Article
Multidisciplinary Sciences
Etienne J. Orliac, Daniel Trejo Banos, Sven E. Ojavee, Kristi Lall, Reedik Magi, Peter M. Visscher, Matthew R. Robinson
Summary: The use of the Bayesian grouped mixture of regressions model (GMRM) in biobanks has shown high genomic prediction accuracy and increased detection of independent loci for genetic association discovery. Considering differences in SNP markers and incorporating prior knowledge of genomic function is crucial for genomic prediction and discovery in large-scale individual-level studies.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2022)
Article
Multidisciplinary Sciences
Etienne J. Orliac, Daniel Trejo Banos, Sven E. Ojavee, Kristi Lall, Reedik Magi, Peter M. Visscher, Matthew R. Robinson
Summary: Genetically informed, deep-phenotyped biobanks are an important research resource, and the recently developed Bayesian grouped mixture of regressions model (GMRM) has been shown to achieve the highest genomic prediction accuracy to date. Comparing to other approaches, GMRM outperforms annotation prediction models by 15-18% and improves the discovery of independent loci by 62-65%. The study emphasizes the importance of incorporating MAF and LD information in genetic associations for both genomic prediction and discovery in large-scale individual-level studies.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2022)
Article
Computer Science, Information Systems
Xingyu Wu, Bingbing Jiang, Kui Yu, Huanhuan Chen
Summary: The article introduces a separation and recovery MB discovery algorithm (SRMB) that improves the accuracy and data efficiency of MB discovery through a two-phase discovery strategy to find more true positives. Experimental results demonstrate the effectiveness and superiority of SRMB in terms of MB discovery, BN structure learning, and feature selection.
INFORMATION SCIENCES
(2021)
Article
Engineering, Industrial
Ahmed El-Awady, Kumaraswamy Ponnambalam
Summary: The development of failure analysis techniques for complex engineering systems faces challenges due to interrelations and uncertainty. Bayesian Networks provide a flexible way to represent such systems probabilistically. Proposed methodologies such as SSBNs and MCSSBNs support efficient prediction of failure probabilities for complex networks with multiple uncertain interconnected variables.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2021)
Article
Biochemistry & Molecular Biology
Gang Zhang, Feng Wang, Shan Li, Kai-Wen Cheng, Yingying Zhu, Ran Huo, Elyar Abdukirim, Guifeng Kang, Tsui-Fen Chou
Summary: RUVBL1 and RUVBL2 are highly conserved AAA ATPases that play a significant role in cancer progression. This study utilized docking-based virtual screening to identify compounds that inhibit the RUVBL1/2 complex. Seven compounds were found to have inhibitory activity in enzymatic and cellular assays. A series of pyrazolo[1,5-a]pyrimidine-3-carboxamide analogs were synthesized based on compound 15, which showed good potential for structural manipulation. Analysis of the structure-activity relationship revealed the importance of the benzyl group on R2 and the aromatic ring-substituted piperazinyl on R4 for inhibitory activity. Compound 18 exhibited the strongest inhibition and showed potential anticancer activity in multiple cell lines. Proteomic analysis identified cellular proteins dysregulated by compounds 16, 18, and 19. These findings suggest that compound 18 could serve as a starting point for structural modifications to improve potential therapeutic molecules.
BIOORGANIC & MEDICINAL CHEMISTRY
(2022)
Article
Construction & Building Technology
Yuefen Gao, Yang Hang, Mengliang Yang
Summary: This paper introduces the Improved CEEMDAN algorithm and Markov chain correction method to predict air conditioning cooling load more accurately. By decomposing affecting parameters and establishing component prediction model, and using parallel computing to enhance operation speed, the improved model shows improved accuracy and is more suitable for practical applications.
JOURNAL OF BUILDING ENGINEERING
(2021)
Article
Biochemistry & Molecular Biology
Anat Etzion-Fuchs, David A. Todd, Mona Singh
Summary: A novel machine learning method dSPRINT has been introduced to predict whether a protein domain binds DNA, RNA, small molecules, ions or peptides, and the positions within it that participate in these interactions. Through stringent cross-validation testing, it has shown excellent performance in uncovering ligand-binding positions and domains.
