Article
Genetics & Heredity
Qing Yuan, Fu-Xing Zuo, Hong-Qing Cai, Hai-Peng Qian, Jing-Hai Wan
Summary: A novel gene signature based on differentially expressed genes (DEGs) between glioblastoma (GBM) and healthy subventricular zone (SVZ) was developed for determining GBM patient prognosis. High-risk patients showed significantly reduced overall survival and were associated with lower immune cell counts and higher PD-L1 expression. Targeting these genes may be a therapeutic strategy for GBM.
FRONTIERS IN GENETICS
(2022)
Article
Microbiology
Xuan Jia, ZhiXiang Yin, Yu Peng
Summary: This article investigates the genetic factors causing male infertility and employs machine learning and statistical methods for cluster analysis of gene expression data. The study finds that the proposed model shows a better identification effect in the clustering and gene enrichment of male infertility patients' gene expression data.
FRONTIERS IN MICROBIOLOGY
(2023)
Article
Oncology
Jiji T. Kurup, Seongho Kim, Benjamin L. Kidder
Summary: Using a network biology framework, we identified cancer type-specific gene regulatory networks for 17 types of cancer, and elucidated core transcription factors and regulatory networks for multiple cancer types. By comparing normal tissues and cells to cancer type-specific networks, we found that the expression of key network-influencing factors can serve as a prognostic indicator for cancer patients.
Article
Multidisciplinary Sciences
Ahmad F. Al Musawi, Satyaki Roy, Preetam Ghosh
Summary: This paper explores link prediction algorithms based on assortativity profiles in complex networks and proposes a method for generating networks with different assortativity levels. Experimental results show that link prediction models that explore large neighborhoods around nodes perform well in both assortative and disassortative networks.
SCIENTIFIC REPORTS
(2022)
Article
Multidisciplinary Sciences
Zohar Pasternak, Noam Chapnik, Roy Yosef, Naama M. Kopelman, Edouard Jurkevitch, Elad Segev
Summary: Cliquely is a software tool based on the exploration of co-occurrence patterns, which can successfully identify known networks from various pathways and add novel proteins to these networks.
Article
Genetics & Heredity
De Wei, Shanghang Shen, Kun Lin, Feng Lu, Pengfeng Zheng, Shizhong Wu, Dezhi Kang
Summary: This study identified NPC2 as a potential prognostic biomarker for glioblastoma based on bioinformatics analysis and experimental validation. NPC2 was found to be upregulated in GBM samples, showing a negative correlation with tumor purity and tumor infiltrating immune cells. Functional experiments confirmed that NPC2 overexpression inhibited the proliferation and migration of GBM cells.
FRONTIERS IN GENETICS
(2021)
Article
Mathematics, Interdisciplinary Applications
Xiaohui Zhou, Asgarali Bouyer, Morteza Maleki, Moslem Mohammadi, Bahman Arasteh
Summary: In this study, a method is proposed to identify important layers with strong spreaders in multiplex networks using several key parameters. This method includes a layer weighting method that takes into account a layer's position, the number of active edges and critical nodes, the ratio of active nodes to all possible connections, and the intersection of intra-layer communication. Experimental results demonstrate that this method significantly outperforms existing methods in detecting high influential spreaders, highlighting the importance of using a suitable layer weighting measure for identifying potential spreaders in multiplex networks.
CHAOS SOLITONS & FRACTALS
(2023)
Article
Mathematics, Interdisciplinary Applications
Jun Ai, Tao He, Zhan Su, Lihui Shang
Summary: The identification of node importance is a challenging topic in network science. This paper proposes a novel method based on node propagation capability to measure the importance of nodes, and validates the effectiveness of the method through empirical analysis.
CHAOS SOLITONS & FRACTALS
(2022)
Article
Biochemical Research Methods
Yu-Ting Tan, Le Ou-Yang, Xingpeng Jiang, Hong Yan, Xiao-Fei Zhang
Summary: Learning how gene regulatory networks change under different conditions is important. Existing methods for inferring differential networks have limitations. In this study, a new method is proposed and shown to outperform other methods in simulation studies and applications.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2022)
Article
Biology
Donglin Zhou, Yimin Zhu, Peng Jiang, Tongfu Zhang, Jianfeng Zhuang, Tao Li, Linzeng Qi, Yunyan Wang
Summary: Fire disease is closely related to the formation and rupture of IA, and three potential hub genes involved in fire disease and cell infiltration have been identified. The results of this study improve our understanding of the mechanism of pyroptosis in IA.
