were used to predict DEMs targeted genes. Functional enrichment analysis was used to demonstrate the topmost classifications of molecular pathways. The prognostic capability of selected DEMs was evaluated through a Kaplan-Meier plot. Moreover, the specificity and sensitivity of detected miRNAs to discriminate BC from adjacent controls were assessed by area under the curve (AUC) using the ROC curve analysis. In the last phase of this study, gene expression on 100 BC tissues and 100 healthy adjacent tissues were analyzed and calculated by using the Real-Time PCR method.ResultsThis study declared that miR-583 and miR-877-5p were downregulated in tumor samples in comparison to adjacent non-tumor samples (|logFC|< 0 and P ≤ 0.05). Accordingly, ROC curve analysis demonstrated the biomarker potential of miR-877-5p (AUC = 0.63) and miR-583 (AUC = 0.69). Our results showed that has-miR-583 and has-miR-877-5p could be potential biomarkers in BC.

The Post-Antibiotic Era: A New Dawn for Bacteriophages.

Phages are the most biologically diverse entities in the biosphere, infecting specific bacteria. Lytic phages quickly kill bacteria, while lysogenic phages integrate their genomes into bacteria and reproduce within the bacteria, participating in the evolution of natural populations. Thus, lytic phages are used to treat bacterial infections. However, due to the huge virus invasion, bacteria have also evolved a special immune mechanism (CRISPR-Cas systems, discovered in 1987). Therefore, it is necessary to develop phage cocktails and synthetic biology methods to infect bacteria, especially against multidrug-resistant bacteria infections, which are a major global threat. This review outlines the discovery and classification of phages and the associated achievements in the past century. The main applications of phages, including synthetic biology and PT, are also discussed, in addition to the effects of PT on immunity, intestinal microbes, and potential safety concerns. In the future, combining bioinformatics, synthetic biology, and classic phage research will be the way to deepen our understanding of phages. Overall, whether phages are an important element of the ecosystem or a carrier that mediates synthetic biology, they will greatly promote the progress of human society.

Prognostic significance of JAM 3 in gastric cancer: An observational study from TCGA and GEO.

Junctional adhesion molecule 3 (JAM3) can be used as a prognostic marker in multiple cancer types. However, the potential prognostic role of JAM3 in gastric cancer (GC) remains unclear. The purpose of this research was to gauge JAM3 expression and methylation as potential biomarkers for GC patient survival. Through bioinformatics research, we analyzed JAM3 expression, methylation, prognosis, and immune cell infiltrations. JAM3 methylation acts as a negative regulator of JAM3, leading to reduced expression of JAM3 in GC tissues relative to normal tissues. Patients with GC who expressed little JAM3 have a better chance of living a long time free of the disease, according to the Cancer Genome Atlas (TCGA) database. Through univariate and multivariate Cox regression analysis, inadequate JAM3 expression was labeled as an isolated indicator for overall survival (OS). The GSE84437 dataset was also used to confirm JAM3 prognostic role in GC, with consistent findings. A meta-analysis also found that low levels of JAM3 expression were significantly associated with longer OS. Finally, there was a strong correlation between JAM3 expression and a subset of immune cells. According to the TCGA database, low JAM3 expression could predict favorable OS and progression-free-survival (PFS) in GC patients (P < .05). The univariate and multivariate Cox regression demonstrated that low JAM3 expression was independent biomarker for OS (P < .05). Moreover, GSE84437 dataset was utilized to verify the prognostic role of JAM3 in GC, and the similar results were reached (P < .05). A meta-analysis revealed that low JAM3 expression was closely relevant to better OS. Finally, JAM3 expression exhibited a close correlation with some immune cells (P < .05). JAM3 might be a viable predictive biomarker and likely plays a crucial part in immune cell infiltration in individuals with GC.

Investigation of the Underlying Mechanism of Depression on Male Infertility: A Bioinformatics and Experimental Research Study

The impact of depression on spermatogenic function and protein expression was investigated by reference to a bioinformatics database for the prediction of key targets and pathways. Experimental validation was performed using a rat model established using the chronic restraint stress method. Organ coefficients and semen parameters were measured. Hematoxylin and eosin (HE) staining was performed to compare testicular tissue between model and control rats. Western blotting and RT–qPCR were conducted to evaluate protein and mRNA expression. As a result, depression and male infertility (MI) were found to share 81 common targets, and the PI3K/Akt/FOXO1 signaling pathway was involved in both conditions. Animal experiments showed testis and epididymis coefficients to be lower in the model group. HE staining showed damage to the seminiferous tubules of model rats. PI3K and p-Akt mRNA and protein were present at lower levels and FOXO1 at higher levels in the model group. Depression led to reduced spermatogenic function in rats and might be associated with differential expression of the PI3K/Akt/FOXO1 signaling pathway.

