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Biochemical Research Methods
Rachisan Djiake Tihagam, Sanchita Bhatnagar
Summary: Transcriptomic profiling is widely used in cancer research to identify subtypes, predict survival, and find therapeutic targets. Integration of multiple datasets increases sample size and improves statistical power, but also introduces variations that need to be adjusted through normalization. This study used meta-analysis to investigate the expression of TRIM37 across different cancer types.
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Developmental Biology
Nelio T. L. Rodrigues, Tom Bland, Joana Borrego-Pinto, Kangbo Ng, Nisha Hirani, Ying Gu, Sherman Foo, Nathan W. Goehring
Summary: This study presents a method called SAIBR, which can correct for autofluorescence and accurately measure and quantify low-expressed proteins.
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Computer Science, Artificial Intelligence
Qiong Chen, Mengxing Huang, Hao Wang, Guangquan Xu
Summary: Discretization is an important data preprocessing technique in data mining, especially in industrial control. However, traditional discretization methods have shortcomings, particularly in the preprocessing of high-resolution remote sensing big data, where necessary information is lost. This study proposes a discretization method for high-resolution remote sensing big data, which determines the membership degree of each pixel using linear decomposition and a fuzzy rough model, and selects discrete breakpoints using an adaptive genetic algorithm. The method achieves optimal discretization scheme in the shortest time by parallel computing the individual fitness of the population using a MapReduce framework.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
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Computer Science, Interdisciplinary Applications
Ruo Li, Yixiao Lu, Yanli Wang, Haoxuan Xu
Summary: This paper presents a numerical scheme based on Hermite spectral method for solving the multi-species Boltzmann equation. By choosing proper expansion centers and collision models, a balance between computational cost and accuracy is achieved, and high-dimensional problems can be handled effectively.
JOURNAL OF COMPUTATIONAL PHYSICS
(2022)
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Geochemistry & Geophysics
Jifa Guo, Shihong Du
Summary: This article proposes a novel multicenter supervised fuzzy classification method to model spectral diversity in multispectral remote sensing data. By clustering and labeling, it effectively improves classification accuracy and provides better representation for multiple centers of land cover types.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
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Energy & Fuels
Shuangqi Li, Hongwen He, Pengfei Zhao, Shuang Cheng
Summary: This paper proposes a novel integrated battery data cleaning framework to systematically solve data quality problems in cloud-based vehicle battery monitoring and management. Experimental results show that the method is highly effective and can provide an efficient data quality assessment tool for cloud-based vehicle battery management.
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Environmental Sciences
Yi-Chun Lin, Jinyuan Shao, Sang-Yeop Shin, Zainab Saka, Mina Joseph, Raja Manish, Songlin Fei, Ayman Habib
Summary: LiDAR technology is advancing rapidly, providing valuable data for characterizing forest vertical structure. Comparative analysis of point clouds from different LiDAR systems helps select appropriate systems and tools for various research questions.
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Chemistry, Analytical
Nikola Latinovic, Ilija Popadic, Branko Tomic, Aleksandar Simic, Petar Milanovic, Srecko Nijemcevic, Miroslav Peric, Mladen Veinovic
Summary: This paper introduces a hardware and software platform for signal processing in long-range, multi-spectral, electro-optical systems (MSEOS). The platform controls and executes complex signal processing algorithms, such as video stabilization and target detection, by allocating processing tasks to different hardware units.
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Endocrinology & Metabolism
Nils Paulhe, Cecile Canlet, Annelaure Damont, Lindsay Peyriga, Stephanie Durand, Catherine Deborde, Sandra Alves, Stephane Bernillon, Thierry Berton, Raphael Bir, Alyssa Bouville, Edern Cahoreau, Delphine Centeno, Robin Costantino, Laurent Debrauwer, Alexis Delabriere, Christophe Duperier, Sylvain Emery, Amelie Flandin, Ulli Hohenester, Daniel Jacob, Charlotte Joly, Cyril Jousse, Marie Lagree, Nadia Lamari, Marie Lefebvre, Claire Lopez-Piffet, Bernard Lyan, Mickael Maucourt, Carole Migne, Marie-Francoise Olivier, Estelle Rathahao-Paris, Pierre Petriacq, Julie Pinelli, Lea Roch, Pierrick Roger, Simon Roques, Jean-Claude Tabet, Marie Tremblay-Franco, Mounir Traikia, Anna Warnet, Vanessa Zhendre, Dominique Rolin, Fabien Jourdan, Etienne Thevenot, Annick Moing, Emilien Jamin, Francois Fenaille, Christophe Junot, Estelle Pujos-Guillot, Franck Giacomoni
Summary: A comprehensive resource, PeakForest addresses the technical bottleneck in annotating large-scale spectral data and identifying metabolites in metabolomics laboratories. It provides a structured database with tools for data curation and browsing, facilitating the sharing and integration of spectral data across laboratories.