NUCLEIC ACIDS RESEARCH
(2021)
Article
Computer Science, Information Systems
Yanjun Han, Soham Jana, Yihong Wu
Summary: This paper studies the learning problem with dependent data, where the goal is to predict the next state based on a trajectory of length n from a stationary Markov chain with k states. The optimal prediction risk in Kullback-Leibler divergence is shown to be T(k^2nlogn/k^2) for 3≤k≤O(vn), which is slower than the rate T(loglognn) for k = 2 previously shown. The slower rates can be attributed to the memory in the data, as the spectral gap of the Markov chain can be arbitrarily small. By studying irreducible reversible chains with a prescribed spectral gap, the memory effect is quantified, and it is shown that the prediction risk in the Markov model is O(k^2n) as long as the spectral gap is not excessively small, which coincides with that of an iid model with the same number of parameters. Extensions to higher-order Markov chains are also obtained.
IEEE TRANSACTIONS ON INFORMATION THEORY
(2023)
Article
Computer Science, Artificial Intelligence
Clement Fernandes, Wojciech Pieczynski
Summary: Hidden Markov chains (HMCs) are widely used in unsupervised Bayesian hidden discrete data restoration. In this paper, a new model that simultaneously extends hidden semi-Markov chains (HSMCs) and hidden evidential Markov chains (HEMCs) is proposed. Its value is validated through experiments on hand-drawn images noised with artificial noises.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2023)
Article
Computer Science, Information Systems
Zhaolong Ling, Bo Li, Yiwen Zhang, Qingren Wang, Kui Yu, Xindong Wu
Summary: Causal feature selection is gaining attention for improving interpretability of predictive models. However, the existing framework is time-consuming on high-dimensional data. To tackle this, we propose CFS, a novel framework with efficient spouses discovery. It only discovers the PC of variables in some children of the target variable, based on the dependency change property. We also propose four new causal feature selection algorithms based on CFS and existing PC discovery algorithms, which are experimentally validated for efficiency and accuracy.
IEEE TRANSACTIONS ON BIG DATA
(2023)
Article
Environmental Sciences
Babak Jamhiri, Yongfu Xu, Fazal E. Jalal
Summary: This study investigated different cracking prediction models and performed sensitivity analysis to evaluate the uncertainties of the models and parameters. The findings suggest that the linear elastoplastic model provides reasonable predictions, while soil parameter variations play an important role. Furthermore, the findings of this study can improve the decision-making processes for expansive soil stabilization by considering a variety of environmental conditional probabilities.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2022)
Article
Engineering, Civil
Peyman Mahmoudi, Allahbakhsh Rigi
Summary: The main objective of this study is to predict the transition probability of different classes of droughts in Iran. The effective drought index (EDI) was used to recognize the abundance of various drought classes in Iran. Cluster analysis was conducted to divide Iran into five separate regions. Homogenous and nonhomogenous Markov chains were used to extract features related to drought severity and prediction. The results show that drought probability decreases with increased severity and the nonhomogenous Markov chain formulation provides more accurate predictions for arid and semiarid regions.
JOURNAL OF HYDROLOGIC ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Yunxia Wang, Fuyuan Cao, Kui Yu, Jiye Liang
Summary: In this article, we propose a new algorithm that utilizes the interventional properties of a causal model to discover the direct causes and direct effects of a target variable from multiple datasets with different manipulations. The algorithm is more suited to real-world cases and tackles a specific challenge. Experimental results validate its effectiveness.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Evolutionary Biology
Guy Baele, Mandev S. Gill, Paul Bastide, Philippe Lemey, Marc A. Suchard
Summary: Markov models are foundational in phylogenetic inference, but often overlook heterogeneity in substitution processes over time on a site-specific level. Introducing Markov-modulated models can improve phylogenetic tree estimation by allowing for variability in substitution behavior across lineages. Through the incorporation of time variability, researchers can compose a wider range of models and improve the overall fit compared to standard substitution models.