Harnessing Deep Learning for Omics in an Era of COVID-19

Omics data are multidimensional, heterogeneous, and high throughput. Robust computational methods and machine learning (ML)-based models offer new prospects to accelerate the data-to-knowledge trajectory. Deep learning (DL) is a powerful subset of ML inspired by brain structure and has created unprecedented momentum in bioinformatics and computational biology research. This article provides an overview of the current DL models applied to multi-omics data for both the beginner and the expert user. Additionally, COVID-19 will continue to impact planetary health as a pandemic and an endemic disease, with genomic and multi-omic pathophysiology. DL offers, therefore, new ways of harnessing systems biology research on COVID-19 diagnostics and therapeutics. Herein, we discuss, first, the statistical ML algorithms and essential deep architectures. Then, we review DL applications in multi-omics data analysis and their intersection with COVID-19. Finally, challenges and several promising directions are highlighted going forward in the current era of COVID-19.

GRACE: a comprehensive web-based platform for integrative single-cell transcriptome analysis.

Large-scale single-cell RNA sequencing (scRNA-seq) has emerged as a robust method for dissecting cellular heterogeneity at single-cell resolution. However, to meet the increasingly high computational demands of non-programming experts, a user-friendly, scalable, and accessible online platform for analyzing scRNA-seq data is urgently needed. Here, we have developed a web-based platform GRACE (GRaphical Analyzing Cell Explorer) (http://grace.flowhub.com.cn or http://grace.jflab.ac.cn:28080) that enables online massive single-cell transcriptome analysis, improving interactivity and reproducibility using high-quality visualization frameworks. GRACE provides easy access to interactive visualization, customized parameters, and publication-quality graphs. Furthermore, it comprehensively integrates preprocessing, clustering, developmental trajectory inference, cell-cell communication, cell-type annotation, subcluster analysis, and pathway enrichment. In addition to the website platform, we also provide a Docker version that can be easily deployed on private servers. The source code for GRACE is freely available at (https://github.com/th00516/GRACE). Documentation and video tutorials are accessible from website homepage (http://grace.flowhub.com.cn). GRACE can analyze massive scRNA-seq data more flexibly and be accessible to the scientific community. This platform fulfills the major gap that exists between experimental (wet lab) and bioinformatic (dry lab) research.

Functional Annotation Routines Used by ABRF Bioinformatics Core Facilities - Observations, Comparisons, and Considerations.

The functional annotation of gene lists is a common analysis routine required for most genomics experiments, and bioinformatics core facilities must support these analyses. In contrast to methods such as the quantitation of RNA-Seq reads or differential expression analysis, our research group noted a lack of consensus in our preferred approaches to functional annotation. To investigate this observation, we selected 4 experiments that represent a range of experimental designs encountered by our cores and analyzed those data with 6 tools used by members of the Association of Biomolecular Resource Facilities (ABRF) Genomic Bioinformatics Research Group (GBIRG). To facilitate comparisons between tools, we focused on a single biological result for each experiment. These results were represented by a gene set, and we analyzed these gene sets with each tool considered in our study to map the result to the annotation categories presented by each tool. In most cases, each tool produces data that would facilitate identification of the selected biological result for each experiment. For the exceptions, Fisher's exact test parameters could be adjusted to detect the result. Because Fisher's exact test is used by many functional annotation tools, we investigated input parameters and demonstrate that, while background set size is unlikely to have a significant impact on the results, the numbers of differentially expressed genes in an annotation category and the total number of differentially expressed genes under consideration are both critical parameters that may need to be modified during analyses. In addition, we note that differences in the annotation categories tested by each tool, as well as the composition of those categories, can have a significant impact on results.

Development of a Pharmacogenomics Model based on Support Vector Regression with Optimal Features Selection Approach to Determine the Initial Therapeutic Dose of Warfarin Anticoagulant Drug

Introduction: Using artificial intelligence tools in pharmacogenomics is one of the latest bioinformatics research fields. One of the most important drugs that determining its initial therapeutic dose is difficult is the anticoagulant warfarin. Warfarin is an oral anticoagulant that, due to its narrow therapeutic window and complex interrelationships of individual factors, the selection of its optimal dose is challenging . Method: Inaccuracy in determining the initial dose of warfarin will simply lead to thrombosis or severe bleeding and ultimately, patient death. Among the relatively successful methods of kernel-based estimation, comparison and identification of suitable kernels have not been researched. In the present research, while carefully examining this approach, different features of selection algorithms were analyzed based on expert opinions, and an appropriate subset of efficient predictor variables was identified for dose estimation. Results: In the current study, a dataset collected by the International Warfarin Consortium was used. The results showed that the support vector machine with a suitable kernel and a subset of the proposed features can successfully predict the ideal dose of warfarin for a significant percentage of patients with an error of approximately 0.7 mg per week . Conclusion: The estimation was conducted using the least squares version of the support vector regression based on a suitable kernel and feature selection strategy. In this way, a better approach for predicting the optimal therapeutic dose of warfarin was presented, which can significantly reduce the wrong dose error and its consequences .

