Article
Biochemical Research Methods
Tobias G. Rehfeldt, Ralf Gabriels, Robbin Bouwmeester, Siegfried Gessulat, Benjamin A. Neely, Magnus Palmblad, Yasset Perez-Riverol, Tobias Schmidt, Juan Antonio Vizcaino, Eric W. Deutsch
Summary: Data set acquisition and curation are challenging in machine learning, particularly for proteomics-based LC-MS data sets due to data reduction. ProteomicsML is introduced as an online resource for accessing proteomics-based data sets and tutorials. It simplifies data access and provides tutorials for interacting with advanced algorithms. ProteomicsML enables comparison of machine learning algorithms and offers introductory material for newcomers in the field. The platform is freely available at https://www.proteomicsml.org/, and contributions are welcome at https://github.com/ProteomicsML/ProteomicsML.
JOURNAL OF PROTEOME RESEARCH
(2023)
Article
Mathematics
Jishu K. Medhi, Pusheng Ren, Mengsha Hu, Xuhui Chen
Summary: Deep learning is a promising technique for bioelectrical signal analysis, as it can automatically discover hidden features from raw data. However, training a deep neural network requires a vast amount of labeled samples, and the performance of the model may deteriorate sharply when applied to different study objects. To address these issues, we propose a deep multi-task learning approach that improves the accuracy of ECG data analysis.
Review
Biochemistry & Molecular Biology
Noam Auslander, Ayal B. Gussow, Eugene V. Koonin
Summary: The exponential growth of biomedical data in recent years has led to the application of various machine learning techniques in biology and clinical research. These methods enable automatic feature extraction, selection, and predictive model generation for efficient study of complex biological systems. Despite facing challenges, integrating machine learning techniques with established bioinformatics approaches presents unique opportunities to overcome these challenges.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2021)
Article
Biochemical Research Methods
Hua Shi, Shuang Li, Xi Su
Summary: N6-methyladenine (6mA), a type of DNA methylation, has gained significant attention in recent years. Accurate identification of 6mA sites is crucial for understanding its biological activities and mechanisms in plant genomes. Traditional wet-lab experiments are time-consuming and labor-intensive, leading to the emergence of computational methods, particularly machine learning. However, current methods heavily rely on prior knowledge and large model scale, resulting in limited generalization performance. To address these limitations, researchers propose Plant6mA, a lightweight structure predictor based on Transformer encoder, which demonstrates superior generalization performance in predicting 6mA locations in plant genomes.
Article
Biochemistry & Molecular Biology
Kaixuan Diao, Jing Chen, Tao Wu, Xuan Wang, Guangshuai Wang, Xiaoqin Sun, Xiangyu Zhao, Chenxu Wu, Jinyu Wang, Huizi Yao, Casimiro Gerarduzzi, Xue-Song Liu
Summary: Seq2Neo is a pipeline that predicts the immunogenicity of neoantigens by providing a solution for neoepitope feature prediction using raw sequencing data. It supports different types of genome DNA alterations and includes a CNN-based model that shows improved performance in immunogenicity prediction compared to currently available tools.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2022)
Article
Chemistry, Multidisciplinary
Xiaoqin Pan, Xuan Lin, Dongsheng Cao, Xiangxiang Zeng, Philip S. Yu, Lifang He, Ruth Nussinov, Feixiong Cheng
Summary: This review introduces guidelines on utilizing deep learning methodologies and tools for drug repurposing, which is of great importance in drug development. The article summarizes the commonly used bioinformatics and pharmacogenomics databases for drug repurposing and discusses the recently developed sequence-based and graph-based representation approaches as well as state-of-the-art deep learning-based methods. The applications of drug repurposing in fighting the COVID-19 pandemic are presented, along with an outline of future challenges.
WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE
(2022)
Article
Agriculture, Dairy & Animal Science
M. B. Rabaglino, C. Le Danvic, L. Schibler, K. Kupisiewicz, J. P. Perrier, C. M. O'Meara, D. A. Kenny, S. Fair, P. Lonergan
Summary: This study aimed to identify sperm proteins acting as biomarkers of fertility in dairy bulls. Through the analysis of proteome, 301 differentially abundant proteins and 34 biomarker proteins were determined. The predictive ability of the biomarkers was evaluated, achieving a prediction accuracy of 94.4%.