SYSTEMATIC BIOLOGY
(2021)
Article
Agriculture, Multidisciplinary
Danxia Wu, Li Wang, Wei Li, Xiangyang Li
Summary: This study reports a new insecticide, 3l, targest OBPs and shows that it is more effective in inhibiting the bioactivity of Bemisia tabaci Mediterranean than imidacloprid.
JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY
(2023)
Correction
Biotechnology & Applied Microbiology
Sergei Yakneen, Sebastian M. Waszak, Michael Gertz, Jan O. Korbel, Brice Aminou, Javier Bartolome, Keith A. Boroevich, Rich Boyce, Angela N. Brooks, Alex Buchanan, Ivo Buchhalter, Adam P. Butler, Niall J. Byrne, Andy Cafferkey, Peter J. Campbell, Zhaohong Chen, Sunghoon Cho, Wan Choi, Peter Clapham, Brandi N. Davis-Dusenbery, Francisco M. De La Vega, Jonas Demeulemeester, Michelle T. Dow, Lewis Jonathan Dursi, Juergen Eils, Roland Eils, Kyle Ellrott, Claudiu Farcas, Francesco Favero, Nodirjon Fayzullaev, Vincent Ferretti, Paul Flicek, Nuno A. Fonseca, Josep Ll. Gelpi, Gad Getz, Bob Gibson, Robert L. Grossman, Olivier Harismendy, Allison P. Heath, Michael C. Heinold, Julian M. Hess, Oliver Hofmann, Jongwhi H. Hong, Thomas J. Hudson, Barbara Hutter, Carolyn M. Hutter, Daniel Hubschmann, Seiya Imoto, Sinisa Ivkovic, Seung-Hyup Jeon, Wei Jiao, Jongsun Jung, Rolf Kabbe, Andre Kahles, Jules N. A. Kerssemakers, Hyung-Lae Kim, Hyunghwan Kim, Jihoon Kim, Youngwook Kim, Kortine Kleinheinz, Michael Koscher, Antonios Koures, Milena Kovacevic, Chris Lawerenz, Ignaty Leshchiner, Jia Liu, Dimitri Livitz, George L. Mihaiescu, Sanja Mijalkovic, Ana Mijalkovic Lazic, Satoru Miyano, Naoki Miyoshi, Hardeep K. Nahal-Bose, Hidewaki Nakagawa, Mia Nastic, Steven J. Newhouse, Jonathan Nicholson, Brian D. O'Connor, David Ocana, Kazuhiro Ohi, Lucila Ohno-Machado, Larsson Omberg, B. F. Francis Ouellette, Nagarajan Paramasivam, Marc D. Perry, Todd D. Pihl, Manuel Prinz, Montserrat Puiggros, Petar Radovic, Keiran M. Raine, Esther Rheinbay, Mara Rosenberg, Romina Royo, Gunnar Ratsch, Gordon Saksena, Matthias Schlesner, Solomon I. Shorser, Charles Short, Heidi J. Sofia, Jonathan Spring, Lincoln D. Stein, Adam J. Struck, Grace Tiao, Nebojsa Tijanic, David Torrents, Peter Van Loo, Miguel Vazquez, David Vicente, Jeremiah A. Wala, Zhining Wang, Sebastian M. Waszak, Joachim Weischenfeldt, Johannes Werner, Ashley Williams, Youngchoon Woo, Adam J. Wright, Qian Xiang, Liming Yang, Denis Yuen, Christina K. Yung, Junjun Zhang, Jan O. Korbel
NATURE BIOTECHNOLOGY
(2023)
Editorial Material
Biochemistry & Molecular Biology
Emanuel Schwarz, Dag Alnaes, Ole A. Andreassen, Han Cao, Junfang Chen, Franziska Degenhardt, Daria Doncevic, Dominic Dwyer, Roland Eils, Jeanette Erdmann, Carl Herrmann, Martin Hofmann-Apitius, Tobias Kaufmann, Nikolaos Koutsouleris, Alpha T. Kodamullil, Adyasha Khuntia, Soeren Mucha, Markus M. Noethen, Riya Paul, Mads L. Pedersen, Andres Quintero, Heribert Schunkert, Ashwini Sharma, Heike Tost, Lars T. Westlye, Youcheng Zhang, Andreas Meyer-Lindenberg
MOLECULAR PSYCHIATRY
(2021)
Article
Oncology