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Computer Science, Information Systems
Giuseppe Psaila, Paolo Fosci
Summary: Internet and mobile technology have enabled the production and dissemination of massive data sets. Analysts require a framework like J-CO Framework to manage and cross-analyze these data sets, allowing them to access remote storage systems and computational resources using a unique query language.
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Multidisciplinary Sciences
Rong Ma, Eric D. D. Sun, James Zou
Summary: Dimension reduction is crucial in modern data science, and this paper introduces a spectral method to evaluate and combine multiple dimension reduction visualizations produced by different algorithms. The proposed method provides a quantitative measure of the relative performance of visualizations and generates a consensus visualization with improved quality.
NATURE COMMUNICATIONS
(2023)
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Geography, Physical
Sudhanshu Shekar Jha, Rama Rao NIdamanuri, Emmett J. Ientilucci
Summary: Target detection involves identifying objects or pixels of interest that are few in numbers and sparsely populated in imagery. This study assesses the quantitative impact of atmospheric parameters on the detectability of engineered targets, showing that spectral knowledge-based targets can be detected using a forward modeling (FM) approach under different atmospheric model scenarios.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
(2022)
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Computer Science, Artificial Intelligence
Martin Papco, Iosu Rodriguez-Martinez, Javier Fumanal-Idocin, Abdulrahman H. Altalhi, Humberto Bustince
Summary: This paper proposes an extension of the deviation-based aggregation function tailored to aggregate multidimensional data, aiming to obtain favorable results in areas with strict temporal constraints such as image processing, deep learning, and decision making.
INFORMATION FUSION
(2021)
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Environmental Sciences
Chaofa Bian, Hongtao Shi, Suqin Wu, Kefei Zhang, Meng Wei, Yindi Zhao, Yaqin Sun, Huifu Zhuang, Xuewei Zhang, Shuo Chen
Summary: Accurate prediction of crop yield is crucial for food security and trade stability. This study developed prediction models for winter wheat yield using machine learning methods and multi-spectral UAV data. The results showed that the GPR model achieved high accuracy in both single and multiple growth stages.
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Biochemical Research Methods
Jianping Zhao, Qi Guan, Chunhou Zheng, Qingqing Cao
Summary: The study introduces an adaptive density-aware spectral clustering method (ADSVAE) that utilizes a variational autoencoder and adaptive density-aware kernel to effectively learn the underlying spatial information, reduce noise, and enhance clustering performance of genomic data. ADSVAE demonstrates good performance on TCGA datasets and outperforms other multi-omics clustering algorithms, highlighting its effectiveness in learning complex data distribution and improving clustering results.
CURRENT BIOINFORMATICS
(2023)
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Chemistry, Analytical
Julian Ollesch, Michael Zaczek, H. Michael Heise, Oliver Theisen, Frederik Grosserueschkamp, Ralf Schmidt, Konrad Morgenroth, Stathis Philippou, Matthias Kemen, Klaus Gerwert
VIBRATIONAL SPECTROSCOPY
(2017)
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Multidisciplinary Sciences
Frederik Grosserueschkamp, Thilo Bracht, Hanna C. Diehl, Claus Kuepper, Maike Ahrens, Angela Kallenbach-Thieltges, Axel Mosig, Martin Eisenacher, Katrin Marcus, Thomas Behrens, Thomas Bruening, Dirk Theegarten, Barbara Sitek, Klaus Gerwert
SCIENTIFIC REPORTS
(2017)
Article
Chemistry, Physical
Yang Li, Yuwei Zhang, Frederik Grosserueschkamp, Sara Stephan, Qiang Cui, Carsten Koetting, Fei Xia, Klaus Gerwert
JOURNAL OF PHYSICAL CHEMISTRY LETTERS
(2018)
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Clinical Neurology
Daniel Niedieker, Frederik Grosserueschkamp, Anja Schreiner, Katalin Barkovits, Carsten Koetting, Katrin Marcus, Klaus Gerwert, Matthias Vorgerd