JOURNAL OF DAIRY SCIENCE
(2022)
Article
Biochemical Research Methods
Siqi Bao, Ke Li, Congcong Yan, Zicheng Zhang, Jia Qu, Meng Zhou
Summary: This study summarizes the recent advances and applications of deep learning-based methods in the analysis of scRNA-seq data and specific tools, and investigates the future perspectives and challenges of deep learning techniques in the appropriate analysis and interpretation of scRNA-seq data.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Biochemical Research Methods
Raul I. Perez Martell, Alison Ziesel, Hosna Jabbari, Ulrike Stege
Summary: This article presents a framework called SUPR REF, which streamlines the process of training, validating, testing, and comparing promoter recognition models. Using biologically relevant benchmark datasets, the authors showcase the framework and evaluate the performance of previous models on new benchmark datasets. The results indicate that there is still room for improvement in the reliability of deep learning methods for promoter recognition in eukaryotic genomic sequences.
BMC BIOINFORMATICS
(2022)
Article
Biochemical Research Methods
Yuhang Liu, Zixuan Wang, Hao Yuan, Guiquan Zhu, Yongqing Zhang
Summary: HEAP is an explainable deep learning framework for predicting enhancers and exploring enhancer grammar. The algorithm accurately predicts enhancer activity in different cell types and provides explanations for the prediction mechanisms, leading to a better understanding of enhancer activity.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Yan Zhu, Fuyi Li, Dongxu Xiang, Tatsuya Akutsu, Jiangning Song, Cangzhi Jia
Summary: A promoter is a region in the DNA sequence that defines where gene transcription begins, and identifying promoters is crucial for understanding gene transcriptional regulation. Computational techniques are effective tools for annotating promoters, and Depicter, a deep learning-based method, helps identify different types of promoter sequences. Extensive testing shows that Depicter outperforms other state-of-the-art methods in predictive performance.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Computer Science, Artificial Intelligence
Gabriela Czibula, Carmina Codre, Mihai Teletin
Summary: This paper introduces a new approach AnomalP for detecting abnormal protein conformational transitions using deep autoencoders to encode information about the structural similarity between proteins belonging to the same superfamily. The study emphasizes the potential of autoencoders to learn biologically relevant patterns, such as protein structural characteristics, and their usefulness in detecting abnormal conformations or proteins related to a superfamily. The aim of the research is to provide better insights into protein structural similarity, with the broader goal of predicting protein conformational transitions.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Biology
Daniel Griffith, Alex S. Holehouse
Summary: The rise of high-throughput experiments has led to the development of new computational approaches, with machine learning methods, particularly deep learning, being increasingly utilized. PARROT is a general framework for training and applying deep learning-based predictors on large protein datasets, demonstrating ease of use and applicability for a wide range of biological problems.
Article
Neurosciences
Yanbu Wang, Linqing Liu, Chao Wang
Summary: This literature review focuses on the latest deep learning solutions for medical and healthcare prediction systems, with a specific emphasis on applications in the medical domain. The study categorizes the cutting-edge deep learning approaches and explores their underlying principles, merits, limitations, methodologies, simulation environments, and datasets. The review highlights the forefront advancements in deep learning techniques and their practical applications in medical prediction systems, while addressing the challenges hindering widespread implementation in medical image segmentation. The evaluation metrics employed encompass a broad spectrum of features.
FRONTIERS IN NEUROSCIENCE
(2023)
Article
Biology
Weiye Qian, Zhiyuan Yang
Summary: The emergence of single-cell RNA sequencing technology allows for simultaneous measurement of DNA, RNA, and protein in a single cell. The CITE-seq method enables the capture of RNA and surface protein expression simultaneously. In this study, CITE-seq datasets were analyzed to identify differentially expressed genes in seven cell types during bone marrow stem cell differentiation, and the relation between RNA and protein levels was predicted with a high score using a deep neural network model. Three cell-type-specific genes were identified in erythrocyte progenitor. This study provides valuable insights into stem cell differentiation in the bone marrow.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Chemistry, Analytical
Ching Tarn, Wen-Feng Zeng
Summary: This study adopts few-shot learning method to enhance the prediction accuracy of deep learning spectrum prediction, validated on multiple datasets, showing significant improvement in prediction accuracy within seconds.
ANALYTICAL CHEMISTRY
(2021)
Article
Medicine, Research & Experimental
Yuwan Zhao, Zhuo Li, Huancheng Tang, Shanhong Lin, Wenfeng Zeng, Dongcai Ye, Xin Zeng, Qiuming Luo, Jianwei Li, Zhixian Ao, Jierong Mo, Lixin Chen, Yiqiu Yang, Yunsheng Huang, Jianjun Liu
Summary: This study investigated the synthesis of a near-infrared light-sensitive NO prodrug and its effects on prostate cancer cells. The results showed that the drug effectively inhibited cell proliferation and promoted apoptosis in a concentration-dependent manner. Furthermore, in vivo experiments demonstrated the anti-cancer effects of the drug, with increased NO concentration in tumors after near-infrared light irradiation.
