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ieee International conference on bioinformatics and computational biology (ICBCB)

作者： Haonan Tong Ke Liu Zhixin Ren Zhongxiang Sun Luyang Liu Hongyin Zhu Qunyang Lin Chuang Zhang Inspur Electronic Information Industry Co. Ltd. Beijing China

ISBN: (数字)9798350375749

ISBN: (纸本)9798350375756

Pyroptosis, a form of programmed cell death, plays a pivotal role in various physiological and pathological processes, including autoimmune diseases like psoriasis and cutaneous lupus erythematosus (CLE). This study aims to analyze the expression patterns of pyroptosis-related genes in psoriasis and CLE using gene profiling techniques, exploring their roles in the onset and progression of these diseases, and identifying potential therapeutic drugs. We acquired gene expression datasets of psoriasis and CLE patients, both from lesional and nonlesional skin areas, using the NCBI Gene Expression Omnibus database. Differentially expressed genes (DEGs) were selected using the limma algorithm, followed by enrichment analysis of these DEGs with WebGestalt. Pyroptosis-related genes were collected to construct transcriptional regulatory networks using GENIE3, and the DGIdb database was used to predict potential therapeutic drugs. We found that psoriasis and CLE patients share pyroptosis genes, including CHMP7 and CASP8. Psoriasis DEGs are involved in biological processes such as cell migration, proliferation, and signal transduction regulation, while CLE DEGs involve immune response, defense mechanisms, and immune system regulation. In the transcriptional regulatory network, transcription factors such as STAT3, VD3, and MYC play key regulatory roles and are potential therapeutic targets. Drugs like Cyclosporine and Methotrexate can target these factors for treatment. Together, this work investigated the mechanisms and biological processes common and distinct in psoriasis and CLE, revealing the regulatory function of pyroptosis in these diseases and providing new insights and methods for their treatment.

关键词： Drugs Databases Psoriasis Biological processes Prediction algorithms Regulation Skin Physiology Gene expression bioinformatics

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Multi-Scale Capsule Network for Predicting DNA-Protein Binding Sites

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第5期18卷 1793-1800页

作者： Zhang, Qinhu Yu, Wenbo Han, Kyungsook Nandi, Asoke K. Huang, De-Shuang Tongji Univ Inst Machine Learning & Syst Biol Sch Elect & Informat Engn 4800 Caoan Rd Shanghai 201804 Peoples R China Tongji Univ Sch Life Sci & Technol Siping Rd 1239 Shanghai 200092 Peoples R China Inha Univ Sch Comp Sci & Engn Incheon 21000 South Korea Brunel Univ London Dept Elect & Comp Engn Uxbridge UB8 3PH Middx England

Discovering DNA-protein binding sites, also known as motif discovery, is the foundation for further analysis of transcription factors (TFs). Deep learning algorithms such as convolutional neural networks (CNN) have been introduced to motif discovery task and have achieved state-of-art performance. However, due to the limitations of CNN, motif discovery methods based on CNN do not take full advantage of large-scale sequencing data generated by high-throughput sequencing technology. Hence, in this paper we propose multi-scale capsule network architecture (MSC) integrating multi-scale CNN, a variant of CNN able to extract motif features of different lengths, and capsule network, a novel type of artificial neural network architecture aimed at improving CNN. The proposed method is tested on real ChIP-seq datasets and the experimental results show a considerable improvement compared with two well-tested deep learning-based sequence model, DeepBind and Deepsea.

关键词： DNA Machine learning computational modeling Feature extraction Kernel Convolution bioinformatics Multi-scale capsule network transcription factors binding specificity

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The motion planning neural circuit in goal-directed navigation as Lie group operator search 38

The motion planning neural circuit in goal-directed navigati...

