Article
Biotechnology & Applied Microbiology
Feng Bao, Yue Deng, Sen Wan, Susan Q. Shen, Bo Wang, Qionghai Dai, Steven J. Altschuler, Lani F. Wu
Summary: This study introduces a method called multi-modal structured embedding (MUSE) to characterize cells and tissue regions by integrating morphological and transcriptional data. The study finds that MUSE can identify missed tissue subpopulations and compensate for modality-specific noise. In healthy and diseased tissues, MUSE reveals biologically meaningful tissue subpopulations and spatial patterns.
NATURE BIOTECHNOLOGY
(2022)
Review
Plant Sciences
Tatiana Ruiz-Bedoya, Kathryn J. McTavish, Tamar V. Av-Shalom, Darrell Desveaux, David S. Guttman
Summary: The field of plant pathology has provided crucial knowledge and genetic resources for improving plant health by revealing mechanisms underlying the arms race. The study of host-microbe interaction has also uncovered a vast evolutionary history of unexplored plant immunodiversity. This diversity in genetic and ecological factors can be harnessed for the rational engineering of durable resistance in sustainable agriculture.
CURRENT OPINION IN PLANT BIOLOGY
(2023)
Review
Biochemistry & Molecular Biology
Jinrui Xu, Henry E. Pratt, Jill E. Moore, Mark B. Gerstein, Zhiping Weng
Summary: This article discusses the efforts to build gene regulatory maps, including the identification of regulatory elements through functional assays and evolutionary analyses, and the challenges faced by the field in achieving higher resolution and comprehensiveness.
HUMAN MOLECULAR GENETICS
(2022)
Review
Microbiology
John P. DeLong, Maitham A. Al-Sammak, Zeina T. Al-Ameeli, David D. Dunigan, Kyle F. Edwards, Jeffry J. Fuhrmann, Jason P. Gleghorn, Hanqun Li, Kona Haramoto, Amelia O. Harrison, Marcia F. Marston, Ryan M. Moore, Shawn W. Polson, Barbra D. Ferrell, Miranda E. Salsbery, Christopher R. Schvarcz, Jasmine Shirazi, Grieg F. Steward, James L. Van Etten, K. Eric Wommack
Summary: This review discusses the traits of viral cells and viral particles, as well as how to categorize virus phenotypes; the foundational goal in biology is understanding how phenotypes emerge from genotypes; identifying the features that constitute a virus's phenotype is crucial for comprehensive interpretation of viral genome sequences and for advancing our understanding of viral evolution and ecology.
NATURE REVIEWS MICROBIOLOGY
(2022)
Article
Biochemistry & Molecular Biology
Yongming Chen, Yiwen Guo, Panfeng Guan, Yongfa Wang, Xiaobo Wang, Zihao Wang, Zhen Qin, Shengwei Ma, Mingming Xin, Zhaorong Hu, Yingyin Yao, Zhongfu Ni, Qixin Sun, Weilong Guo, Huiru Peng
Summary: In this study, a wheat integrative gene regulatory network (wGRN) was constructed by combining an updated genome annotation and diverse functional datasets. wGRN contains 7.2 million genome-wide interactions and is able to assign genes to specific biological pathways and prioritize candidate genes associated with complex phenotypic traits. The network was also used to enhance the interpretation of a transcriptome dataset and discover novel regulators contributing to phenotypic differences in wheat.
Article
Psychiatry
Jun-Yang Wang, Xiao-Yan Li, Hui-Juan Li, Jie-Wei Liu, Yong-Gang Yao, Ming Li, Xiao Xiao, Xiong-Jian Luo
Summary: Recent integrative analyses identified TMEM180 as a gene associated with schizophrenia risk through TWAS and SMR in East Asian populations. TMEM180 mRNA expression was significantly decreased in schizophrenia cases compared to controls, and its role in neurodevelopment and brain function was highlighted. Integration and sharing of resources in biomedical research are essential in the big data era.