Article
Multidisciplinary Sciences
Claus Kuepper, Angela Kallenbach-Thieltges, Hendrik Juette, Andrea Tannapfel, Frederik Grosserueschkamp, Klaus Gerwert
SCIENTIFIC REPORTS
(2018)
Article
Pathology
Kathrin E. Witzke, Frederik Grosserueschkamp, Hendrik Juette, Melanie Horn, Florian Roghmann, Nicolas von Landenberg, Thilo Bracht, Angela Kallenbach-Thieltges, Heiko Kaefferlein, Thomas Bruening, Karin Schork, Martin Eisenacher, Katrin Marcus, Joachim Noldus, Andrea Tannapfel, Barbara Sitek, Klaus Gerwert
AMERICAN JOURNAL OF PATHOLOGY
(2019)
Article
Biochemical Research Methods
Johanne H. Solheim, Evgeniy Gunko, Dennis Petersen, Frederik Grosserueschkamp, Klaus Gerwert, Achim Kohler
JOURNAL OF BIOPHOTONICS
(2019)
Article
Biochemical Research Methods
Arne P. Raulf, Joshua Butke, Claus Kuepper, Frederik Grosserueschkamp, Klaus Gerwert, Axel Mosig
Article
Neurosciences
Sarah H. Shahmoradian, Amanda J. Lewis, Christel Genoud, Jurgen Hench, Tim E. Moors, Paula P. Navarro, Daniel Castano-Diez, Gabriel Schweighauser, Alexandra Graff-Meyer, Kenneth N. Godie, Rosmarie Sutterlin, Evelien Huisman, Angela Ingrassia, Yvonne de Gier, Annemieke J. M. Rozemuller, Jing Wang, Anne De Paepe, Johannes Erny, Andreas Staempfli, Joerg Hoernschemeyer, Frederik Grosserueschkamp, Daniel Niedieker, Samir F. El-Mashtoly, Marialuisa Quadri, Wilfred F. J. Van IJcken, Vincenzo Bonifati, Klaus Gerwert, Bernd Bohrmann, Stephan Frank, Markus Britschgi, Henning Stahlberg, Wilma D. J. Van de Berg, Matthias E. Lauer
NATURE NEUROSCIENCE
(2019)
Article
Biochemical Research Methods
Stanislau Trukhan, Valeria Tafintseva, Kristin Tondel, Frederik Grosserueschkamp, Axel Mosig, Vassili Kovalev, Klaus Gerwert, Achim Kohler
JOURNAL OF BIOPHOTONICS
(2020)
Article
Multidisciplinary Sciences
Angela Kallenbach-Thieltges, Frederik Grosserueschkamp, Hendrik Juette, Claus Kuepper, Anke Reinacher-Schick, Andrea Tannapfel, Klaus Gerwert
SCIENTIFIC REPORTS
(2020)
Article
Biochemical Research Methods
Arne P. Raulf, Joshua Butke, Lukas Menzen, Claus Kuepper, Frederik Grosserueschkamp, Klaus Gerwert, Axel Mosig
Summary: This study proposes a method to use deep neural networks to approximate the complex preprocessing function of infrared spectra, removing scattering components. The resulting model is significantly faster and shows strong generalization across different tissue types, while also revealing model uncertainty and band shifts in the amide I region using Bayesian machine learning approaches.
JOURNAL OF BIOPHOTONICS
(2021)
Article
Neurosciences
Dominik Roehr, Baayla D. C. Boon, Martin Schuler, Kristin Kremer, Jeroen J. M. Hoozemans, Femke H. Bouwman, Samir F. El-Mashtoly, Andreas Nabers, Frederik Grosserueschkamp, Annemieke J. M. Rozemuller, Klaus Gerwert
ACTA NEUROPATHOLOGICA COMMUNICATIONS
(2020)
Article
Computer Science, Artificial Intelligence
David Schuhmacher, Stephanie Schorner, Claus Kupper, Frederik Grosserueschkamp, Carlo Sternemann, Celine Lugnier, Anna-Lena Kraeft, Hendrik Jutte, Andrea Tannapfel, Anke Reinacher-Schick, Klaus Gerwert, Axel Mosig
Summary: This paper introduces a hypothesis-based framework for falsifiable explanations of machine learning models, which connects the intermediate space of the model with the data samples to provide falsifiable explanations. The authors instantiate this framework in the field of computational pathology using hyperspectral infrared microscopy, and validate the explanations by histological staining.
MEDICAL IMAGE ANALYSIS
(2022)
Article
Oncology
Klaus Gerwert, Stephanie Schoerner, Frederik Grosserueschkamp, Anna -Lena Kraeft, David Schuhmacher, Carlo Sternemann, Inke S. Feder, Sarah Wisser, Celine Lugnier, Dirk Arnold, Christian Teschendorf, Lothar Mueller, Nina Timmesfeld, Axel Mosig, Anke Reinacher-Schick, Andrea Tannapfel
Summary: This study proposes a novel label-free digital pathology approach using infrared imaging and artificial intelligence to classify microsatellite instability (MSI) vs. microsatellite stability (MSS) in unstained tissue sections. The two-step convolutional neural networks (CNN) algorithm achieves high classification accuracy and rapidity in early colon cancer tissue samples.
EUROPEAN JOURNAL OF CANCER
(2023)
