Article
Biochemical Research Methods
Shun Liu, Jianchao Zhou, Ziyan Feng, Jiawen Zhang, Shuang Li, Zilong Jin, Chenfei Zhang, Shiliang Li, Gaoqi He, Honglin Li
Summary: In this study, a novel virtual screening tool called VRPharmer is proposed that enables users to perform the entire screening process in virtual reality environments. It provides both interactive and typical screening modes, editable pharmacophore models, and improved molecular rendering algorithms for precise representations.
Article
Biochemical Research Methods
Chakkarai Sathyaseelan, Murali Aadhitya Magateshvaren Saras, L. Ponoop Prasad Patro, Patil Pranita Uttamrao, Thenmalarchelvi Rathinavelan
Summary: SARS-CoV-2 has undergone significant genomic mutations over the past 3 years, resulting in the periodic emergence of multiple variants. Some of these variants show enhanced fitness advantage, transmissibility, and pathogenicity, and can also reduce vaccine efficacy. Therefore, it is crucial to track viral evolution to prevent and protect against SARS-CoV-2 infection. CoVe-tracker, an interactive web-GUI platform, has been developed to monitor the evolutionary dynamics of the virus's pan proteome. It provides country-wise and protein-wise amino acid mutations (currently 44,139) of SARS-CoV-2, as well as their month-wise distribution. CoVe-tracker also offers information on position-wise evolution in the SARS-CoV-2 proteome, and provides month-and country-wise distributions of 2,065 phylogenetic assignment of named global outbreak (PANGO) lineages and their 177,564 variants.
JOURNAL OF PROTEOME RESEARCH
(2023)
Article
Computer Science, Cybernetics
Ruixuan Sun, Avinash Akella, Ruoyan Kong, Moyan Zhou, Joseph A. Konstan
Summary: Recommender systems need to strike a balance between matching users' tastes and providing unexpected recommendations. User opinions on the breadth of recommendations vary.
INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION
(2023)
Article
Biochemical Research Methods
Johanna Zoppi, Jean-Francois Guillaume, Michel Neunlist, Samuel Chaffron
Summary: MiBiOmics is a web-based application that facilitates multi-omics data visualization, exploration, integration, and analysis through interactive protocols. It helps mine complex biological systems and identify robust biomarkers linked to specific contextual parameters or biological states.
BMC BIOINFORMATICS
(2021)
Article
Computer Science, Hardware & Architecture
Enhui Huang, Yanlei Diao, Anna Liu, Liping Peng, Luciano Di Palma
Summary: There is an increasing gap between the rapid growth of data and human ability to comprehend it, leading to a demand for data management tools. This work proposes an interactive data exploration system using the explore-by-example approach to bridge this gap. The system addresses the limitations of traditional active learning techniques and achieves superior accuracy and efficiency in data exploration.
Article
Computer Science, Artificial Intelligence
Muyang Li, Ji Lin, Yaoyao Ding, Zhijian Liu, Jun-Yan Zhu, Song Han
Summary: This paper proposes a general compression framework for reducing the inference time and model size of the generator in cGANs. By transferring knowledge of multiple intermediate representations and using neural architecture search, the method is effective across different settings, enabling interactive image synthesis.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Geography
Marynia Kolak, Xun Li, Qinyun Lin, Ryan Wang, Moksha Menghaney, Stephanie Yang, Vidal Anguiano
Summary: Utilizing distributed spatial infrastructures with cloud computing technologies, a new, open source spatial middleware component has been implemented to address the data-driven knowledge discovery and spatial analysis needs central to the COVID-19 pandemic. By incorporating multiple data streams and perspectives, explorative spatial data analysis and statistical hotspot standards have been integrated to detect infectious disease clusters in real time. Additionally, principles of user-centered design have been incorporated in the direction and design of Atlas application development through engagement with a research coalition.
TRANSACTIONS IN GIS
(2021)
Article
Chemistry, Multidisciplinary
Haneum Lee, Cheonghwan Hur, Bunyodbek Ibrokhimov, Sanggil Kang
Summary: In the era of big data, feature engineering has proven to be efficient and important in reducing dimensionality and extracting useful information from original features. It can be expressed as dimensionality reduction and is divided into feature selection and feature extraction. Sparse autoencoder (SAE) is a representative deep feature learning method that combines feature selection and feature extraction. However, existing SAEs do not consider feature importance during training, leading to the extraction of irrelevant information. In this paper, we propose an interactive guiding sparse autoencoder (IGSAE) that utilizes interactive guiding layers and sparsity constraints to guide the information extraction process. Experiments show that IGSAE outperforms other dimensionality reduction methods in terms of classification performance.