SCHIZOPHRENIA BULLETIN
(2021)
Article
Clinical Neurology
Hong-Dong Li, Cory C. Funk, Karen McFarland, Eric B. Dammer, Mariet Allen, Minerva M. Carrasquillo, Yona Levites, Paramita Chakrabarty, Jeremy D. Burgess, Xue Wang, Dennis Dickson, Nicholas T. Seyfried, Duc M. Duong, James J. Lah, Steven G. Younkin, Allan Levey, Gilbert S. Omenn, Nilufer Ertekin-Taner, Todd E. Golde, Nathan D. Price
Summary: In this study, intron retention (IR) was found to play a role in Alzheimer's disease (AD) through genome-wide analysis of genetic, transcriptomic, and proteomic data. Thousands of IR events were identified, along with differentially expressed genes associated with AD and splicing-related genes that may regulate IR. The findings provide a new resource for exploring new AD biomarkers and pathological mechanisms.
ALZHEIMERS & DEMENTIA
(2021)
Article
Biochemical Research Methods
Yuzhou Chang, Carter Allen, Changlin Wan, Dongjun Chung, Chi Zhang, Zihai Li, Qin Ma
Summary: Single-cell RNA-Seq data is valuable for exploring cell heterogeneity and signature genes in various diseases. QUBIC2 is a powerful tool for identifying condition-specific functional gene modules, but its limited availability restricted its usage. IRISFGM, an R package developed by the authors, utilizes QUBIC2 to effectively analyze FGMs and cell clusters, providing essential insights for downstream analysis.
Article
Engineering, Industrial
Isabelle Y. S. Chan, Hao Chen
Summary: Due to land resource scarcity, sustainable urban development in high-density cities has long been challenging. This study aims to investigate the influences of various underground environment factors on users' health through a holistic approach. The results provide evidence for the importance of distinguishing between underground and aboveground developments in guidelines and standards, especially those related to space management.
ENGINEERING CONSTRUCTION AND ARCHITECTURAL MANAGEMENT
(2023)
Article
Neurosciences
Shaoqiang Han, Ruiping Zheng, Shuying Li, Bingqian Zhou, Yu Jiang, Caihong Wang, Yarui Wei, Jianyue Pang, Hengfen Li, Yong Zhang, Yuan Chen, Jingliang Cheng
Summary: The study revealed altered intrinsic timescale gradient in patients with depression compared to healthy controls, potentially related to monoamine receptor/transporter densities and illness duration. Genes related to timescale aberrance were enriched for synapse-related and neurotransmitter (receptor) terms, elaborating the underlying transcriptional basis of timescale aberrance. This provides insights into the link between neuroimaging, transcriptome, and neurotransmitter information, contributing to a comprehensive understanding of depression.
FRONTIERS IN NEUROSCIENCE
(2022)
Article
Multidisciplinary Sciences
Emile Faure, Sakina-Dorothee Ayata, Lucie Bittner
Summary: Marine microbes play a crucial role in ecosystem functions, with recent data on planktonic communities driving the need for innovative data-driven methods to quantify and predict their functions. A network-based analysis of 885 marine metagenome-assembled genomes revealed 233,756 protein functional clusters, of which 15% were unannotated. Machine learning identified biogeographical provinces as the best predictors of cluster abundance, including 1347 unannotated clusters. The study found the Mediterranean Sea to be an outlier in terms of protein functional clusters composition.
NATURE COMMUNICATIONS
(2021)
Review
Psychology, Multidisciplinary
Ingrid Schoon
Summary: There is a lack of consensus regarding the number and definition of core social-emotional competences, leading to challenges in future research and scientific utility. To address this, an integrative taxonomy called DOMASEC is introduced, which specifies core domains and manifestations of social-emotional competences across different frameworks and theories.
FRONTIERS IN PSYCHOLOGY
(2021)
Article
Computer Science, Artificial Intelligence
Jorge Martinez-Gil, Riad Mokadem, Josef Kung, Abdelkader Hameurlain
Summary: The significance of automatically identifying the semantic similarity between two small pieces of text has recently increased. The advancements in neural computation and its impact on various computer-related domains have created more opportunities for better solutions. This research proposes a neurofuzzy approach that combines neural networks and fuzzy logic to accurately determine semantic textual similarity.
DATA & KNOWLEDGE ENGINEERING
(2023)
Article
Biochemical Research Methods
Zhen Tian, Haichuan Fang, Yangdong Ye, Zhenfeng Zhu
Summary: In this article, a novel gene functional similarity calculation method is proposed, which focuses on the specificity of terms and edges. Experimental results show that the proposed method outperforms several baseline methods.