Elisa Espinet, Zuguang Gu, Charles D. Imbusch, Nathalia A. Giese, Magdalena Buescher, Mariam Safavi, Silke Weisenburger, Corinna Klein, Vanessa Vogel, Mattia Falcone, Jacob Insua-Rodriguez, Manuel Reitberger, Vera Thiel, Steffi O. Kossi, Alexander Muckenhuber, Karnjit Sarai, Alex Y. L. Lee, Elyne Backx, Soheila Zarei, Matthias M. Gaida, Manuel Rodriguez-Paredes, Elisa Donato, Hsi-Yu Yen, Roland Eils, Matthias Schlesner, Nicole Pfarr, Thilo Hackert, Christoph Plass, Benedikt Brors, Katja Steiger, Dieter Weichenhan, H. Efsun Arda, Ilse Rooman, Janel L. Kopp, Oliver Strobel, Wilko Weichert, Martin R. Sprick, Andreas Trumpp
Summary: Pancreatic ductal adenocarcinoma (PDAC) has two distinct subtypes, one aggressive subtype with low methylation and high IFN signaling, and another subtype with high methylation and low IFN signaling. These subtypes preserve traits from normal ductal/acinar cells associated with IFN signaling.
Article
Biochemistry & Molecular Biology
Marina Laplana, Matthias Bieg, Christian Faltus, Svitlana Melnik, Olga Bogatyrova, Zuguang Gu, Thomas Muley, Michael Meister, Hendrik Dienemann, Esther Herpel, Christopher Amos, Matthias Schlesner, Roland Eils, Christoph Plass, Angela Risch
Summary: This study utilized a targeted sequencing approach to investigate DNA methylation changes in NSCLC patients, identifying differential methylation regions and confirming potential regulatory elements. The research contributes to understanding the mechanisms of lung cancer initiation and progression, and offers new potential targets for cancer treatment.
Article
Oncology
Martina K. Zowada, Stephan M. Tirier, Sebastian M. Dieter, Teresa G. Krieger, Ava Oberlack, Robert Lorenz Chua, Mario Huerta, Foo Wei Ten, Karin Laaber, Jeongbin Park, Katharina Jechow, Torsten Mueller, Mathias Kalxdorf, Mark Kriegsmann, Katharina Kriegsmann, Friederike Herbst, Jeroen Krijgsveld, Martin Schneider, Roland Eils, Hanno Glimm, Christian Conrad, Claudia R. Ball
Summary: Different cell types with tumor-initiating cell (TIC) activity in colorectal cancer (CRC) show distinct gene expression patterns at single-cell level. Metabolic states are closely linked to TIC activity in primary CRC cultures, suggesting oxidative phosphorylation as a potential target for novel therapies. Transcriptional heterogeneity at single-cell resolution identifies functional states during TIC differentiation and may reveal novel vulnerabilities in human CRC.
Article
Pathology
Florian Haller, Lea D. Schlieben, Fulvia Ferrazzi, Michael Michal, Robert Stoehr, Evgeny A. Moskalev, Matthias Bieg, Judith V. M. G. Bovee, Philip Stroebel, Naveed Ishaque, Robert Gruetzmann, Norbert Meidenbauer, Roland Eils, Stefan Wiemann, Arndt Hartmann, Michal Michal, Abbas Agaimy
Summary: This study evaluated NAB2-STAT6 gene fusion variants in lipomatous SFTs and found significant differences in gene expression and fusion variants compared to nonlipomatous SFTs. The results provide a possible molecular genetic basis for the distinct morphologic features of lipomatous SFTs.
