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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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Patient-by-Patient Deep Transfer Learning for Drug-Response Profiling Using Confocal Fluorescence Microscopy of Pediatric Patient-Derived Tumor-Cell Spheroids

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2022年第12期41卷 3981-3999页

作者： Berker, Yannick ElHarouni, Dina Peterziel, Heike Fiesel, Petra Witt, Olaf Oehme, Ina Schlesner, Matthias Oppermann, Sina Hopp Children’s Cancer Center Heidelberg (KiTZ) Heidelberg Germany German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK) Clinical Cooperation Unit Pediatric Oncology Heidelberg Germany German Cancer Research Center (DKFZ) Bioinformatics and Omics Data Analytics Heidelberg Germany

Image-based phenotypic drug profiling is receiving increasing attention in drug discovery and precision medicine. Compared to classical end-point measurements quantifying drug response, image-based profiling enables both the quantification of drug response and characterization of disease entities and drug-induced cell-death phenotypes. Here, we aim to quantify imagebased drug responses in patient-derived 3D spheroid tumor cell cultures, tackling the challenges of a lack of single-cell-segmentation methods and limited patientderived material. Therefore, we investigate deep transfer learning with patient-by-patient fine-tuning for cell-viability quantification. We fine-tune a convolutional neural network (pre-trained on ImageNet) with 210 control images specific to a single training cell line and 54 additional screenspecific assay control images. This method of image-based drug profiling is validated on 6 cell lines with known drug sensitivities, and further tested with primary patientderived samples in a medium-throughput setting. Network outputs at different drug concentrations are used for drugsensitivity scoring, and dense-layer activations are used in t-distributed stochastic neighbor embeddings of drugs to visualize groups of drugs with similar cell-death phenotypes. Image-based cell-line experiments show strong correlation to metabolic results (R approximate to 0.7) and confirm expected hits, indicating the predictive power of deep learning to identify drug-hit candidates for individual patients. In patient-derivedsamples, combiningdrug sensitivity scoring with phenotypic analysis may provide opportunities for complementary combination treatments. Deep transfer learning with patient-by-patient fine-tuning is a promising, segmentation-free image-analysis approach for precision medicine and drug discovery.

关键词： Molecular and cellular imaging microscopy pattern recognition and classification neural network spheroid phenotypic drug profiling translation

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Single-cell transcriptomics and epigenomics point to CD58-CD2 interaction in controlling primary melanoma growth and immunity

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Cancer Communications 2025年第4期45卷 465-470页

作者： Antonia Stubenvoll Maria Schmidt Johanna Moeller Max Alexander Lingner Chango Carolyn Schultz Olga Antoniadou Henry Loeffler-Wirth Stephan Bernhart Florian Groβe Beatrice Thier Annette Paschen Ulf Anderegg Jan CSimon Mirjana Ziemer Clara TSchoeder Hans Binder Manfred Kunz Department of Dermatology Venereology and AllergologyUniversity of Leipzig Medical CenterLeipzigGermany Interdisciplinary Center for Bioinformatics University of LeipzigLeipzigGermany Institute for Drug Discovery University of Leipzig Medical CenterLeipzigGermany Bioinformatics Group Department of Computer ScienceUniversity of LeipzigLeipzigGermany Department of Diagnostics Fraunhofer Institute of Cell Therapy and Immunology(IZI)LeipzigGermany Center for Scalable Data Analytics and Artificial Intelligence(ScaDS.AI)Dresden/Leipzig LeipzigGermany Department of Dermatology University Hospital EssenUniversity Duisburg-Essen and German Cancer Consortium(DKTK)Partner Site Essen/DüsseldorfEssenGermany

Immunotherapy is currently one of the most promising treatment options for malignant melanoma[1].To uncover new immunological targets for future treatment approaches,single-cell transcriptomic and epigenomic analyses ... 详细信息

关键词： malignant melanoma single cell transcriptomics melanocytic nevus immunity immunotherapy primary melanoma epigenomic analyses epigenomics cd cd interaction

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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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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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TCRosetta:An Integrated Analysis and Annotation Platform for T-cell Receptor Sequences

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Genomics, Proteomics & bioinformatics 2024年第4期22卷 181-190页

作者： Tao Yue Si-Yi Chen Wen-Kang Shen Zhan-Ye Zhang Liming Cheng An-Yuan Guo Center for Artificial Intelligence Biology Hubei Bioinformatics&Molecular Imaging Key LaboratoryCollege of Life Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Department of Laboratory Medicine Tongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030China Department of Thoracic Surgery West China Biomedical Big Data CenterWest China HospitalSichuan UniversityChengdu 610041China

T cells and T-cell receptors(TCRs)are essential components of the adaptive immune *** of the TCR repertoire offers a promising and highly informative source for understanding the functions of T cells in the immune response and *** TCR repertoire studies have attracted much attention,there are few online servers available for TCR repertoire analysis,especially for TCR sequence annotation or advanced ***,we developed TCRosetta,a comprehensive online server that integrates analytical methods for TCR repertoire analysis and *** combines general feature analysis,large-scale sequence clustering,network construction,peptide–TCR binding prediction,generation probability calculation,and k-mer motif analysis for TCR sequences,making TCR data analysis as simple as *** TCRosetta server accepts multiple input data formats and can analyze�20,000 TCR sequences in less than 3 *** is the most comprehensive web server available for TCR repertoire analysis and is freely available at https://***/TCRosetta/.

关键词： T-cell receptor TCR repertoire analysis Batch search Annotation Web server.

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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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Pap Smear Medical Image Classification Using Deep Learning: A Systematic Review 6

Pap Smear Medical Image Classification Using Deep Learning: ...

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

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

ISBN: (纸本)9798350345162

Cervical cancer has been known as one of the most prevalent medical disorders globally and a leading cause of death. Early detection, particularly through Pap tests, plays a vital role in its prevention. Previous studies have leveraged machine learning and deep learning techniques to classify the medical images obtained from Pap tests. In this study, a Systematic Literature Review methodology was used to examine 15 relevant papers that have been filtered from queries to Google Scholar which have gone through 4 stages of filtering that include: identification, screening, eligibility, and inclusion. This study addresses two research questions regarding the datasets and deep learning techniques for classifying pap smear images in recent years. The performance of the models was analyzed and potential areas for improvements are suggested. The findings of this study reveal that the Herlev University Hospital and SIPaKMed datasets are the most used. The methodologies used by researchers range from machine learning techniques, transfer learning using Convolutional Neural Networks, and utilize state-of-the-art models with novel optimizing methodology. While there are exciting opportunities in the field, challenges include model generalization and interpretability. © 2023 IEEE.

关键词： cervical cancer deep learning machine learning pap test

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