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2nd International conference on Informatics, ICI 2023

作者： Minhass, Shubham Chauhan, Ritu Kaur, Harleen Amity University Amity Institute of Information Technology UP India Amity University IoT and Artificial Intelligence Lab Center for Computational Biology and Bioinformatics UP India Jamia Hamdard Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350343830

As Internet of Things (IoT) technologies proliferate and smart devices become more and more integrated into our daily lives and workplaces, it is becoming more and more important to address the complex issues of data security and privacy within smart homes and buildings. Even though the Internet of Things (IoT) has many benefits, such as remote control, real-time monitoring, and sophisticated security features, it also raises serious concerns about how sensitive data is protected. By examining the particular problems associated with protecting sensitive data in the IoT era and guaranteeing the continuous provision of dependable services within smart environments, this research paper seeks to close a significant knowledge gap. In order to do this, the research focuses on the complex data-centric features of smart ecosystems, including data processing, generation, and storage. This paper thoroughly examines the range of current IoT technologies, data management practices, and privacy challenges by using a qualitative research approach. It also offers a thorough examination of the privacy protections and security measures that are now in place in smart buildings and homes. This research focuses primarily on the interface between technology, security, and privacy of data. This work effectively fills in the significant research vacuum by clarifying the specific issues it seeks to solve. It provides useful advice and insights that can help individuals and organizations navigate the complex world of smart technologies, securely store their data, and realize the full potential of these game-changing advancements. © 2023 ieee.

关键词： Sensitive data

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Resting State Functional Connectivity Analysis During General Anesthesia: A High-Density EEG Study

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2022年第1期19卷 3-13页

作者： Bi, Hui Cao, Shumei Yan, Hanying Jiang, Zhongyi Zhang, Jun Zou, Ling Changzhou Univ Sch Informat Sci & Engn Changzhou 213164 Jiangsu Peoples R China Fudan Univ Huashan Hosp Dept Anesthesiol Shanghai 200040 Peoples R China Fudan Univ Shanghai Canc Ctr Dept Anesthesiol Shanghai 200032 Peoples R China Key Lab Brain Machine Collaborat Intelligence Fdn Hangzhou 310018 Zhejiang Peoples R China

The depth of anesthesia monitoring is helpful to guide administrations of general anesthetics during surgical procedures, however, the conventional 2-4 channels electroencephalogram (EEG) derived monitors have their limitations in monitoring conscious states due to low spatial resolution and suboptimal algorithm. In this study, 256-channel high-density EEG signals in 24 subjects receiving three types of general anesthetics (propofol, sevoflurane and ketamine) respectively were recorded both before and after anesthesia. The raw EEG signals were preprocessed by EEGLAB 14.0. Functional connectivity (FC) analysis by traditional coherence analysis (CA) method and a novel sparse representation (SR) method. And the network parameters, average clustering coefficient (ACC) and average shortest path length (ASPL) before and after anesthesia were calculated. The results show SR method find more significant FC differences in frontal and occipital cortices, and whole brain network (p<0.05). In contrast, CA can hardly obtain consistent ASPL in the whole brain network (p>0.05). Further, ASPL calculated by SR for whole brain connections in all of three anesthesia groups increased, which can be a unified EEG biomarker of general anesthetics-induced loss of consciousness (LOC). Therefore FC analysis based on SR analysis has better performance in distinguishing anesthetic-induced LOC from awake state.

关键词： Anesthesia electroencephalogram (EEG) functional connectivity sparse representation

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Prediction of Transposon Insertion Sites in the Human Genome Using Deep Learning Techniques 2

Prediction of Transposon Insertion Sites in the Human Genome...