BIOLOGICAL RESEARCH
(2023)
Article
Biochemical Research Methods
Fei Wang, Yulian Ding, Xiujuan Lei, Bo Liao, Fangxiang Wu
Summary: Drug repositioning is crucial for drug discovery, and this study proposes a new method, GS4CDRSC, based on sample clustering to identify gene signatures for cancer drug repositioning, which outperforms existing methods in predicting known drugs for specific cancers.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2022)
Article
Economics
Yucheng Sun, Wen Xu, Chuanhai Zhang
Summary: This paper tests whether the continuous component of a candidate factor can be explained by the latent common factors in high-frequency financial data. Two identification strategies are introduced for two types of regressions: regressions of intraday asset returns on the estimated factors and the candidate, and regressions of the candidate factor on the estimated ones. The test statistics are constructed by adding randomness to the residuals of the regressions, and the consistency of the randomized tests is demonstrated. Simulations are conducted to evaluate the tests' performance in finite samples, and empirical applications are performed to identify relationships between candidate factors and latent ones.
JOURNAL OF ECONOMETRICS
(2023)
Article
Computer Science, Interdisciplinary Applications
Min Li, Shuming Zhou, Dajin Wang, Gaolin Chen
Summary: Nodes in complex networks have different levels of importance. Identifying influential nodes is crucial for understanding the network's structure and practical applications. There are various measures to identify influential nodes, including Betweenness Centrality, Closeness Centrality, Degree Centrality, Information Centrality, Load Centrality, and Eigenvector Centrality. This paper proposes a new centrality ranking scheme named RCWTA, which combines resistive centrality with classic centrality and weighted TOPSIS ranking method. Simulation experiments show the effectiveness of the proposed resistive centrality measures, with resistive Harmonic Centrality performing the best.
JOURNAL OF COMPUTATIONAL SCIENCE
(2023)
Article
Physics, Multidisciplinary
Na Zhao, Hao Wang, Jun-jie Wen, Jie Li, Ming Jing, Jian Wang
Summary: This paper proposes a new centrality method called Spon Centrality, which combines algorithmic efficiency and accuracy. Spon only requires information within the three-hop neighborhood of a node to assess its centrality, exhibiting lower time complexity and suitability for large-scale networks.
NEW JOURNAL OF PHYSICS
(2023)
Article
Biochemical Research Methods
Ning Wang, Diane Lefaudeux, Anup Mazumder, Jingyi Jessica Li, Alexander Hoffmann
Summary: This study explores the identifiability of gene regulatory strategies using a mechanistic modeling approach and computational workflow. Results show that most GRS can be easily distinguished, but a third require more quantitative data. By developing an advanced error model and incorporating it into a Bayesian framework, GRS models for individual genes can be identified with multiple datasets, allowing for better allocation of experimental resources. Applying this workflow to immune response genes in macrophages revealed more combinatorial control than previously thought, supported by chromatin immuno-precipitation analysis.
PLOS COMPUTATIONAL BIOLOGY
(2021)
Article
Statistics & Probability
Saeid Amiri, Bertrand S. Clarke, Jennifer L. Clarke
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
(2018)
Article
Clinical Neurology
Vladislav A. Petyuk, Rui Chang, Manuel Ramirez-Restrepo, Noam D. Beckmann, Marc Y. R. Henrion, Paul D. Piehowski, Kuixi Zhu, Sven Wang, Jennifer Clarke, Matthew J. Huentelman, Fang Xie, Victor Andreev, Anzhelika Engel, Toumy Guettoche, Loida Navarro, Philip De Jager, Julie A. Schneider, Christopher M. Morris, Ian G. McKeith, Robert H. Perry, Simon Lovestone, Randall L. Woltjer, Thomas G. Beach, Lucia I. Sue, Geidy E. Serrano, Andrew P. Lieberman, Roger L. Albin, Isidre Ferrer, Deborah C. Mash, Christine M. Hulette, John F. Ervin, Eric M. Reiman, John A. Hardy, David A. Bennett, Eric Schadt, Richard D. Smith, Amanda J. Myers
Article
Nutrition & Dietetics
Julianne C. Kopf, Mallory J. Suhr, Jennifer Clarke, Seong-il Eyun, Jean-Jack M. Riethoven, Amanda E. Ramer-Tait, Devin J. Rose
Article
Statistics & Probability
Saeid Amiri, Bertrand S. Clarke, Jennifer L. Clarke, Hoyt Koepke
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
(2019)
Article
Statistics & Probability
Adrian Dobra, Camilo Valdes, Dragana Ajdic, Bertrand Clarke, Jennifer Clarke
ANNALS OF APPLIED STATISTICS
(2019)
Article
Multidisciplinary Sciences
Clara Penas, Marie E. Maloof, Vasileios Stathias, Jun Long, Sze Kiat Tan, Jose Mier, Yin Fang, Camilo Valdes, Jezabel Rodriguez-Blanco, Cheng-Ming Chiang, David J. Robbins, Daniel J. Liebl, Jae K. Lee, Mary E. Hatten, Jennifer Clarke, Nagi G. Ayad
NATURE COMMUNICATIONS
(2019)
Article
Multidisciplinary Sciences
Kyung R. Min, Adriana Galvis, Katherine L. Baquerizo Nole, Rohita Sinha, Jennifer Clarke, Robert S. Kirsner, Dragana Ajdic
Article
Cell Biology
Cole P. Frisbie, Alexander Y. Lushnikov, Alexey Krasnoslobodtsev, Jean-Jack M. Riethoven, Jennifer L. Clarke, Elena Stepchenkova, Armen Petrosyan
Article
Microbiology
Enakshy Dutta, John Dustin Loy, Caitlyn A. Deal, Emily L. Wynn, Michael L. Clawson, Jennifer Clarke, Bing Wang
Summary: This study developed a qPCR assay to provide information about AMR status in BRD cases, serving as an alternative to culture-based tests. The qPCR method showed good agreement with the gold-standard test for both MACs and TETs in lung tissues, while passing validation criteria only for TET resistance detection in nasal swabs. The culture-independent assay developed here has the potential to rapidly characterize AMR in BRD cases directly from clinical samples with equivalent accuracy and higher efficiency than culture-based tests.