APPLIED SCIENCES-BASEL
(2023)
Article
Multidisciplinary Sciences
Haim Bar, Seojin Bang
Summary: This study introduces a method to recover gene network structure from co-expression data, utilizes a mixture model to detect edges and control false discovery rate. Inference of gene network structure using a large dataset revealed characteristics of different cancer subtypes and enriched pathways.
Review
Computer Science, Information Systems
Jiqun Liu
Summary: This article provides an overview of 76 task-based interactive IR studies published between 2000 and 2020, with researchers focusing on multiple dimensions of search tasks and analyzing the associations between implicit task dimensions and observable predictors. The results of this review can facilitate knowledge growth in the IIR community and serve as the basis for future research on new modalities of user-task interactions.
INFORMATION PROCESSING & MANAGEMENT
(2021)
Article
Computer Science, Information Systems
Yixin Chen, Dongsheng Li, Yu Hua, Wenbo He
Summary: This research focuses on establishing an effective and efficient content-based redundancy detection system, which achieves both high detection accuracy and speed through innovative design concepts and methods.
IEEE TRANSACTIONS ON BIG DATA
(2021)
Article
Computer Science, Hardware & Architecture
Hathem Khelil, Mahmoud Brahimi
Summary: The composition of Web services poses a critical challenge in business process development. This study presents the DBTLBO algorithm, an enhanced version of Balanced Teaching-Learning-Based optimization algorithm, to address the time-consuming problem of Web service selection. The algorithm supports the discrete nature of the problem and demonstrates its effectiveness compared to other algorithms.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Physics, Multidisciplinary
Jedidiah Yanez-Sierra, Arturo Diaz-Perez, Victor Sosa-Sosa
Summary: The article introduces a simple yet effective method for identifying spreaders in graph analysis, utilizing the underlying graph topology to ensure that selected nodes are both relevant and well-distributed. Experimental results demonstrate that the proposed method outperforms several reference algorithms in terms of propagation influence and node scattering, and combining it with classical metrics can further improve results.
Article
Management
Fan Liu, Huchang Liao, Abdullah Al-Barakati
Summary: This study proposes a decision-making model that integrates machine learning and multi-criteria decision-making methods to assist patients in selecting doctors based on user-generated content. The research constructs a physician evaluation system using data mining methods, mines satisfaction information from reviews using sentiment analysis, and considers patients' risk preferences and the interactions among criteria.
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE
(2023)
Article
Computer Science, Hardware & Architecture
Ourania Spantidi, Georgios Zervakis, Iraklis Anagnostopoulos, Joerg Henkel
Summary: This study proposes an automated framework for weight-to-approximation mapping through formal property exploration to improve efficiency of DNN accelerators. Experimental results show that at the MAC unit level, it achieved higher energy gains compared to existing mappings.
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
(2022)
Article
Oncology
Yuan Xie, Anders Sundstrom, Naga P. Maturi, E-Jean Tan, Voichita D. Marinescu, Malin Jarvius, Malin Tirfing, Chuan Fin, Lei Chen, Magnus Essand, Fredrik J. Swartling, Sven Nelander, Yiwen Jiang, Lene Uhrbom
JOURNAL OF PATHOLOGY
(2019)
Article
Biochemical Research Methods
Holger Weishaupt, Patrik Johansson, Anders Sundstrom, Zelmina Lubovac-Pilav, Bjoern Olsson, Sven Nelander, Fredrik J. Swartling
Article
Neurosciences
Alessandro Mega, Mette Hartmark Nilsen, Lina Wik Leiss, Nicholas P. Tobin, Hrvoje Miletic, Linda Sleire, Carina Strell, Sven Nelander, Cecilia Krona, Daniel Hagerstrand, Per O. Enger, Monica Nister, Arne Ostman
Article
Oncology