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2nd ieee International conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Preeti, P. Nakul, Mihir Singh, Abhishek Dudeja, Chhavi Kanishka Rawal, Kamal Amity University Noida Centre for Computational Biology and Bioinformatics AIB Noida India

ISBN: (纸本)9798350368109

Transposable elements (TEs) account for approximately 44% of the human genome but less than.05% are known to be active. In contrast, these TEs are highly mobile in the genome which can result genomic rearrangement and several associated human diseases. It is notoriously difficult to predict the properties of DNA sequences at prospective insertion sites that make them good elements. This study proposes a deep learning model that leverages DNA properties to predict potential transposon insertion sites in nucleotide sequences, focusing on the active L1 and Alu elements identified in human chromosome 3 using RepeatMasker. Positive datasets comprise 30 bp upstream sequences of detected L1 and Alu elements, while negative datasets are randomly selected 30 bp sequences without these elements. Employing DNASCANNER v2, we computed 159 DNA properties encompassing nucleotide composition, structural traits, physicochemical attributes, thermodynamics, and mechanics. The data was partitioned into training, validation, and testing sets, scaled, and normalized. Fully Connected Layers were utilized to construct the deep learning models, which underwent hyperparameter tuning and training on the training subset. Evaluation metrics like sensitivity, precision, accuracy, recall, specificity, and AUC assessed model performance. The proposed Alu-L1-TIS DL model aims to provide a versatile predictive tool for identifying potential Alu or L1 TIS in the human genome. The web server for TIS predictions can be accessed at https://***/. This research contributes to a better understanding of the role of transposable elements in human diseases and genomic instability. © 2024 ieee.

关键词： Nucleotides

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Utilizing Comprehensive Biological Network to Improve Accuracy of computational Drug Repurposing

Utilizing Comprehensive Biological Network to Improve Accura...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Aleksandar Poleksic Department of Computer Science University of Northern Iowa Cedar Falls IA USA

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Drug repurposing can overcome challenges associated with expensive and time-consuming drug discovery pipelines by quickly finding new uses for existing drugs. computational drug repurposing is founded on the principle that similar drugs treat similar diseases. Thus, the research in the field has traditionally focused on defining and implementing measures of drug and disease similarities that closely resemble similarities in the observed drug pharmacological profiles and disease pathways. In this study, we incorporate comprehensive data on biological relationships and interactions to arrive at a fast and accurate method for completing the existing knowledge on drug-disease associations and, in turn, find new indications for FDA approved drugs.

关键词： Drugs Accuracy computational modeling Systems biology Pipelines Machine learning Predictive models Biomedical measurement Drug discovery Diseases

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scCDG: A Method Based on DAE and GCN for scRNA-Seq Data Analysis

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2022年第6期19卷 3685-3694页

作者： Wang, Hai-Yun Zhao, Jian-Ping Su, Yan-Sen Zheng, Chun-Hou Xinjiang Univ Coll Math & Syst Sci Urumqi 830046 Xinjiang Peoples R China Xinjiang Univ Inst Math & Phys Coll Math & Syst Sci Urumqi 830046 Xinjiang Peoples R China Anhui Univ Sch Artificial Intelligence Hefei 230039 Anhui Peoples R China

Identifying cell types is one of the main goals of single-cell RNA sequencing (scRNA-seq) analysis, and clustering is a common method for this item. However, the massive amount of data and the excess noise level bring challenge for single cell clustering. To address this challenge, in this paper, we introduced a novel method named single-cell clustering based on denoising autoencoder and graph convolution network (scCDG), which consists of two core models. The first model is a denoising autoencoder (DAE) used to fit the data distribution for data denoising. The second model is a graph autoencoder using graph convolution network (GCN), which projects the data into a low-dimensional space (compressed) preserving topological structure information and feature information in scRNA-seq data simultaneously. Extensive analysis on seven real scRNA-seq datasets demonstrate that scCDG outperforms state-of-the-art methods in some research sub-fields, including single cell clustering, visualization of transcriptome landscape, and trajectory inference.

关键词： Noise reduction Convolution Computer architecture Microprocessors Sequential analysis Encoding Decoding Clustering scRNA-seq denoising autoencoder (DAE) graph convolution network (GCN)

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Research on the mechanism of ncRNA in neurogenesis based on deep learning bioinformatics algorithm

Research on the mechanism of ncRNA in neurogenesis based on ...

