Article
Plant Sciences
Jonathan Wei Xiong Ng, Swee Kwang Chua, Marek Mutwil
Summary: Understanding how different cellular components work together to form a living cell is possible through multidisciplinary approaches combining molecular and computational biology. Machine learning has great potential in life sciences as it can discover novel relationships between biological features. Researchers created a dataset of gene features and developed a machine learning workflow to identify linked features. The detected linked features are visualized as a Feature Important Network (FIN), which provides insights into gene function. To enhance accessibility, the FINder database is made available to the scientific community.
FRONTIERS IN PLANT SCIENCE
(2022)
Article
Multidisciplinary Sciences
Hiroaki Ohishi, Seiru Shimada, Satoshi Uchino, Jieru Li, Yuko Sato, Manabu Shintani, Hitoshi Owada, Yasuyuki Ohkawa, Alexandros Pertsinidis, Takashi Yamamoto, Hiroshi Kimura, Hiroshi Ochiai
Summary: The authors developed a new tool called STREAMING-tag that can reveal the relationship between transcriptional activity and protein clusters, which is useful for quantitatively understanding transcriptional regulation in living cells.
NATURE COMMUNICATIONS
(2022)
Article
Multidisciplinary Sciences
Micah G. Donovan, Matthew D. Galbraith, Joaquin M. Espinosa
Summary: This study investigated the functional specialization of transcriptional cyclin dependent kinases (tCDKs) through analysis of high-content genetic dependency, gene expression, patient survival, and drug response datasets. The results showed that CDK7 and CDK9 are the most relevant targets, with distinct mechanisms of oncogenicity and context-dependent contributions to cancer survival and drug sensitivity.
SCIENTIFIC REPORTS
(2022)
Article
Biochemistry & Molecular Biology
Sabine Pinter, Franziska Knodel, Michel Choudalakis, Philipp Schnee, Carolin Kroll, Marina Fuchs, Alexander Broehm, Sara Weirich, Mareike Roth, Stephan A. Eisler, Johannes Zuber, Albert Jeltsch, Philipp Rathert
Summary: In this study, we identified DEAD-box helicase 19A (DDX19A) as a novel coregulator of lysine specific demethylase 1 (LSD1), which regulates gene expression by controlling the trimethylation of lysine 27 on histone 3. This discovery sheds light on a novel transcriptional regulatory pathway involving LSD1, DDX19A, and histone modifications.
NUCLEIC ACIDS RESEARCH
(2021)
Article
Biochemical Research Methods
Di Xiao, Hani Jieun Kim, Ignatius Pang, Pengyi Yang
Summary: This study developed a statistical framework to identify stable phosphorylated sites in human phosphoproteomics datasets, which are evolutionarily conserved, functionally important, and enriched in core signaling and gene pathways. Particularly, SPSs in the RNA splicing pathway are frequent targets of cancer mutations, suggesting a potential link between dysregulated RNA splicing and cancer development through mutations on SPSs.
Article
Biochemistry & Molecular Biology
Shixian Wang, Lina Zhang, Runtao Yang, Yujiao Zhao
Summary: This study presents a capsule network-based prediction technique for phosphorylation sites in prokaryotes. By incorporating a self-attention mechanism and improved algorithm, the prediction accuracy and stability are enhanced. Experimental results demonstrate close to 70% accuracy in predicting three phosphorylation sites in prokaryotes.
Article
Biochemistry & Molecular Biology
Pengshuai Yan, Weihua Li, Enxiang Zhou, Ye Xing, Bing Li, Jing Liu, Zhanhui Zhang, Dong Ding, Zhiyuan Fu, Huiling Xie, Jihua Tang
Summary: A single recessive mutation called fea5 was found to increase grain yield in maize. Candidate region was initially mapped and differentially expressed genes (DEGs) in phytohormone signal transduction were identified. Phytohormone profiling showed increased levels of auxin, jasmonic acid, ethylene, and cytokinin, while decreased level of gibberellin in fea5. Integrating mapping and transcriptome analysis identified Zm00001d048841 as the most likely candidate gene.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Article
Biochemistry & Molecular Biology
Narayanan Puthillathu Vasudevan, Dharmendra K. Soni, John R. Moffett, Jishnu K. S. Krishnan, Abhilash P. Appu, Sarani Ghoshal, Peethambaran Arun, John M. Denu, Thomas P. Flagg, Roopa Biswas, Aryan M. Namboodiri
Summary: The acetate activating enzyme Acss2 plays a role in matching cellular metabolism to current conditions and has regulatory functions in addition to its role in lipid synthesis. Using Acss2 knockout mice, this study investigated the roles of Acss2 in liver, brain, and adipose tissue and found dysregulation of numerous signaling pathways and cellular processes. The loss of Acss2 resulted in few changes in fatty acid constitution in these organ systems. Overall, this research demonstrates the organ-specific transcriptional regulatory patterns of Acss2 and its role as a regulatory enzyme.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Article
Cell Biology
Guangrong Qin, Theo A. Knijnenburg, David L. Gibbs, Russell Moser, Raymond J. Jr Jr Monnat, Christopher J. Kemp, Ilya Shmulevich
Summary: In this study, a functional module states framework is developed to systematically understand how different modules in cells respond to drug or genetic perturbations. The framework defines a transcriptional state space and reveals the association between transcriptional states and drug concentration and targets. It can also be used to predict transcriptional state-dependent drug sensitivity.