AMERICAN JOURNAL OF PATHOLOGY
(2021)
Review
Biochemical Research Methods
Florian Borchert, Andreas Mock, Aurelie Tomczak, Jonas Huegel, Samer Alkarkoukly, Alexander Knurr, Anna-Lena Volckmar, Albrecht Stenzinger, Peter Schirmacher, Juergen Debus, Dirk Jaeger, Thomas Longerich, Stefan Froehling, Roland Eils, Nina Bougatf, Ulrich Sax, Matthieu-P Schapranow
Summary: Precision oncology is a rapidly evolving interdisciplinary medical specialty, mainly driven by academia. The most commonly used knowledge bases provide good programmatic access options and have been integrated into software tools, but access options are limited for information regarding clinical classifications and therapy recommendations. Specialized tools are needed for different steps in the diagnostic process.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Biochemistry & Molecular Biology
Stefan C. Dentro, Ignaty Leshchiner, Kerstin Haase, Maxime Tarabichi, Jeff Wintersinger, Amit G. Deshwar, Kaixian Yu, Yulia Rubanova, Geoff Macintyre, Jonas Demeulemeester, Ignacio Vazquez-Garcia, Kortine Kleinheinz, Dimitri G. Livitz, Salem Malikic, Nilgun Donmez, Subhajit Sengupta, Pavana Anur, Clemency Jolly, Marek Cmero, Daniel Rosebrock, Steven E. Schumacher, Yu Fan, Matthew Fittall, Ruben M. Drews, Xiaotong Yao, Thomas B. K. Watkins, Juhee Lee, Matthias Schlesner, Hongtu Zhu, David J. Adams, Nicholas McGranahan, Charles Swanton, Gad Getz, Paul C. Boutros, Marcin Imielinski, Rameen Beroukhim, S. Cenk Sahinalp, Yuan Ji, Martin Peifer, Inigo Martincorena, Florian Markowetz, Ville Mustonen, Ke Yuan, Moritz Gerstung, Paul T. Spellman, Wenyi Wang, Quaid D. Morris, David C. Wedge, Peter Van Loo
Summary: By extensively characterizing intra-tumor heterogeneity (ITH) across 2,658 cancer samples spanning 38 cancer types, this study found evidence of distinct subclonal expansions in nearly all informative samples, with frequent branching relationships between subclones. Positive selection of subclonal driver mutations was observed across most cancer types, indicating the importance of ITH and its drivers in tumor evolution.
Editorial Material
Cardiac & Cardiovascular Systems
Ulf Landmesser, Irina Lehmann, Roland Eils
EUROPEAN HEART JOURNAL
(2021)
Article
Biotechnology & Applied Microbiology
J. Loske, J. Roehmel, S. Lukassen, S. Stricker, Vg Magalhaes, J. Liebig, Rl Chua, L. Thurmann, M. Messingschlager, A. Seegebarth, B. Timmermann, S. Klages, M. Ralser, B. Sawitzki, Le Sander, Vm Corman, C. Conrad, S. Laudi, M. Binder, S. Trump, R. Eils, M. A. Mall, I Lehmann
Summary: Children exhibit higher basal expression of relevant pattern recognition receptors in airway immune cells, resulting in stronger early innate antiviral responses to SARS-CoV-2 infection compared to adults. Unique immune cell subpopulations, including cytotoxic T cells and memory CD8+ T cells, predominantly occur in children.
NATURE BIOTECHNOLOGY
(2022)
Article
Immunology
Anita Balazs, Pamela Millar-Buechner, Michael Muelleder, Vadim Farztdinov, Lukasz Szyrwiel, Annalisa Addante, Aditi Kuppe, Tihomir Rubil, Marika Drescher, Kathrin Seidel, Sebastian Stricker, Roland Eils, Irina Lehmann, Birgit Sawitzki, Jobst Roehmel, Markus Ralser, Marcus A. Mall
Summary: The nasal epithelium acts as the first line of defense against inhaled pathogens, allergens, and irritants, and plays a crucial role in the development of various respiratory diseases. This study aims to investigate the age-related differences in the structure and function of the nasal epithelium. The results showed intrinsic, age-related differences in the structure and function of the nasal epithelium, which may contribute to the development of age-dependent respiratory diseases.
