Imputing Covariance for Meta-Analysis in the Presence of Interaction by Enwu Liu, Ryan Yan Liu

“…Detecting interactions is a critical aspect of medical research. …”
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POST-SOVIET TERRITORIAL CONFLICTS IN MOLDOVA AND ESTONIA: WHEN POLITICAL ELITES CAN LEARN FROM THE MUTUAL MISTAKES by A. Yu. Sungurov, N. Belyavskaya

“…Later, this Estonian experience was already applied by Moldovan  politicians in the process of relieving tension in the situation of Gagauzia. The detected interaction of political elites of Moldova and Transnistria is viewed from the perspective  of the concept of  “policy learning” by Hugh Heclo and Richard Rose.…”
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Preparation and Characterization of a Bioartificial Polymeric Material: Bilayer of Cellulose Acetate-PVA by Andrés Bernal-Ballén, Ivo Kuritka, Petr Saha

“…Moreover, beyond the interface, there were no detected interactions between the polymers as can be evidenced by the presence of a unique Tg in the bilayer. …”
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GOTHiC, a probabilistic model to resolve complex biases and to identify real interactions in Hi-C data. by Borbala Mifsud, Inigo Martincorena, Elodie Darbo, Robert Sugar, Stefan Schoenfelder, Peter Fraser, Nicholas M Luscombe

“…Existing methods use complex models to account for biases and do not provide a significance threshold for detecting interactions. Here we introduce a simple binomial probabilistic model that resolves complex biases and distinguishes between true and false interactions. …”
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Super-resolved trajectory-derived nanoclustering analysis using spatiotemporal indexing by Tristan P. Wallis, Anmin Jiang, Kyle Young, Huiyi Hou, Kye Kudo, Alex J. McCann, Nela Durisic, Merja Joensuu, Dietmar Oelz, Hien Nguyen, Rachel S. Gormal, Frédéric A. Meunier

“…Spatial indexing is widely used in video games to detect interactions between moving geometric objects. Here, we use the R-tree spatial indexing algorithm to determine the overlap of the bounding boxes of individual molecular trajectories to establish membership in nanoclusters. …”
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scSDNE: A semi-supervised method for inferring cell-cell interactions based on graph embedding. by Chenchen Jia, Haiyun Wang, Jianping Zhao, Junfeng Xia, Chunhou Zheng

“…Experiments on real single-cell RNA sequencing (scRNA-seq) datasets demonstrate that our method detects interactions with a high degree of reliability compared with other methods. …”
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A transcription-centric model of SNP-age interaction. by Kun Wang, Mahashweta Basu, Justin Malin, Sridhar Hannenhalli

“…In six hypertension-implicated tissues, detected interactions significantly inform hypertension state of an individual. …”
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Application of Machine Learning Models in Social Sciences: Managing Nonlinear Relationships by Theodoros Kyriazos, Mary Poga

“…A discussion follows on how these models automatically detect interactions and threshold effects, offering superior predictive power and robustness against noise compared to traditional methods. …”
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Critical assessment of LC3/GABARAP ligands used for degrader development and ligandability of LC3/GABARAP binding pockets by Martin P. Schwalm, Johannes Dopfer, Adarsh Kumar, Francesco A. Greco, Nicolas Bauer, Frank Löhr, Jan Heering, Sara Cano-Franco, Severin Lechner, Thomas Hanke, Ivana Jaser, Viktoria Morasch, Christopher Lenz, Daren Fearon, Peter G. Marples, Charles W. E. Tomlinson, Lorene Brunello, Krishna Saxena, Nathan B. P. Adams, Frank von Delft, Susanne Müller, Alexandra Stolz, Ewgenij Proschak, Bernhard Kuster, Stefan Knapp, Vladimir V. Rogov

“…Surprisingly, we were unable to detect interaction with their designated target LC3 using a diversity of biophysical methods. …”
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Challenges in Detecting Ecological Interactions Using Sedimentary Ancient DNA Data by Fiona Margaret Callahan, Jacky Kaiyuan Li, Rasmus Nielsen

“…Additionally, we find that for more realistic simulation scenarios, with sample sizes that are currently realistic for this type of data, models are typically unable to detect interactions better than random assignment of associations. …”
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PR3 and elastase alter PAR1 signaling and trigger vWF release via a calcium-independent mechanism from glomerular endothelial cells. by Samantha P Tull, Anne Bevins, Sahithi Jyothsna Kuravi, Simon C Satchell, Bahjat Al-Ani, Stephen P Young, Lorraine Harper, Julie M Williams, George Ed Rainger, Caroline O S Savage

“…Both proteases interacted with and disarmed surface GEC PAR1, but there was no detectable interaction with cellular PAR2. Neither protease induced a calcium response in GEC. …”
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Hsa_circ_0000423 promotes colorectal cancer EMT and immune escape by competitive adsorption of miR-369-3p mediating CCND1 expression by TianFu Huang, KaiHai Jiang, LinTao Li, GuangSheng Li, YuSheng Cao, XuSen Huang