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2024 International conference Optoelectronic Information and Optical Engineering, OIOE 2024

作者： Zhu, Mohan College of Life Sciences Sichuan University Sichuan China

ISBN: (数字)9781510688209

ISBN: (纸本)9781510688193

In the new era of science and technology, the research and application of deep learning algorithms for biological information are bringing revolutionary breakthroughs to the medical field. Recently, researchers have used deep learning techniques by integrating the mechanisms of long non-coding RNAs (lncRNAs) in neurogenesis, ensuring they work together to support normal development and functional maintenance of the nervous system. Non-coding RNA (ncRNA) constitutes a higher proportion of nucleic acids compared to coding RNA. Unlike protein-coding RNA, ncRNA is transcribed from DNA but does not undergo translation into proteins;instead, it functions as a functional RNA molecule. In recent years, research on ncRNA has deepened, and the development of ncRNA recognition algorithms has aided in understanding the functions of ncRNA, gradually revealing its growing importance in regulatory processes. An increasing number of ncRNAs have established their significant roles in the fields of disease, reprogramming, and differentiation. In disease research, researchers have developed databases and computational methods based on these databases to predict the relationships between diseases and related ncRNAs. In the field of reprogramming and differentiation, miRNAs, a well-studied subgroup of ncRNAs, have shown considerable progress. MicroRNAs can act on transcription factors (TFs) and other regulatory factors involved in reprogramming, thereby enhancing reprogramming efficiency. From a developmental biology perspective, certain miRNAs, such as miRNA-124 or let-7a, can interact with their target genes to regulate neural cell development. Recent studies have increasingly focused on the role of long non-coding RNAs (lncRNAs) in the fields of cellular reprogramming and developmental biology. Researchers have discovered that specific lncRNAs promote neuron differentiation, influence the expression of neighboring genes, and serve as key modulators of signaling pathways. Additionally

关键词： Stem cells

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AI-Driven Drug Discovery: Unravelling the Potential of Generative Adversarial Networks (GANs) in Pharmaceutical Research 3

AI-Driven Drug Discovery: Unravelling the Potential of Gener...

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3rd International conference on Technological Advancements in computational Sciences, ICTACS 2023

作者： Burri, Srinivasa Rao Diallo, Mamadou Yero Sharma, Lakshay Dutt, Vishal Western Union DenverCO United States Sharda University Computer Science & Engineering Greater Noida India Indraprastha Institute of Information Technology Computer Science & Applied Mathematics Delhi India Chandigarh University Department of Cse Punjab Mohali India

ISBN: (纸本)9798350342338

This paper examines how AI has revolutionised drug development and medical research using the ChEMBL dataset. The primary study areas are AI-driven therapeutic target identification., computational approaches in drug development., drug repurposing for COVID-19 therapies., and AI methods for natural leather flaw detection. Target selection must balance novelty and confidence., and AI-driven therapeutic target identification is considered. Structure-based virtual screening and profound learning predictions of ligand properties and target activities are considered for application in scaling up to broader chemical spaces. AI is used to discover new links between drugs., targets., and diseases and treat COVID-19. The paper also highlights this field's enforcement challenges and offers solutions. A Generative Adversarial Network (GAN)-based automatic flaw identification system for natural leather is another topic of study. The results show that the suggested strategy is economical and accurate., despite limitations and biases. AI has revolutionised medical diagnostics., medication development., and precision medicine., making this work meaningful. This paper's findings offer a cross-disciplinary perspective on artificial intelligence's potential in healthcare., revealing knowledge gaps and suggesting further research. © 2023 ieee.

关键词： Artificial intelligence Chemical biology Database (ChEMBL) Dataset computational Approaches COVID-19 Treatments Defect Identification Drug Discovery Drug Repurposing Generative Adversarial Network (GAN) Precision Medicine Therapeutic Target Identification

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Uncertainty Quantification Using Subsampling for Assembly-Free Estimates of Genomic Distance and Phylogenetic Relationships 26th

Uncertainty Quantification Using Subsampling for Assembly-F...