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38th conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zuo, Junfeng Wu, Ying Nian Wu, Si Zhang, Wen-Hao Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary Studies School of Psychology and Cognitive Sciences IDG/McGovern Institute for Brain Research Center of Quantitative Biology Peking University China Department of Statistics University of California Los Angeles United States Lyda Hill Department of Bioinformatics UT Southwestern Medical Center United States O'Donnell Brain Institute UT Southwestern Medical Center United States

The information processing in the brain and embodied agents form a sensory-action loop to interact with the world. An important step in the loop is motion planning which selects motor actions based on the current world state and task need. In goal-directed navigation, the brain chooses and generates motor actions to bring the current state into the goal state. It is unclear about the neural circuit mechanism of motor action selection, nor its underlying theory. The present study formulates the motion planning as a Lie group operator search problem, and uses the 1D rotation group as an example to provide insight into general operator search in neural circuits. We found the abstract group operator search can be implemented by a two-layer feedforward circuit utilizing circuit motifs of connection phase shift, nonlinear activation function, and pooling, similar to Drosophila's goal-directed navigation neural circuits. And the computational complexity of the feedforward circuit can be even lower than common signal processing algorithms in certain conditions. We also provide geometric interpretations of circuit computation in the group representation space. The feedforward motion planning circuit is further combined with sensory and motor circuit modules into a full circuit of the sensory-action loop implementing goal-directed navigation. Our work for the first time links the abstract operator search with biological neural circuits. © 2024 Neural information processing systems foundation. All rights reserved.

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Combination Supplements for Endometrial Cancer

Combination Supplements for Endometrial Cancer

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ieee Symposium on bioinformatics and Bioengineering (BIBE)

作者： Madhurima Mondal Aditya Lahiri Haswanth Vundavilli Giuseppe Del Priore Peter Reeves Aniruddha Datta Department of Electrical and Computer Engineering Texas A&M University College Station TX USA Department of Biomedical and Health Informatics The Children's Hospital of Philadelphia Philadelphia PA USA Department of Bioinformatics and Computational Biology The University of Texas MD Anderson Cancer Center Houston TX USA Department of Obstetrics and Gynecology Morehouse School of Medicine Atlanta GA USA Sumaq Life LLC Michigan USA

This paper suggests an optimal supplement combination (EGCG, Curcumin, Melatonin, Aspirin, and Baicalein) for treating endometrial cancer using a Boolean model. Endometrial cancer affects the uterine lining, with a high incidence in 2023. Surgery, chemotherapy, or radiation may not be options for some patients, leading to increased interest in dietary supplements. However, using ineffective supplements can cause health problems. Our study focuses on safer, less toxic supplements, offering the most efficient combination.

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ROSE: Robust Selective Fine-tuning for Pre-trained Language Models

ROSE: Robust Selective Fine-tuning for Pre-trained Language ...

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2022 conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Jiang, Lan Zhou, Hao Lin, Yankai Li, Peng Zhou, Jie Jiang, Rui MOE Key Laboratory of Bioinformatics Center for Synthetic and Systems Biology Department of Automation BNRist Tsinghua University China Pattern Recognition Center WeChat AI Tencent Inc. China Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Tsinghua University China

Even though the large-scale language models have achieved excellent performances, they suffer from various adversarial attacks. A large body of defense methods has been proposed. However, they are still limited due to redundant attack search spaces and the inability to defend against various types of attacks. In this work, we present a novel fine-tuning approach called RObust SEletive fine-tuning (ROSE) to address this issue. ROSE conducts selective updates when adapting pre-trained models to downstream tasks, filtering out invaluable and unrobust updates of parameters. Specifically, we propose two strategies: the first-order and second-order ROSE for selecting target robust parameters. The experimental results show that ROSE achieves significant improvements in adversarial robustness on various downstream NLP tasks, and the ensemble method even surpasses both variants above. Furthermore, ROSE can be easily incorporated into existing fine-tuning methods to improve their adversarial robustness further. The empirical analysis confirms that ROSE eliminates unrobust spurious updates during fine-tuning, leading to solutions corresponding to flatter and wider optima than the conventional method. Code is available at https://***/jiangllan/ROSE. © 2022 Association for computational Linguistics.