“…Assays with dual luciferase reporter and RIP were conducted to detect interactions between hsa_circ_0000423, miR-369-3p and CCND1. …”
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LuTHy: a double‐readout bioluminescence‐based two‐hybrid technology for quantitative mapping of protein–protein interactions in mammalian cells by Philipp Trepte, Sabrina Kruse, Simona Kostova, Sheila Hoffmann, Alexander Buntru, Anne Tempelmeier, Christopher Secker, Lisa Diez, Aline Schulz, Konrad Klockmeier, Martina Zenkner, Sabrina Golusik, Kirstin Rau, Sigrid Schnoegl, Craig C Garner, Erich E Wanker

“…The double‐readout procedure detects interactions with higher sensitivity than traditional single‐readout methods and is broadly applicable, for example, for detecting the effects of small molecules or disease‐causing mutations on PPIs. …”
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Structural basis for the recognition of two different types of receptors by Western equine encephalitis virus by Bingting Ma, Ziyi Cao, Weijia Ding, Xinzheng Zhang, Ye Xiang, Duanfang Cao

“…LA1-2, LA2-3, LA3-4, LA4-5, and LA5-6 of VLDLR all have detectable interactions with WEEV. Detailed structures of WEEV in complex with LA1-2 and LA2-3 show that the N-terminal LA repeat binds in the cleft and that the C-terminal LA repeat is attached to the E2 B domain. …”
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A robust and adaptive framework for interaction testing in quantitative traits between multiple genetic loci and exposure variables. by Julian Hecker, Dmitry Prokopenko, Matthew Moll, Sanghun Lee, Wonji Kim, Dandi Qiao, Kirsten Voorhies, Woori Kim, Stijn Vansteelandt, Brian D Hobbs, Michael H Cho, Edwin K Silverman, Sharon M Lutz, Dawn L DeMeo, Scott T Weiss, Christoph Lange

“…While this strategy can increase the power to detect interactions, identifying contributing genes and pathways is difficult based on these relatively global results. …”
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Genome-wide association study between SARS-CoV-2 single nucleotide polymorphisms and virus copies during infections. by Ke Li, Chrispin Chaguza, Julian Stamp, Yi Ting Chew, Nicholas F G Chen, David Ferguson, Sameer Pandya, Nick Kerantzas, Wade Schulz, Yale SARS-CoV-2 Genomic Surveillance Initiative, Anne M Hahn, C Brandon Ogbunugafor, Virginia E Pitzer, Lorin Crawford, Daniel M Weinberger, Nathan D Grubaugh

“…We further applied a marginal epistasis test to detect interactions among SNPs and identified multiple pairs of substitutions located in the spike gene that have non-linear effects on viral copies. …”
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Base Station Placement Algorithm for Large-Scale LTE Heterogeneous Networks. by Seungseob Lee, SuKyoung Lee, Kyungsoo Kim, Yoon Hyuk Kim

“…Both the simulation and analytical results indicate that the proposed solution outperforms the random-grouping based EA as well as an EA that detects interacting variables by monitoring the changes in the objective function algorithm in terms of system throughput performance.…”
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When to Use What: A Comparison of Three Approaches to Quantify Relationships Among Ecosystem Services by Zhen Zhong, Bochuan Zhou, Lingqiang Kong, Xuening Fang

“…We found that: (1) the ES trade-off/synergy relationships detected by the three approaches exhibit significant divergence, with only 1.45% consistency among the 66 pairs of ES relationships. (2) All three approaches can overlook ES trade-offs, miss ES synergies, and erroneously detect interactions where none exist. (3) The mechanisms contributing to the misidentification of ES relationships by the three approaches include: neglecting the underlying assumptions of different approaches, insufficient time interval length, short time series of ES data, data aggregation effects, non-linear changes in ESs, time lag effects of ES relationships, among others. …”
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mimicINT: A workflow for microbe-host protein interaction inference [version 2; peer review: 2 approved] by Andreas Zanzoni, Lilian Drets, Mégane Boujeant, Marceau Cristianini, Christine Brun, Aurélie Bergon, Lionel Spinelli, Kevin Maldonado, Sébastien A. Choteau

“…Results In two use cases, mimicINT can identify potential interfaces in experimentally detected interaction between pathogenic Escherichia coli type-3 secreted effectors and human proteins and infer biologically relevant interactions between Marburg virus and human proteins. …”
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Association between Val158Met COMT, TNF-α -857 C>T, TNFR1 36 A>G, IL-1α 4845 G>T and IL-10 -1082 A>G polymorphisms and risk of early-onset preeclampsia and its complications by Krnjeta Tijana, Mirković Ljiljana, Ignjatović Svetlana, Tomšević Dragana, Lukić Jelena, Topalov Drina, Majkić-Singh Nada

“…The COMT Met allele as well as IL-1α T showed a protective role, decreasing the risk of early-onset PE after age and body mass index (BMI) adjustments. The detected interactions between the COMT Met and IL-10 A alleles, as well as between the COMT Met and TNF-α T alleles were insignificant after age and BMI adjustments. …”
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