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PCMD:A multilevel comparison database of intraand cross-species metabolic profiling in 530 plant species

Plant Communications 2024年第10期5卷 33-44页

作者： Yue Hu Yao Ruan Xin-Le Zhao Feng Jiang Dongxu Liu Qiang Zhu Qing-Ye Zhang Qing-Yong Yang National Key Laboratory of Crop Genetic Improvement Hubei Hongshan LaboratoryHuazhong Agricultural UniversityWuhan 430070HubeiChina Hubei Key Laboratory of Agricultural Bioinformatics and Hubei Engineering Technology Research Center of Agricultural Big Data College of InformaticsHuazhong Agricultural UniversityWuhan 430070China Yazhouwan National Laboratory Sanya 572024China

Comparative metabolomics plays a crucial role in investigating gene function,exploring metabolite evolution,and accelerating crop genetic ***,a systematic platform for intra-and crossspecies comparison of metabolites is currently ***,we report the Plant Comparative Metabolome database(PCMD;http://***/PCMD),a multilevel comparison database based on predicted metabolic profiles of 530 plant *** PCMD serves as a platform for comparing metabolite characteristics at various levels,including species,metabolites,pathways,and biological *** database also provides a number of user-friendly online tools,such as species comparison,metabolite enrichment,and ID conversion,enabling users to perform comparisons and enrichment analyses of metabolites across different *** addition,the PCMD establishes a unified system based on existing metabolite-related databases by standardizing metabolite *** PCMD is the most speciesrich comparative plant metabolomics database currently available,and a case study demonstrates its ability to provide new insights into plant metabolic diversity.

关键词： comparative metabolomics plant database multilevel comparison metabolite characteristics

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Mapping Multi-factor-mediated Chromatin Interactions to Assess Dysregulation of Lung Cancer-related Genes

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Genomics, Proteomics & bioinformatics 2023年第3期21卷 573-588页

作者： Yan Zhang Jingwen Zhang Wei Zhang Mohan Wang Shuangqi Wang Yao Xu Lun Zhao Xingwang Li Guoliang Li National Key Laboratory of Crop Genetic Improvement Huazhong Agricultural UniversityWuhan 430070China Hubei Key Laboratory of Agricultural Bioinformatics and Hubei Engineering Technology Research Center of Agricultural Big Data 3D Genomics Research CenterHuazhong Agricultural UniversityWuhan 430070China

Studies on the lung cancer genome are indispensable for developing a cure for lung ***-genome resequencing,genome-wide association studies,and transcriptome sequencing have greatly improved our understanding of the cancer ***,dysregulation of longrange chromatin interactions in lung cancer remains poorly *** better understand the three-dimensional(3D)genomic interaction features of the lung cancer genome,we used the A549 cell line as a model system and generated high-resolution chromatin interactions associated with RNA polymerase II(RNAPII),CCCTC-binding factor(CTCF),enhancer of zeste homolog 2(EZH2),and histone 3 lysine 27 trimethylation(H3K27me3)using long-read chromatin interaction analysis by paired-end tag sequencing(ChIA-PET).Analysis showed that EZH2/H3K27me3-mediated interactions further repressed target genes,either through loops or domains,and their distributions along the genome were distinct from and complementary to those associated with ***-related genes were highly enriched with chromatin interactions,and chromatin interactions specific to the A549 cell line were associated with oncogenes and tumor suppressor genes,such as additional repressive interactions on FOXO4 and promoter–promoter interactions between NF1 and *** of an anchor associated with chromatin interactions reversed the dysregulation of cancer-related genes,suggesting that chromatin interactions are essential for proper expression of lung cancer-related *** findings demonstrate the 3D landscape and gene regulatory relationships of the lung cancer genome.

关键词： Lung cancer 3D genome ChIA-PET Chromatin interaction Dysregulation

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M6A peak calling accounting for varying sequencing bias across regions and samples

M6A peak calling accounting for varying sequencing bias acro...

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2024 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Lanyu Guo, Zhenxing Qin, Zhaohui Wu, Zhijin Brown University Dept of Biostatistics ProvidenceRI02903 United States The Chinese University of Hong Kong School of Data Science Shenzhen China Emory University Dept of Biostatistics and Bioinformatics AtlantaGA30329 United States

ISBN: (纸本)9798350386226

N6-Methyladenosine (m6A) is the most abundant type of mRNA methylation and is most widely measured by methylated RNA immunoprecipitation sequencing (MeRIP-seq). In MeRIP-seq, an immunoprecipitation (IP) sample and a pairing control (input) sample are sequenced for each biological sample. Methylated regions are identified as peaks showing increased counts in the IP sample versus the input. We report that technical bias in sequencing can vary substantially in the IP and input samples depending on the local sequence context. Current sequencing depth-based normalization does not appropriately account for the varying technical bias along the transcriptome and leads to inaccurate identification of m6A regions. We describe a method to estimate a local size factor that reflects the RNA sequence context and show that peak calling using these region-specific size factors identifies more accurate peak regions. © 2024 IEEE.