FRONTIERS IN IMMUNOLOGY
(2022)
Article
Genetics & Heredity
Sebastian Tiesmeyer, Shashwat Sahay, Niklas Mueller-Boetticher, Roland Eils, Sebastian D. Mackowiak, Naveed Ishaque
Summary: The combination of a cell's transcriptional profile and location defines its function in a spatial context. Spatially resolved transcriptomics (SRT) has become a popular method for characterizing cells in situ. However, the correct aggregation of mRNA molecules into cells has been a computational problem in single-molecule SRT methods. SSAM-lite is an easy-to-use graphical interface tool that enables rapid and segmentation-free cell typing of SRT data in a web browser.
FRONTIERS IN GENETICS
(2022)
Article
Multidisciplinary Sciences
Philipp Klein, Stefan M. Kallenberger, Hanna Roth, Karsten Roth, Thi Bach Nga Ly-Hartig, Vera Magg, Janez Ales, Soheil Rastgou Talemi, Yu Qiang, Steffen Wolf, Olga Oleksiuk, Roma Kurilov, Barbara Di Ventura, Ralf Bartenschlager, Roland Eils, Karl Rohr, Fred A. Hamprecht, Thomas Hoefer, Oliver T. Fackler, Georg Stoecklin, Alessia Ruggieri
Summary: This study elucidated the molecular mechanism of stress granules formation by integrating quantitative experiments and mathematical modeling. The study revealed that the stress response is controlled by a stochastic switch, with key elements including cooperative activation of PKR, ultrasensitive response of SG formation to eIF2 alpha phosphorylation, and negative feedback via GADD34. Furthermore, the study identified GADD34 mRNA levels as a molecular memory of the ISR that plays a central role in cell adaptation to acute and chronic stress.
Article
Biochemistry & Molecular Biology
Philipp Georg, Rosario Astaburuaga-Garcia, Lorenzo Bonaguro, Sophia Brumhard, Laura Michalick, Lena J. Lippert, Tomislav Kostevc, Christiane Gaebel, Maria Schneider, Mathias Streitz, Vadim Demichev, Ioanna Gemuend, Matthias Barone, Pinkus Tober-Lau, Elisa T. Helbig, David Hillus, Lev Petrov, Julia Stein, Hannah-Philine Dey, Daniela Paclik, Christina Iwert, Michael Muelleder, Simran Kaur Aulakh, Sonja Djudjaj, Roman D. Buelow, Henrik E. Mei, Axel R. Schulz, Andreas Thiel, Stefan Hippenstiel, Antoine-Emmanuel Saliba, Roland Eils, Irina Lehmann, Marcus A. Mall, Sebastian Stricker, Jobst Roehmel, Victor M. Corman, Dieter Beule, Emanuel Wyler, Markus Landthaler, Benedikt Obermayer, Saskia von Stillfried, Peter Boor, Munevver Demir, Hans Wesselmann, Norbert Suttorp, Alexander Uhrig, Holger Mueller-Redetzky, Jacob Nattermann, Wolfgang M. Kuebler, Christian Meisel, Markus Ralser, Joachim L. Schultze, Anna C. Aschenbrenner, Charlotte Thibeault, Florian Kurth, Leif E. Sander, Nils Bluethgen, Birgit Sawitzki
Summary: Severe COVID-19 is associated with highly activated CD16(+) T cells that exhibit cytotoxic functions and contribute to endothelial injury. These CD16(+) T cells can degranulate and induce cytotoxicity through immune-complex-mediated mechanisms independent of the T cell receptor, which is not observed in other diseases. The presence of activated CD16(+) T cells and elevated levels of complement proteins upstream of C3a are associated with a fatal outcome of COVID-19, indicating the pathological role of enhanced cytotoxicity and complement activation in the disease.
Article
Medical Informatics
Jakob Steinfeldt, Thore Buergel, Lukas Loock, Paul Kittner, Greg Ruyoga, Julius Upmeier zu Belzen, Simon Sasse, Henrik Strangalies, Lara Christmann, Noah Hollmann, Benedict Wolf, Brian Ference, John Deanfield, Ulf Landmesser, Roland Eils
Summary: In this study, a neural network-based risk model (NeuralCVD) was developed and validated to estimate cardiovascular risk for primary prevention. The model integrates polygenic and clinical predictors and improves risk discrimination compared to established clinical scores and a Cox model. The findings highlight the importance of genetic information in identifying individuals with a high genetic predisposition for preventive interventions.