BMC BIOINFORMATICS
(2022)
Review
Biochemical Research Methods
Dionysios Fanidis, Panagiotis Moulos
Summary: There are no widely accepted golden standards for the normalization and statistical analysis of RNA-Seq data. The PANDORA algorithm outperforms other methods in detecting differentially expressed genes and long non-coding RNAs.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Biochemical Research Methods
Tao Wang, Yongzhuang Liu, Quanwei Yin, Jiaquan Geng, Jin Chen, Xipeng Yin, Yongtian Wang, Xuequn Shang, Chunwei Tian, Yadong Wang, Jiajie Peng
Summary: QTL analyses of multiomic molecular traits play a significant role in inferring the functional effects of genome variants. However, limited study sample size restricts QTL discovery and leads to missing molecular trait-variant associations. This study presents xQTLImp, a computational framework, to efficiently impute missing molecular QTL associations. Experimental results demonstrate high imputation accuracy and novel QTL discovery ability of xQTLImp.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Biochemical Research Methods
Wei Wang, Ruijiang Han, Menghan Zhang, Yuxian Wang, Tao Wang, Yongtian Wang, Xuequn Shang, Jiajie Peng
Summary: BrainMI is a novel framework that integrates brain connectome data and molecular-based gene association networks to predict brain disease genes. It constructs a new gene network based on resting-state functional magnetic resonance imaging data and brain region-specific gene expression data, and utilizes a multiple network integration method to learn low-dimensional features of genes. BrainMI achieves higher performance in predicting brain disease genes compared to existing state-of-the-art methods.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Computer Science, Information Systems
Jiajie Peng, Jinjin Yang, D. Vijay Anand, Xuequn Shang, Kelin Xia
Summary: The packing of genomic DNA into highly-order hierarchical assemblies greatly affects chromosome flexibility, dynamics, and functions. This study proposes an FRI-based model to quantify chromosome flexibility, which shows better accuracy and computational efficiency compared to the Gaussian network model (GNM). The model is based on the correlation between flexibility index and measurements for chromosome accessibility, and it can easily incorporate interchromosome interactions for improved accuracy.
FRONTIERS OF COMPUTER SCIENCE
(2022)
Article
Biotechnology & Applied Microbiology
Yongtian Wang, Liran Juan, Jiajie Peng, Tao Wang, Tianyi Zang, Yadong Wang
Summary: In this paper, a computational model is proposed for exploring metabolite-disease pairs and has good performance in predicting potential metabolites related to diseases through adequate validation. The results show that DLMPM has a better performance in prioritizing candidate diseases-related metabolites compared with the previous methods and would be helpful for researchers to reveal more information about human diseases.
Correction
Biochemical Research Methods
Tao Wang, Yongzhuang Liu, Quanwei Yin, Jiaquan Geng, Jin Chen, Xipeng Yin, Yongtian Wang, Xuequn Shang, Chunwei Tian, Yadong Wang, Jiajie Peng
BRIEFINGS IN BIOINFORMATICS
(2022)
Editorial Material
Genetics & Heredity
Tao Wang, Miguel E. Renteria, Jiajie Peng
FRONTIERS IN GENETICS
(2022)
Review
Biochemical Research Methods
Caiwei Zhen, Yuxian Wang, Jiaquan Geng, Lu Han, Jingyi Li, Jinghao Peng, Tao Wang, Jianye Hao, Xuequn Shang, Zhongyu Wei, Peican Zhu, Jiajie Peng
Summary: This paper conducts an in-depth benchmark study on the performance of eight single-cell Hi-C data clustering methods and implements an evaluation system. The research results provide important references for studying genome structure variation and cell separation at different cell cycle stages.
BRIEFINGS IN BIOINFORMATICS
(2022)
Review
Medicine, General & Internal
Jiajie Peng, Sihan Li, Xiangying Lin, Degui Zhong, Rong Zheng, Minghan Huang, Pengfei Li, Hongmei Song, Tetsuya Asakawa
Summary: This meta-analysis compared the clinical outcomes of two alternative surgeries for cervical spondylosis patients, ACDFWP and ACDA. The study found that ACDFWP had better cervical alignment compared to ACDA, but no significant differences were observed in other outcomes. Further well-designed studies are needed to confirm these findings.