Machine Learning Analysis of RNA-seq Data for Diagnostic and Prognostic Prediction of Colon Cancer

Data from omics studies have been used for prediction and classification of various diseases in biomedical and bioinformatics research. In recent years, Machine Learning (ML) algorithms have been used in many different fields related to healthcare systems, especially for disease prediction and classification tasks. Integration of molecular omics data with ML algorithms has offered a great opportunity to evaluate clinical data. RNA sequence (RNA-seq) analysis has been emerged as the gold standard for transcriptomics analysis. Currently, it is being used widely in clinical research. In our present work, RNA-seq data of extracellular vesicles (EV) from healthy and colon cancer patients are analyzed. Our aim is to develop models for prediction and classification of colon cancer stages. Five different canonical ML and Deep Learning (DL) classifiers are used to predict colon cancer of an individual with processed RNA-seq data. The classes of data are formed on the basis of both colon cancer stages and cancer presence (healthy or cancer). The canonical ML classifiers, which are k-Nearest Neighbor (kNN), Logistic Model Tree (LMT), Random Tree (RT), Random Committee (RC), and Random Forest (RF), are tested with both forms of the data. In addition, to compare the performance with canonical ML models, One-Dimensional Convolutional Neural Network (1-D CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM) DL models are utilized. Hyper-parameter optimizations of DL models are constructed by using genetic meta-heuristic optimization algorithm (GA). The best accuracy in cancer prediction is obtained with RC, LMT, and RF canonical ML algorithms as 97.33%. However, RT and kNN show 95.33% performance. The best accuracy in cancer stage classification is achieved with RF as 97.33%. This result is followed by LMT, RC, kNN, and RT with 96.33%, 96%, 94.66%, and 94%, respectively. According to the results of the experiments with DL algorithms, the best accuracy in cancer prediction is obtained with 1-D CNN as 97.67%. BiLSTM and LSTM show 94.33% and 93.67% performance, respectively. In classification of the cancer stages, the best accuracy is achieved with BiLSTM as 98%. 1-D CNN and LSTM show 97% and 94.33% performance, respectively. The results reveal that both canonical ML and DL models may outperform each other for different numbers of features.

Preface

The 2022 2nd International Conference on Computer Technology, Information Engineering and Electron Materials (CTIEEM 2022) was successfully held in Guangzhou, China from November 25th-27th, 2022 (virtual form). The conference is a leading academic conference and has the potential to bring new horizons and insights to the researchers and practitioners in the fields related to computer technology, information engineering and electron materials.CTIEEM 2022 created an academic platform for academic sharing and scientific innovation, brought together experts, scholars, and engineering and technical personnel from all over the world to present their research results and exchange ideas, promoted the industrial cooperation of academic achievements, and facilitated future business or research contacts among all the participants.The scope of the conference covered all areas of research in computer technology, information engineering and electron materials, including Wireless Sensor Network, Array Signal Processing Technology, Intelligent and Optimal Control System, Electrical Engineering Materials, Magnetic Material, etc. The conference brought together about 50 participants, primarily from China, but also from Malaysia, Korea, and other countries.A three-day program of the conference was divided into three sessions, including keynote speeches, oral presentations, and academic investigation. Each keynote speaker was given 30-40 minutes for keynote speeches and every presenter was given 10-15 minutes. Among them, Prof. Qin Xin from University of the Faroe Islands, Denmark is one of our keynote speakers and performed a wonderful keynote speech for us. His researches mainly focus on the design and analysis of sequential, parallel and distributed algorithms for various communication and optimization problems in wireless communication networks, as well as cryptography and digital currencies including quantum money. Moreover, he also investigates the combinatorial optimization problems with applications in Bioinformatics, Data Mining and Space Research and has produced more than 111 peer reviewed scientific papers, which are mainly published in leading international conferences and journals, such as ICALP, ACM PODC, IEEE MASS, ISAAC, IEEE ICC, etc. Currently, Prof. Qin Xin is serving on Management Committee Board of Denmark for several EU ICT projects. The insightful speeches of each keynote speakers triggered interesting discussions and a good number of informal talks between all the participants.We would like to thank all the participants in the 2022 2nd International Conference on Computer Technology, Information Engineering and Electron Materials. Besides, our special acknowledgement goes to the Journal of Physics: Conference Series, for the endeavor of all its colleagues in publishing this paper volume. We are sure that the proceedings of CTIEEM 2022 will serve as an important research source for references, which will contribute to not only scientific and engineering progress but also other new products and processes.The Committee of CTIEEM 2022List of Committee member are available in this pdf