BIOMEDICINE & PHARMACOTHERAPY
(2021)
Article
Biochemical Research Methods
Zhen-Lin Chen, Peng-Zhi Mao, Wen-Feng Zeng, Hao Chi, Si-Min He
Summary: pDeepXL is a deep learning tool for predicting MS/MS spectra of cross-linked peptide pairs. Trained using transfer learning, it accurately predicts spectra of both noncleavable and cleavable cross-linked peptide pairs, and shows improved robustness through online fine-tuning. Integration of pDeepXL into a database search engine increases the identification of cross-link spectra by 18% on average.
JOURNAL OF PROTEOME RESEARCH
(2021)
Article
Computer Science, Interdisciplinary Applications
Wen-Feng Zeng, Qing-Xin Chu
Summary: An antenna decoupling method based on modal control is proposed in this paper, which excites a pair of decoupling modes simultaneously to achieve decoupling. The effectiveness of this method is validated through the analysis and design of a head-to-head antenna pair. Additionally, an eight-element MIMO antenna is designed, fabricated, and measured to demonstrate the good performance of the proposed method.
INTERNATIONAL JOURNAL OF RF AND MICROWAVE COMPUTER-AIDED ENGINEERING
(2022)
Article
Biochemistry & Molecular Biology
Isabell Bludau, Sander Willems, Wen-Feng Zeng, Maximilian T. Strauss, Fynn M. Hansen, Maria C. Tanzer, Ozge Karayel, Brenda A. Schulman, Matthias Mann
Summary: The recent revolution in computational protein structure prediction has provided new insights into the study of the entire proteome. In this study, the researchers analyze posttranslational modifications (PTMs) of proteins to determine their structural context and investigate their potential regulatory sites. The analysis reveals global patterns of PTM occurrence and spatial coregulation of different types of PTMs.
Article
Biochemical Research Methods
Bo Wen, Eric J. Jaehnig, Bing Zhang
Summary: OmicsEV is an R package that evaluates the quality of omics data tables by using various methods to assess depth, normalization, biological signal, and other factors. It generates comprehensive visual and quantitative evaluation results to help assess data quality and determine the optimal data processing method and parameters.
Article
Engineering, Electrical & Electronic
Yu-Zhong Liang, Fu-Chang Chen, Wen-Feng Zeng, Qing-Xin Chu
Summary: This communication investigates the method of mode cancellation for designing a two-port dielectric resonator antenna (DRA) for in-band full-duplex (IBFD) applications. The antenna structure is simple, consisting only of a single DRA element, a pair of feeding lines, and a pair of metallic probes. By utilizing different modes, the mutual coupling between the exciting port and the passive port can be suppressed to a very low level without the need for an extra decoupling structure. A prototype is fabricated and measured to verify the design, with the results demonstrating broad bandwidth and high isolation throughout the working band.
IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION
(2023)
Article
Multidisciplinary Sciences
Wen-Feng Zeng, Xie-Xuan Zhou, Sander Willems, Constantin Ammar, Maria Wahle, Isabell Bludau, Eugenia Voytik, Maximillian T. Strauss, Matthias Mann
Summary: Machine learning and deep learning are becoming increasingly important in MS-based proteomics. AlphaPeptDeep is a modular Python framework built on PyTorch that can learn and predict peptide properties. It features a model shop that allows non-specialists to create models easily. AlphaPeptDeep can also predict sequence-based properties and performs well in predicting retention time, collisional cross sections, and fragment intensities.
NATURE COMMUNICATIONS
(2022)
Article
Multidisciplinary Sciences
Siyuan Kong, Pengyun Gong, Wen-Feng Zeng, Biyun Jiang, Xinhang Hou, Yang Zhang, Huanhuan Zhao, Mingqi Liu, Guoquan Yan, Xinwen Zhou, Xihua Qiao, Mengxi Wu, Pengyuan Yang, Chao Liu, Weiqian Cao
Summary: pGlycoQuant is a generic tool for quantitative analysis of intact glycopeptides using both primary and tandem mass spectrometry. It employs a deep learning model and a Match In Run algorithm to improve glycopeptide matching and expand the quantitative function of various search engines. Its application in N-glycoproteomic study demonstrates its potential in exploring site-specific glycosylation and its role in biological processes.
NATURE COMMUNICATIONS
(2022)
Article
Cardiac & Cardiovascular Systems
Nan Cai, Cunren Li, Xianfang Gu, Wenfeng Zeng, Jiawei Zhong, Jingfeng Liu, Guopeng Zeng, Junxing Zhu, Haifeng Hong
Summary: The study found that there is a relationship between CYP2C19 gene polymorphisms and hypertension in the Hakka population. Loss-of-function genotypes of CYP2C19 increase the risk of hypertension.