Ioannis Kaffes, Frank Szulzewsky, Zhihong Chen, Cameron J. Herting, Ben Gabanic, Jose E. Velazquez Vega, Jennifer Shelton, Jeffrey M. Switchenko, James L. Ross, Leon F. McSwain, Jason T. Huse, Bengt Westermark, Sven Nelander, Karin Forsberg-Nilsson, Lene Uhrbom, Naga Prathyusha Maturi, Patrick J. Cimino, Eric C. Holland, Helmut Kettenmann, Cameron W. Brennan, Daniel J. Brat, Dolores Hambardzumyan
Article
Cell Biology
Matko Cancer, Lisa F. Drews, Johan Bengtsson, Sara Bolin, Gabriela Rosen, Bengt Westermark, Sven Nelander, Karin Forsberg-Nilsson, Lene Uhrbom, Holger Weishaupt, Fredrik J. Swartling
CELL DEATH & DISEASE
(2019)
Article
Multidisciplinary Sciences
Elin Almstedt, Ramy Elgendy, Neda Hekmati, Emil Rosen, Caroline Warn, Thale Kristin Olsen, Cecilia Dyberg, Milena Doroszko, Ida Larsson, Anders Sundstrom, Marie Arsenian Henriksson, Sven Pahlman, Daniel Bexell, Michael Vanlandewijck, Per Kogner, Rebecka Jornsten, Cecilia Krona, Sven Nelander
NATURE COMMUNICATIONS
(2020)
Article
Oncology
Erika Dalmo, Patrik Johansson, Mia Niklasson, Ida Gustavsson, Sven Nelander, Bengt Westermark
MOLECULAR CANCER RESEARCH
(2020)
Article
Oncology
Helena Johard, Anna Omelyanenko, Gao Fei, Misha Zilberter, Zankruti Dave, Randa Abu-Youssef, Linnea Schmidt, Aditya Harisankar, C. Theresa Vincent, Julian Walfridsson, Sven Nelander, Tibor Harkany, Klas Blomgren, Michael Andang
MOLECULAR CANCER RESEARCH
(2020)
Article
Cell Biology
Patrik Johansson, Cecilia Krona, Soumi Kundu, Milena Doroszko, Sathishkumar Baskaran, Linnea Schmidt, Claire Vinel, Elin Almstedt, Ramy Elgendy, Ludmila Elfineh, Caroline Gallant, Sara Lundsten, Fernando J. Ferrer Gago, Aleksi Hakkarainen, Petra Sipila, Maria Haggblad, Ulf Martens, Bo Lundgren, Melanie M. Frigault, David P. Lane, Fredrik J. Swartling, Lene Uhrbom, Marika Nestor, Silvia Marino, Sven Nelander
Article
Biochemistry & Molecular Biology
Ida Larsson, Erika Dalmo, Ramy Elgendy, Mia Niklasson, Milena Doroszko, Anna Segerman, Rebecka Jornsten, Bengt Westermark, Sven Nelander
Summary: The research establishes quantitative models of time-dependent transcriptional variation of patient-derived glioblastoma cells, revealing a hierarchical and plastic organization of GBM with cell state switching rates and patterns being partly patient-specific. Therapeutic interventions have complex dynamic effects, including inhibiting specific states and altering differentiation.
MOLECULAR SYSTEMS BIOLOGY
(2021)
Article
Biology
Carolina Marques, Thomas Unterkircher, Paula Kroon, Barbara Oldrini, Annalisa Izzo, Yuliia Dramaretska, Roberto Ferrarese, Eva Kling, Oliver Schnell, Sven Nelander, Erwin F. Wagner, Latifa Bakiri, Gaetano Gargiulo, Maria Stella Carro, Massimo Squatrito
Summary: The study identified FOSL1 as a key regulator of the mesenchymal subtype in GBM, with NF1 gene playing a role in GBM mesenchymal transformation through modulation of FOSL1 expression. Depletion of FOSL1 in NF1-mutant human BTSCs and Kras-mutant mouse neural stem cells results in loss of the mesenchymal gene signature.
Article
Biochemical Research Methods
Philip Gerlee, Philipp M. Altrock, Adam Malik, Cecilia Krona, Sven Nelander
Summary: This article studies the Allee effect in cancer using an individual-based model. The study finds that autocrine signaling plays a crucial role in the generation of the Allee effect, where low cell densities can lead to reduced or negative growth rate. The results of the model are consistent with experimental observations and have important implications for understanding and treating cancer dynamics.
PLOS COMPUTATIONAL BIOLOGY
(2022)
Article
Biology
Emil Rosen, Hitesh Bhagavanbhai Mangukiya, Ludmila Elfineh, Rebecka Stockgard, Cecilia Krona, Philip Gerlee, Sven Nelander
Summary: Cell migration is an important factor in tumor invasion, and finding drugs that can inhibit this process is crucial. However, there is a lack of scalable screening methods for identifying anti-migratory drugs.
COMMUNICATIONS BIOLOGY
(2023)
Article
Mathematical & Computational Biology
Deniz Secilmis, Thomas Hillerton, Daniel Morgan, Andreas Tjarnberg, Sven Nelander, Torbjorn E. M. Nordling, Erik L. L. Sonnhammer
NPJ SYSTEMS BIOLOGY AND APPLICATIONS
(2020)
Article
Oncology
Sven Nelander
MOLECULAR & CELLULAR ONCOLOGY
(2019)