INTRACTABLE & RARE DISEASES RESEARCH
(2022)
Article
Biochemical Research Methods
Wei Chen, Zhiwei Li, Hongyi Fang, Qianyuan Yao, Cheng Zhong, Jianye Hao, Qi Zhang, Xuanjing Huang, Jiajie Peng, Zhongyu Wei
Summary: In this article, two frameworks are proposed to support automatic medical consultation, which are doctor-patient dialogue understanding and task-oriented interaction. A new large medical dialogue dataset with multi-level fine-grained annotations is created, and five independent tasks are established, including named entity recognition, dialogue act classification, symptom label inference, medical report generation, and diagnosis-oriented dialogue policy. Benchmark results for each task are reported to demonstrate the usability of the dataset and establish a baseline for future studies.
Article
Multidisciplinary Sciences
Yafei Dai, Qiangqiang Zhang, Fei Wu, Jiajie Peng, Xiaobao Xu, Quansheng Du, Qing Pan, Yongjun Chen
Summary: With the development of natural science, interdisciplinary scientific research has become an inevitable trend in pursuit of scientific and technological innovations. Countries and regions like the United States, European Union, and China have established institutions to promote interdisciplinary research, but face challenges such as disciplinary barriers and funding mechanisms. To encourage interdisciplinary research, the National Natural Science Foundation of China established the Department of Interdisciplinary Sciences in 2020, aiming to create a culture of interdisciplinary cooperation and reform funding mechanisms.
CHINESE SCIENCE BULLETIN-CHINESE
(2023)
Article
Biochemical Research Methods
Yongtian Wang, Xinmeng Liu, Yewei Shen, Xuerui Song, Tao Wang, Xuequn Shang, Jiajie Peng
Summary: Circular RNAs (circRNAs) are important in biological processes and closely related to disease diagnosis, treatment, and inference. A computational model based on collaborative deep learning with circRNA multi-view functional annotations is proposed to predict potential circRNA-disease associations efficiently. The model shows better performance in predicting candidate disease-related circRNAs and has high practicality for the diagnosis and treatment of human diseases.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biotechnology & Applied Microbiology
Shuhui Liu, Yupei Zhang, Jiajie Peng, Xuequn Shang
Summary: Analyzing cell-cell communication in the tumor micro-environment helps understand cancer progression and drug tolerance. Existing methods based on known molecular interactions have limitations in predicting cellular communications. In this study, we propose an improved hierarchical variational autoencoder (HiVAE) model that utilizes single-cell RNA-seq data to estimate cell-cell communication scores.
BRIEFINGS IN FUNCTIONAL GENOMICS
(2023)
Article
Biochemical Research Methods
Tao Wang, Jinjin Yang, Yifu Xiao, Jingru Wang, Yuxian Wang, Xi Zeng, Yongtian Wang, Jiajie Peng
Summary: Drug-food interactions (DFIs) refer to the situation where some constituents of food affect the bioaccessibility or efficacy of a drug by involving in drug pharmacodynamic and/or pharmacokinetic processes. This article proposes a novel end-to-end graph embedding-based method named DFinder to identify DFIs. DFinder combines node attribute features and topological structure features to learn the representations of drugs and food constituents. The evaluation results indicate that DFinder outperforms other baseline methods.
Article
Biochemical Research Methods
Wei Chen, Cheng Zhong, Jiajie Peng, Zhongyu Wei
Summary: The automatic diagnostic system queries potential symptoms from patients and predicts possible diseases. Existing methods overlook the importance of symptom inquiry, resulting in low diagnostic accuracy. To address this, a new framework called DxFormer is proposed, which decouples symptom inquiry and disease diagnosis and optimizes them separately. Experimental results confirm that improving symptom recall can enhance diagnostic accuracy.
Proceedings Paper
Computer Science, Artificial Intelligence
Ruijiang Han, Wei Wang, Yuxi Long, Jiajie Peng
Summary: In this work, a post-processing unsupervised deep representation debiasing algorithm called DeepMinMax is proposed, which obtains unbiased representations directly from pre-trained representations without re-training or fine-tuning the entire model. Experimental results on synthetic and real-world datasets show that DeepMinMax outperforms existing state-of-the-art algorithms on downstream tasks.
THIRTY-SIXTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FOURTH CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE / TWELVETH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE
(2022)