关键词： Methylation

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UniSleepPos: Sleep Posture Identification System Utilizing Millimeter-wave Radar

UniSleepPos: Sleep Posture Identification System Utilizing M...

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2024 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Kun Li, Min He, Chu Tian, Xu Mao, Junbin Zeng, Min Liu, Jin Xinjiang University Xinjiang Engineering Research Center of Big Data and Intelligent Software School of Software Urumqi China Central South University Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Changsha China

ISBN: (纸本)9798350386226

Sleep posture identification is crucial for accurately assessing sleep quality and diagnosing related diseases. In the realm of non-intrusive sleep monitoring, non-contact technologies are becoming increasingly mainstream. Millimeter-wave radar is frequently utilized in sleep posture identification due to its high resolution, strong penetration, and excellent sensitivity. However, traditional radar-based methods for sleep posture identification often struggle with reliability when dealing with diverse individuals and complex sleep environments. To address these challenges, we propose UniSleepPos, which designs a novel dual-view fusion mechanism to integrate depression and elevation angle signals obtained from radar, thus accurately capturing the posture information of the monitored subject in three-dimensional space. Furthermore, we combine sleep posture identification with individual characteristics, utilizing existing individual labels as prior knowledge to assist in sleep posture identification. The integration of prior knowledge provides a valuable information source for the model, helping to enhance its understanding of the data and improve its performance. We collected sleep posture data from eight volunteers using millimeter-wave radar devices under various environmental conditions. Leave-one-subject-out experiments were conducted to validate the effectiveness of UniSleepPos. The results indicated that UniSleepPos significantly outperforms existing methods, demonstrating its potential for practical applications. © 2024 IEEE.

关键词： Sleep research

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Punctuated ancestral gene gains in streptophyte evolution

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Molecular Plant 2022年第5期15卷 799-801页

作者： Iker Irisarri Jan de Vries University of Goettingen Institute for Microbiology and GeneticsDepartment of Applied BioinformaticsGoldschmidtstr.137077 GöttingenGermany Campus Institute Data Science(CIDAS) GöttingenGermany University of Goettingen Goettingen Center for Molecular Biosciences(GZMB)Department of Applied BioinformaticsGoldschmidtstr.137077

Understanding the genomic substrate behind evolutionary change is a major endeavor in biology,shedding light on impor-tant aspects of the evolutionary history as well as unraveling the mechanisms of genome *** of the ... 详细信息

关键词： evolution evolution evolutionary

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On the Measurement of the Inductive Bias Impact in Convolutional Neural Networks Training for Single Image Super-Resolution 8

On the Measurement of the Inductive Bias Impact in Convoluti...

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8th International Conference on Computer Science and Computational Intelligence, ICCSCI 2023

作者： Kwantawijaya, Aldrian Najoan, Daniel Stephanus Tamimmanar Cenggoro, Tjeng Wawan Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

The performance of SISR models is currently growing at a rapid pace since the adoption of Convolutional Neural Networks (CNN) in the domain. Despite that, the understanding of the underlying factors of this rapid growth is still low. In this study, we proposed a study design to measure the impact of the inductive bias in CNN on its training performance, which is one of the factors of the excellent performance of CNN in SISR. With this study design, we observed that not all CNN has a supportive inductive bias to the training performance. This result can provide guidance to future research in choosing a suitable CNN architecture for optimal SISR performance. © 2023 The Authors. Published by Elsevier B.V.

关键词： Convolutional Neural Networks Deep Learning Inductive Bias Single-Image Super Resolution

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A Systematic Literature Review of CNN Approaches in Classifying Brain Tumor 6

A Systematic Literature Review of CNN Approaches in Classify...

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6th International Conference on Computer and Informatics Engineering, IC2IE 2023

作者： Paulindino, Aedentrisa Yasmanda Pardamean, Bens Elwirehardja, Gregorius Natanael Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798350345162

The ability of Convolutional Neural Networks (CNNs) to accurately discriminate between normal and tumorous brain tissues has been promising. The review focuses on the different CNN models, pre-processing methods, data augmentation, and Transfer Learning (TL) strategies used in this research. This Systematic Literature Review (SLR) collected the data from Google Scholar. The results of this study indicate that open-source datasets from Kaggle and Brain MRI Images for Brain Tumor Detection are the most used datasets. However, limited data and imbalanced class problems remain common challenges across various datasets. To overcome those challenges, using a larger dataset, oversampling, Generative Adversarial Network (GAN), federated learning, and Self-Supervised Learning (SSL) to handle the imbalance are the potential solution. Additionally, popular CNN architectures for brain tumor classification extensively use pre-trained models such as VGG16, VGG19, DenseNet121, DenseNet201, GoogleNet, ResNet-50, and Inception-v3. TL strategies are preferred, allowing CNNs to leverage knowledge from large datasets, improving generalization even with limited labeled data. © 2023 IEEE.