关键词： computational linguistics

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AMPpred-DLFF:prediction of AMPs based on deep learning and multi-view features fusion

AMPpred-DLFF:prediction of AMPs based on deep learning and m...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Yu Chen Xingpeng Jiang Weizhong Zhao Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Central China Normal University WuHan China School of Computer Science Central China Normal University WuHan China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Antimicrobial peptides (AMPs) hold significant promise in antibacterial and anticancer research, offering a crucial solution to the escalating issue of antibiotic resistance and paving the way for developing novel strategy for cancer treatments. Previously, experimental methods were used to identify AMPs. However, there is now a shift towards developing computational approaches to predict AMPs accurately, greatly reducing the time and effort required for experimental identification. Past computation methods for predicting AMPs have either focused on feature encoding and extraction or emphasized neural network design. Unfortunately, relying on a single method or feature can lead to the oversight of important information. In this study, we present AMPpred-DLFF, a novel computational method for identifying AMPs. This method initially employs ESM-2 to generate residue embedding representations, captures the spatial relationships between residues, and constructs a graph to integrate peptide data. Subsequently, a graph attention network extracts features from the graph data. Concurrently, multiple encoding techniques are used to extract residue features from sequence data, which are then processed by a convolutional neural network. Finally, the feature representations from both modules are combined, and the peptide sequence is predicted as an antimicrobial peptide through a fully connected layer. Performance assessments on several benchmark datasets demonstrate that AMPpred-DLFF outperforms current leading methods, underscoring the effectiveness of multi-view fusion in enhancing AMP prediction accuracy.

关键词： Hands Deep learning Peptides Neural networks Predictive models Feature extraction Antibacterial activity Encoding Data mining Immune system

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FEATURE-pHLA: Physico-chemical features efficiently predict peptide-HLA binding affinity

FEATURE-pHLA: Physico-chemical features efficiently predict ...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Hamda Alhosani Raghvendra Mall Ankita Singh Filippo Castiglione Biotechnology Research Center Technology Innovation Institute Abu Dhabi United Arab Emirates Institute for Applied Computing National Research Council of Italy Rome Italy

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Human leukocyte antigen or HLA plays a crucial role in the recognition of antigenic peptides as this binding is responsible for subsequent immune response by eliciting T-cell activation. Accurate prediction of peptide-HLA binding affinity is imperative for facilitating vaccine development and immunotherapies. Recent advancements in transformer-based models and protein language models in predicting peptide-HLA interactions have shown significant improvements. Current methodologies rely on deep learning methods and GPU-intensive computations. We propose a simple and computationally cheaper method that demonstrates efficacy. Our tree-based model, named FEATUREPHLA, utilizes the physico-chemical fingerprints obtained from peptides and HLA sequences and is highly *** goal was to estimate the predictive efficacy of these physico-chemical features for the task of peptide-HLA binding prediction. Our proposed method outperforms other methods on experimentally verified peptide-HLA binders from the HPV vaccine data securing the highest number of true positives and the lowest number of false negatives, thereby, showcasing its predictive power on real-world scenarios. Our study reveals the relevance of biology-inspired features for the calculation of molecular interactions and lays the groundwork towards developing more accurate biology-informed predictive models.

关键词： Proteins Protein engineering Accuracy computational modeling Peptides Lymphocytes Predictive models Transformers biology Vaccines

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BECNN: Bias Field Estimation CNN Trained with a Dual Route Implicit Supervised Learning Strategy