Article
Biochemical Research Methods
Chen Cao, Devin Kwok, Shannon Edie, Qing Li, Bowei Ding, Pathum Kossinna, Simone Campbell, Jingjing Wu, Matthew Greenberg, Quan Long
Summary: The power of genotype-phenotype association mapping studies greatly increases when contributions from multiple variants in a focal region are meaningfully aggregated. In this work, a novel method called kernel-based TWAS (kTWAS) is developed, combining the strengths of transcriptome-wide association studies (TWAS) and kernel methods.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Biochemistry & Molecular Biology
Jun-Xiao Ma, Yi Yang, Guang Li, Bin-Guang Ma
Summary: Symbiotic nitrogen fixation plays a crucial role in the nitrogen biogeochemical cycles and is the main nitrogen source for the biosphere. The molecular mechanisms of communication between rhizobia and host plants have been extensively studied, with a growing demand for integrated multiomics information. A computational framework was presented to study the protein-protein interaction network in the symbiosis system of B. diazoefficiens USDA110, revealing conserved functional modules and key protein hubs.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2021)
Editorial Material
Cell Biology
Christian Agatemor, Sasa Ama Dyese Middleton, Daniela Toledo
Summary: Cells utilize post-translational and post-transcription modifications as crucial mechanisms to maintain homeostasis and regulate gene transcription. Recent discoveries have shown that these modifications are more pervasive and important than previously thought.
TRENDS IN CELL BIOLOGY
(2022)
Article
Chemistry, Physical
Lingjun Huang, Dan Li, Ning Li, Guilian Liu
Summary: This study proposes a novel method for optimizing hydrogen networks, considering the allocation and cost of compressors. By analyzing different compression paths, the goal of minimizing the total cost is achieved.
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
(2022)
Article
Oncology
Sourat Darabi, Andrew Elliott, David R. Braxton, Jia Zeng, Kurt Hodges, Kelsey Poorman, Jeff Swensen, Basavaraja U. Shanthappa, James P. Hinton, Geoffrey T. Gibney, Justin Moser, Thuy Phung, Michael B. Atkins, Gino K. In, Wolfgang M. Korn, Burton L. Eisenberg, Michael J. Demeure
Summary: Malignant melanoma is a complex disease with a high mortality rate. Recent molecular studies have identified gene fusions in melanomas, which may serve as therapeutic targets. However, more research is needed to fully understand the implications and effectiveness of targeting these fusions.
Editorial Material
Multidisciplinary Sciences
Mark R. Woodford, Dimitra Bourboulia, Mehdi Mollapour
Summary: Molecular chaperones play a crucial role in establishing important protein-protein interaction networks, which are often enriched in certain diseases. A study published in Nature Communications used epichaperomics to identify unique changes occurring in chaperone-formed protein networks during mitosis in cancer cells.
NATURE COMMUNICATIONS
(2023)
Article
Biochemical Research Methods
Xiao Luo, Xinming Tu, Yang Ding, Ge Gao, Minghua Deng
Article
Genetics & Heredity
Weilai Chi, Minghua Deng
Article
Genetics & Heredity
Liang Chen, Yuyao Zhai, Qiuyan He, Weinan Wang, Minghua Deng
Article
Biochemical Research Methods
Liang Chen, Qiuyan He, Yuyao Zhai, Minghua Deng
Summary: Inspired by unsupervised domain adaptation, the study introduces a flexible single-cell semi-supervised clustering and annotation framework, scSemiCluster. By integrating reference and target data for training, the model utilizes structure similarity regularization and pairwise constraints to optimize clustering results. Without explicit domain alignment and batch effect correction, scSemiCluster outperforms other state-of-the-art algorithms, making it the first to utilize both deep discriminative clustering and deep generative clustering in the single-cell field.