关键词： brain tumor classification cnn mri pre-trained transfer learning

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Systematic Literature Review of Transformer Model Implementations in Detecting Depression 6

Systematic Literature Review of Transformer Model Implementa...

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6th International Conference on Computer and Informatics Engineering, IC2IE 2023

作者： Nanggala, Kenjovan Pardamean, Bens Elwirehardja, Gregorius Natanael Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798350345162

This systematic literature review explores the application of transformer models in early detection of human depression, encompassing text, audio, and video data modalities. Transformer architectures, notably BERT for text, have proven adept at capturing crucial contextual and linguistic patterns associated with depression. For audio and video data, hybrid approaches that combine transformer models with other architectures are prevalent. Key features considered include eye gaze, head pose, facial muscle movements, and audio characteristics such as MFCC and Log-mel Spectrogram, along with text embeddings. Performance comparisons underscore the superiority of text-based data in consistently delivering the most promising results, followed by audio and video modalities when utilizing transformer models. The fusion of multiple modalities emerges as an effective strategy for enhancing predictive accuracy, with the amalgamation of audio, video, and text data yielding the most precise outcomes. However, it is noteworthy that unimodal approaches also exhibit potential, with text data exhibiting superior performance over audio and video data. Nevertheless, several challenges persist in this research domain, including imbalanced datasets, the limited availability of comprehensive and diverse samples, and the inherent complexities in interpreting visual cues. Addressing these challenges remains imperative for the continued advancement of depression detection using transformer-based models across various modalities. © 2023 IEEE.

关键词： deep learning major depressive disorder multimodal transformer unimodal

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Transformers for molecular property prediction: Lessons learned from the past five years

arXiv

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arXiv 2024年

作者： Sultan, Afnan Sieg, Jochen Mathea, Miriam Volkamer, Andrea Data Driven Drug Design Center for Bioinformatics Saarland University Germany BASF SE Ludwigshafen Germany

Molecular Property Prediction (MPP) is vital for drug discovery, crop protection, and environmental science. Over the last decades, diverse computational techniques have been developed, from using simple physical and chemical properties and molecular fingerprints in statistical models and classical machine learning to advanced deep learning approaches. In this review, we aim to distill insights from current research on employing transformer models for MPP. We analyze the currently available models and explore key questions that arise when training and fine-tuning a transformer model for MPP. These questions encompass the choice and scale of the pre-training data, optimal architecture selections, and promising pre-training objectives. Our analysis highlights areas not yet covered in current research, inviting further exploration to enhance the field’s understanding. Additionally, we address the challenges in comparing different models, emphasizing the need for standardized data splitting and robust statistical analysis. © 2024, CC BY.

关键词： Deep learning

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A Systematic Literature Review of Deep Learning Application in Multiclass Anomaly Detection for Chest Medical Imaging 6

A Systematic Literature Review of Deep Learning Application ...

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6th International Conference on Computer and Informatics Engineering, IC2IE 2023

作者： Philips Pardamean, Bens Elwirehardja, Gregorius Natanael Isnan, Mahmud Bina Nusantara University Binus Graduate Program - Master of Compuer Science Computer Science Department Jakarta Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta Indonesia

ISBN: (纸本)9798350345162

Medical imaging abnormality detection is challenging, but deep learning approaches have shown promise. This paper reviews the current state of the art in deep learning approaches for detecting abnormalities in chest medical imaging. To discover the trends, opportunities, and challenges associated with this field, 18 studies were selected from Google Scholar based on their titles, abstracts, and contents for extensive review to answer two research questions. The study found that the National Institutes of Health (NIH) Chest X-ray 14 dataset is the most used dataset for this task. Most research uses a single-modal approach, considering only image data as input, with X-ray being the more popular instrument. There are 8 out of 18 studies leverage the transfer learning approach, with ResN et50 being the most popular network. MobileNetV2 has demonstrated competitive results compared to more robust networks. Preprocessing techniques such as image enhancement and data augmentation are leveraged by 61.1 % of the reviewed studies and are shown to improve model performance. © 2023 IEEE.

关键词： abnormality detection attention mechanisms chest x-ray CT images deep learning transfer learning

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