BMC CARDIOVASCULAR DISORDERS
(2023)
Article
Medicine, Research & Experimental
Lisa Schweizer, Tina Schaller, Maximilian Zwiebel, Oezge Karayel, Johannes Bruno Mueller-Reif, Wen-Feng Zeng, Sebastian Dintner, Thierry M. Nordmann, Klaus Hirschbuehl, Bruno Maerkl, Rainer Claus, Matthias Mann
Summary: SARS-CoV-2 can cause damage to lung tissue and other organs in the human body, and this study aimed to analyze these effects comprehensively. Using a mass spectrometry proteomics workflow, the researchers identified inflammatory responses as the initial reaction in all tissues. They also found specific patterns of damage in different organs, such as diffuse alveolar damage in the lungs and organ-specific changes in the kidneys, liver, and lymphatic and vascular systems. In the brain, secondary inflammatory effects were linked to neurotransmitter receptors and myelin degradation. These findings contribute to our understanding of the mechanisms of COVID-19 and provide insights for organ-specific therapeutic interventions.
EMBO MOLECULAR MEDICINE
(2023)
Article
Biochemistry & Molecular Biology
Marvin Thielert, Ericka C. M. Itang, Constantin Ammar, Florian A. Rosenberger, Isabell Bludau, Lisa Schweizer, Thierry M. Nordmann, Patricia Skowronek, Maria Wahle, Wen-Feng Zeng, Xie-Xuan Zhou, Andreas-David Brunner, Sabrina Richter, Mitchell P. Levesque, Fabian J. Theis, Martin Steger, Matthias Mann
Summary: Single-cell proteomics allows unbiased characterization of biological function and heterogeneity at the protein level. However, current limitations include proteomic depth, throughput, and robustness. In this study, we introduce a streamlined multiplexed workflow using mDIA to address these limitations. Our approach enables automated and complete dimethyl labeling of bulk or single-cell samples, without compromising proteomic depth. We also demonstrate the ability to quantify twice as many proteins per single cell compared to previous methods, and our workflow allows routine analysis of 80 single cells per day. Additionally, we combine mDIA with spatial proteomics to increase the throughput for microdissection and MS analysis, and successfully identify proteomic signatures of cells within distinct tumor microenvironments in primary cutaneous melanoma.
MOLECULAR SYSTEMS BIOLOGY
(2023)
Article
Multidisciplinary Sciences
Wen Bu, Chad J. Creighton, Kelsey S. Heavener, Carolina Gutierrez, Yongchao Dou, Amy T. Ku, Yiqun Zhang, Weiyu Jiang, Jazmin Urrutia, Wen Jiang, Fei Yue, Luyu Jia, Ahmed Atef Ibrahim, Bing Zhang, Shixia Huang, Yi Li
Summary: Technological modifications to the CRISPR-Cas9 vector system allow for precise gene editing in mice, generating tumor models with high flexibility and efficiency. This advancement bridges the gap between CRISPR technology and accurate mouse models, providing more consistent models for studying human tumor evolution and drug testing.
Article
Radiology, Nuclear Medicine & Medical Imaging
Emel Alkim, Heidi Dowst, Julie DiCarlo, Lacey E. Dobrolecki, Anadulce Hernandez-Herrera, David A. Hormuth II, Yuxing Liao, Apollo McOwiti, Robia Pautler, Mothaffar Rimawi, Ashley Roark, Ramakrishnan Rajaram Srinivasan, Jack Virostko, Bing Zhang, Fei Zheng, Daniel L. Rubin, Thomas E. Yankeelov, Michael T. Lewis
Summary: Co-clinical trials involve evaluating therapeutics in both patients and patient-derived xenografts (PDX) to determine how well PDX responses match patient responses, in order to inform pre-clinical and clinical trials. The challenge lies in managing and analyzing the vast amount of data generated across different scales and species. To overcome this challenge, a web-based tool called MIRACCL is being developed to correlate MRI-based changes in tumor characteristics with mRNA expression data in a co-clinical trial setting.
Article
Engineering, Electrical & Electronic
Qingxin Chu, Wenfeng Zeng
Summary: This paper focuses on the antenna coupling within the MIMO system in 5G and summarizes decoupling techniques. It also elaborates on the new decoupling research developments based on the theory of characteristic mode and provides design examples to validate the proposed decoupling method.
CHINESE JOURNAL OF ELECTRONICS
(2022)