Article
Biochemical Research Methods
Liang Chen, Shun He, Yuyao Zhai, Minghua Deng
JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY
(2020)
Article
Biochemical Research Methods
Hui Wan, Liang Chen, Minghua Deng
Summary: Motivation: The rapid development of single-cell RNA sequencing (scRNA-seq) enables the study of heterogeneity in individual cell characteristics. However, the noisy and high-dimensional nature of scRNA-seq data presents challenges in clustering analysis. This study proposes a novel scRNA-seq clustering algorithm called scNAME, which incorporates a mask estimation task for gene pertinence mining and a neighborhood contrastive learning framework for cell intrinsic structure exploitation. The combination of these techniques in scNAME is conducive to rare cell type detection. Experimental results confirm the accuracy, robustness, and scalability of the method. The biological analysis also validates the biological significance of scNAME. This research contributes to gene relationship exploration and global cellular similarity repository in the single-cell field.
Article
Biochemical Research Methods
Musu Yuan, Liang Chen, Minghua Deng
Summary: The research introduces a robust deep learning-based single-cell Multiple Reference Annotator that effectively transfers knowledge from multiple insufficient reference datasets to unlabeled target data, while also removing batch effects.
Article
Engineering, Electrical & Electronic
Xiao Luo, Zeyu Ma, Wei Cheng, Minghua Deng
Summary: This paper proposes an effective unsupervised hashing method called Hashing via Structural and Intrinsic siMilarity learning (HashSIM). It tackles the drawbacks of existing methods by utilizing structural similarity learning and intrinsic similarity learning. Experimental results demonstrate that HashSIM outperforms state-of-the-art baselines on multiple benchmark datasets.
IEEE SIGNAL PROCESSING LETTERS
(2022)
Article
Computer Science, Information Systems
Xiao Luo, Haixin Wang, Daqing Wu, Chong Chen, Minghua Deng, Jianqiang Huang, Xian-Sheng Hua
Summary: Nearest neighbor search is a basic task in fields like computer vision and data mining, and hashing is a widely used method for its efficiency. Deep hashing methods, with the development of deep learning, show more advantages than traditional methods. In this survey, deep supervised hashing and deep unsupervised hashing algorithms are investigated in detail. Additionally, important topics such as semi-supervised deep hashing, domain adaption deep hashing, and multi-modal deep hashing are introduced, along with commonly used datasets and performance evaluation schemes.
ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
(2023)
Article
Computer Science, Artificial Intelligence
Wei Ju, Xiao Luo, Zeyu Ma, Junwei Yang, Minghua Deng, Ming Zhang
Summary: This paper proposes a Graph Harmonic Neural Network (GHNN) that combines the advantages of graph convolutional networks and graph kernels to fully utilize unlabeled data, overcoming the scarcity of labeled data in semi-supervised scenarios.
Article
Genetics & Heredity
Hui Wan, Liang Chen, Minghua Deng
Summary: Current cell-type annotation tools for scRNA-seq data rely on well-annotated source data to identify cell types in target data. However, the need for raw source data may not always be fulfilled due to privacy concerns. These methods also struggle to detect novel cell types and often require subjective thresholds. The proposed scEMAIL framework addresses these limitations by automatically detecting novel cell types without accessing source data and utilizing a novel cell-type perception module.
GENOMICS PROTEOMICS & BIOINFORMATICS
(2022)
Article
Biochemical Research Methods
Yuyao Zhai, Liang Chen, Minghua Deng
Summary: The rapid development of single-cell RNA sequencing technology enables us to study gene expression heterogeneity at the cellular level. In this paper, a new and practical task called generalized cell type annotation and discovery is proposed for scRNA-seq data, aiming to label target cells with either known cell types or cluster labels instead of a unified 'unassigned' label.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biochemistry & Molecular Biology
Jiaxin Luo, Minghua Deng, Xuegong Zhang, Xiaoqiang Sun
Summary: Cell-cell communication is crucial for determining cell fates and functions in multicellular organisms. This study evaluated and compared the performances of different inference methods for cell-cell communication using various data sets. The results identified the best-performing methods for ligand-receptor inference and ligand/receptor-target regulation prediction, and provided a guideline and an ensemble pipeline for practical applications.
Article
Biochemical Research Methods
Musu Yuan, Liang Chen, Minghua Deng
Summary: This study developed a novel joint clustering framework called MoClust for analyzing single-cell multi-omics data. The framework improves data quality through automatic doublet detection and omics-specific autoencoders, and enhances clustering accuracy and separability through contrastive learning-based distribution alignment.
Article
Computer Science, Artificial Intelligence
Xiao Luo, Wei Ju, Meng Qu, Yiyang Gu, Chong Chen, Minghua Deng, Xian-Sheng Hua, Ming Zhang
Summary: This article introduces a self-supervised graph representation learning framework called CLEAR, which explores the structural semantics of a graph from global and local perspectives. It combines graph-level augmentation strategies and a graph neural network-based encoder to capture global semantics, while utilizing graph clustering techniques and a self-attention interaction module to aggregate the semantics of local subgraphs. The integration of both global and local semantics enhances the semantic-discriminative ability of graph representations. Experimental results on various benchmarks demonstrate the effectiveness of CLEAR in graph classification and transfer learning tasks.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)