LANCET DIGITAL HEALTH
(2022)
Article
Biology
Kunal Bhattacharya, Shikha Mahato, Satyendra Deka, Nongmaithem Randhoni Chanu, Amit Kumar Shrivastava, Pukar Khanal
Summary: Chemoresistance, a major challenge in cancer treatment, is associated with the cellular glutathione-related detoxification system. A study has identified GSTP1 enzyme as critical in the inactivation of anticancer drugs and suggests the need for GSTP1 inhibitors to combat chemoresistance. Through molecular docking and simulations, the study found that quercetin 7-O-beta-D-glucoside showed promise as a potential candidate for addressing chemoresistance in cancer patients.
COMPUTATIONAL BIOLOGY AND CHEMISTRY
(2024)
Article
Biology
Manwi Shankar, Majji Sai Sudha Rani, Priyanka Gopi, P. Arsha, Prateek Pandya
Summary: This study investigates the interaction between the food dye BBY and the serum protein BSA. The results show that BBY binds to a specific site on BSA through hydrophobic interactions, affecting the structural stability of the protein. These findings enhance our understanding of the molecular-level interactions between BBY and BSA.
COMPUTATIONAL BIOLOGY AND CHEMISTRY
(2024)
Article
Biology
Chi Zhang, Qian Gao, Ming Li, Tianfei Yu
Summary: In this study, we propose a graph neural network-based autoencoder model, AGraphSAGE, that effectively predicts protein-protein interactions across diverse biological species by integrating gene ontology.
COMPUTATIONAL BIOLOGY AND CHEMISTRY
(2024)
Article
Biology
Kangjie Wu, Liqian Xu, Xinxiang Li, Youhua Zhang, Zhenyu Yue, Yujia Gao, Yiqiong Chen
Summary: Named Entity Recognition (NER) is a crucial task in natural language processing (NLP) and big data analysis, with wide application range. This paper proposes an improved neural network method for NER of rice genes and phenotypes, which can learn semantic information in the context without feature engineering. Experimental results show that the proposed model outperforms other models.
COMPUTATIONAL BIOLOGY AND CHEMISTRY
(2024)
Article
Biology
Suman Hait, Sudip Kundu
Summary: Interactions between amino acids in proteins are crucial for stability and structural integrity. Thermophiles have more and more stable interactions to survive in extreme environments. Different types of interactions are enriched in different structural regions.
COMPUTATIONAL BIOLOGY AND CHEMISTRY
(2024)
Article
Biology
Kountay Dwivedi, Ankit Rajpal, Sheetal Rajpal, Virendra Kumar, Manoj Agarwal, Naveen Kumar
Summary: This study aims to identify biomarkers for non-small cell lung cancer (NSCLC) using copy number variation (CNV) data. A novel deep learning architecture, XL1R-Net, is proposed to improve the classification accuracy for NSCLC subtyping. Twenty NSCLC-relevant biomarkers are uncovered using explainable AI (XAI)-based feature identification. The results show that the identified biomarkers have high classification performance and clinical relevance. Additionally, twelve of the biomarkers are potentially druggable and eighteen of them have a high probability of predicting NSCLC patients' survival likelihood according to the Drug-Gene Interaction Database and the K-M Plotter tool, respectively. This research suggests that investigating these seven novel biomarkers can contribute to NSCLC therapy, and the integration of multiomics data and other sources will help better understand NSCLC heterogeneity.
COMPUTATIONAL BIOLOGY AND CHEMISTRY
(2024)
Article
Biology
Pengli Lu, Wenqi Zhang, Jinkai Wu
Summary: Researchers have developed a computational method, AMPCDA, to predict circRNA-disease associations using predefined metapaths, achieving high predictive accuracy. This method effectively combines node embeddings with higher-order neighborhood representations and provides valuable guidance for revealing new disease mechanisms in biological research.
COMPUTATIONAL BIOLOGY AND CHEMISTRY
(2024)