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26th International conference on Research in computational Molecular biology, RECOMB 2022

作者： Rachtman, Eleonora Sarmashghi, Shahab Bafna, Vineet Mirarab, Siavash Bioinformatics and Systems Biology Graduate Program UC San Diego La JollaCA United States Department of Electrical and Computer Engineering UC San Diego La JollaCA United States Department of Computer Science and Engineering UC San Diego La JollaCA United States

Computing the distance between two genomes, often without using alignments or even access to assembled sequences, is fundamental to many downstream analyses. © 2022, The Author(s), under exclusive license to Spri... 详细信息

ISBN: (纸本)9783031047480

关键词： Uncertainty analysis

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Guest Editorial for Selected Papers From BIOKDD 2019

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第3期18卷 809-810页

作者： Yan, Da Thankachan, Sharma Chen, Jake Y. Univ Alabama Birmingham Dept Comp Sci Birmingham AL 35294 USA Univ Cent Florida Dept Comp Sci Orlando FL 32816 USA Univ Alabama Birmingham UAB Dept Genet UAB Informat Inst Genet Comp Sci & Informat Sect Birmingham AL 35294 USA

The papers in this special section were presented at the 18th International Workshop on Data Mining in bioinformatics (BIOKDD), held in conjunction with the ACM SIGKDD International conference on Knowledge Discovery a... 详细信息

关键词： Special Issues And Sections bioinformatics Biological System Modeling Data Mining Big Data Meetings

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COMBING: Clustering in Oncology for Mathematical and Biological Identification of Novel Gene Signatures

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2022年第6期19卷 3317-3331页

作者： Battistella, Enzo Vakalopoulou, Maria Sun, Roger Estienne, Theo Lerousseau, Marvin Nikolaev, Sergey Andres, Emilie Alvarez Carre, Alexandre Niyoteka, Stephane Robert, Charlotte Paragios, Nikos Deutsch, Eric Univ Paris Saclay Cent Supelec Math & Informat Complexite & Syst F-91190 Gif Sur Yvette France Univ Paris Saclay Cent Supelec Inria F-91190 Gif Sur Yvette France Gustave Roussy Canc Campus Noesia Ctr Artificial Intelligence Radiat Therapy Gustave Roussy Cent Supelec TheraPanacea F-94805 Villejuif France Univ Paris Saclay Inst Gustave Roussy INSERM U1030Mol Radiotherapy & Innovat Therapeut F-94800 Villejuif France Univ Paris 07 St Louis Hosp F-75010 Paris France

Precisionmedicine is a paradigm shift in healthcare relying heavily on genomics data. However, the complexity of biological interactions, the large number of genes aswell as the lack of comparisons on the analysis of data, remain a tremendous bottleneck regarding clinical adoption. In this paper, we introduce a novel, automatic and unsupervised framework to discover low-dimensional gene biomarkers. Our method is based on the LP-Stability algorithm, a high dimensional center-based unsupervised clustering algorithm. It offers modularity as concerns metric functions and scalability, while being able to automatically determine the best number of clusters. Our evaluation includes both mathematical and biological criteria to define a quantitativemetric. The recovered signature is applied to a variety of biological tasks, including screening of biological pathways and functions, and characterization relevance on tumor types and subtypes. Quantitative comparisons among different distance metrics, commonly used clusteringmethods and a referential gene signature used in the literature, confirmstate of the art performance of our approach. In particular, our signature, based on 27 genes, reports at least 30 times bettermathematical significance (average Dunn's Index) and 25% better biological significance (average Enrichment in Protein-Protein Interaction) than those produced by other referential clustering methods. Finally, our signature reports promising results on distinguishing immune inflammatory and immune desert tumors, while reporting a high balanced accuracy of 92% on tumor types classification and averaged balanced accuracy of 68% on tumor subtypes classification, which represents, respectively 7% and 9% higher performance compared to the referential signature.

关键词： Clustering algorithms biology Tumors Measurement Genomics bioinformatics Indexes Clustering predictive signature biomarkers genomics multi-tumor association

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