Article
Engineering, Environmental
Yuepeng Sun, Bertrand Clarke, Jennifer Clarke, Xu Li
Summary: This study explores using a machine learning approach, random forests (RF's), to identify the associations between antibiotic resistance genes (ARGs) and bacterial taxa in activated sludge of wastewater treatment plants (WWTPs). The results show that RF's can successfully predict the abundance of select ARGs, with (opportunistic) pathogens and indicator bacteria having more positive functional relationships. Machine learning approaches like RF's have the potential to identify bacterial hosts of ARGs and reveal functional relationships in WWTPs.
Article
Nutrition & Dietetics
Katherine M. Weh, Yun Zhang, Connor L. Howard, Amy B. Howell, Jennifer L. Clarke, Laura A. Kresty
Summary: This study investigated the inhibitory effects of cranberry polyphenols on premalignant Barrett's esophagus (BE) and esophageal adenocarcinoma (EAC) cell lines. The results showed that cranberry polyphenols, particularly C-PAC, were more effective at inducing cell death in these cell lines compared to a combination of anthocyanins, flavonoids, and glycosides (AFG). The analysis of protein pathways revealed previously unidentified mechanisms by which cranberry polyphenols exert their cancer inhibitory effects. These findings provide new insights into the potential use of cranberry constituents as preventive agents for EAC.
Article
Biochemical Research Methods
Carolyn J. Lawrence-Dill, Robyn L. Allscheid, Albert Boaitey, Todd Bauman, Edward S. Buckler, Jennifer L. Clarke, Christopher Cullis, Jack Dekkers, Cassandra J. Dorius, Shawn F. Dorius, David Ertl, Matthew Homann, Guiping Hu, Mary Losch, Eric Lyons, Brenda Murdoch, Zahra-Katy Navabi, Somashekhar Punnuri, Fahad Rafiq, James M. Reecy, Patrick S. Schnable, Nicole M. Scott, Moira Sheehan, Xavier Sirault, Margaret Staton, Christopher K. Tuggle, Alison Van Eenennaam, Rachael Voas
PLOS COMPUTATIONAL BIOLOGY
(2022)
Article
Genetics & Heredity
Michael S. Adamowicz, Taylor N. Rambo, Jennifer L. Clarke
Summary: Mixed human DNA samples can be challenging for forensic analysts, but probabilistic genotyping software like MaSTR provides accurate statistical data by calculating likelihood ratios.
Article
Virology
Ema H. Graham, Wesley A. Tom, Alison C. Neujahr, Michael S. Adamowicz, Jennifer L. Clarke, Joshua R. Herr, Samodha C. Fernando
Summary: This study reveals that the human skin virome is mainly composed of tailed Caudovirales phages that carry auxiliary genes to increase bacterial host fitness through antimicrobial resistance. The virome is associated with auxiliary metabolic genes and antimicrobial resistance genes, which help increase bacterial host fitness.
Article
Mathematical & Computational Biology
Jennifer L. Clarke, Laurel D. Cooper, Monica F. Poelchau, Tanya Z. Berardini, Justin Elser, Andrew D. Farmer, Stephen Ficklin, Sunita Kumari, Marie-Angelique Laporte, Rex T. Nelson, Rie Sadohara, Peter Selby, Anne E. Thessen, Brandon Whitehead, Taner Z. Sen
Summary: In the past few decades, there has been a rapid growth in the number and scope of agricultural genetics, genomics, and breeding databases and resources. The AgBioData Consortium aims to facilitate data management and integration, and a survey conducted among its members showed positive data-sharing practices but no substantial change in ontology use. Recommendations include providing training, further studying metadata sharing, and improving data sharing and ontology use.
DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION
(2023)