BECNN: Bias Field Estimation CNN Trained with a Dual Route I...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Shuaizheng Chen Chaolu Feng Wei Li Jinzhu Yang Dazhe Zhao School of Computer Science And Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Bias fields adversely affect various automatic analysis technologies. Therefore, bias field correction is essential. However, deep learning based methods encounter challenges in obtaining ground truth. Although existing methods attempt to address this problem by using training-free techniques or constructing datasets with approximations of the ground truth, the lack of task-oriented guidance, training instability, and inappropriate use of approximations still impact performance. Additionally, different approaches for bias field correction, i.e., estimating bias fields versus directly restoring clean images, exhibit different performances. However, there is no consensus on the best way, resulting in limited performance in some cases. To address these problems, we propose a bias field generation method to construct the dataset and provide task-oriented information. We then propose the concept of Equivalent of Residual Mapping (ERM) to analyze the advantages of estimating bias fields. According to ERM, we propose the Bias field Estimation Convolutional Neural Network (BECNN). Finally, we propose the Dual Route Implicit Supervised Learning (DRISL) strategy to balance the guidance derived from approximations of the ground truth with over-dependence on them. The proposed method is compared qualitatively and quantitatively with the correlated methods. Experiment results demonstrate that the proposed method performs effectively both on bias field estimation and correction.

关键词： Training Measurement Deep learning Image resolution computational modeling Supervised learning Estimation Image restoration computational efficiency Convolutional neural networks

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Determinative Role of Non-structural Protein Genes in Genus Identification of Coronaviruses

Determinative Role of Non-structural Protein Genes in Genus ...

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2022 Ural-Siberian conference on computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2022

作者： Chaley, Maria Kutyrkin, Vladimir Institute of Mathematical Problems of Biology Ras Branch of Keldysh Institute of Applied Mathematics Ras Bioinformatics Laboratory Pushchino Russia Moscow State Technical University N.a. N.E. Bauman Department of Fundamental Sciences Moscow Russia

ISBN: (数字)9781665452885

ISBN: (纸本)9781665452885

Interrelationship of coronavirus genus with key fragments of viral genome was investigated. Genes of structural proteins (S-gene of spike protein and N-gene of nucleocapsid protein) and ORF1ab of polyprotein pp1ab, that in infected cell is split into 16 non-structural proteins, were considered as such fragments. Statistical method based on averaged codon distribution in the genes of genus prototype variants was applied in the work to recognize genus of coronavirus. High reliability of this method has been demonstrated earlier in recognizing the 15 species and serotypes of the flaviviruses, such as viruses of yellow fever, dengue fever, various encephalitides, etc. For each key fragment of the coronavirus genome the numerical experiments on identification of genus for the 3242 viral genomes from the GenBank have been done. The highest reliability (98%) was achieved, when ORF1ab frequency codon characteristics were used. It appeared to be that in recognizing genus of Gammacoronavirus, basing on spike protein gene, about half of the 345 genomes of this genus were identified as Betacoronavirus (84.6%) and Alphacoronavirus (15.4%). Analogous phenomenon of significant error appeared in determinating Alphacoronavirus genus, basing on nucleocapsid protein gene, also. However, these significant errors may be a consequence of the coronavirus genome plasticity in the result of homologous recombinations between the viral genomes. © 2022 ieee.

关键词： Coronavirus

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A Survey Paper on Computer Aided Detection of Tuberculosis 7

A Survey Paper on Computer Aided Detection of Tuberculosis

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7th International conference on Parallel, Distributed and Grid Computing, PDGC 2022

作者： Khan, Zuber Jain, Tanisha Ansari, Ariba Arya, Ravi Kumar Indraprastha Institute of Information Technology Delhi Department of Electronics and Communication Engineering Delhi India Indraprastha Institute of Information Technology Delhi Department of Computational Biology Delhi India Jawaharlal Nehru University School of Engineering Delhi India

ISBN: (纸本)9781665454018

Tuberculosis (TB) is a disease that claims millions of lives each year, primarily in impoverished places. Traditional TB screening procedures may not only take longer time, but may also be infeasible in areas with inadequate healthcare infrastructure. Artificial intelligence developments have propelled computer-aided diagnostic systems to new heights. The identification of TB using computer-aided approaches offers various benefits and has the potential to be superior to traditional diagnostic procedures, particularly in poor and middle-income countries where medical specialists and machinery are few. This survey article intends to describe the scientific effort done in computer assisted detection of TB and provides light on future research opportunities. © 2022 ieee.

关键词： Developing countries

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