An improved reference library and method for accurate cell-type deconvolution of bulk-tissue miRNA data by Shaoying Zhu, Hui Yang, Jun Liu, Qingsheng Fu, Wei Huang, Qi Chen, Andrew E. Teschendorff, Yungang He, Zhen Yang

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A lifetime-enhancing cooperative data gathering and relaying algorithm for cluster-based wireless sensor networks by G Pius Agbulu, G Joselin Retna Kumar, A Vimala Juliet

“…It makes full use of dedicated relay cooperative multi-hop communication with network coding mechanisms to achieve reduced data propagation cost from the various cluster sections to the central base station. …”
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Differences in olfactory bulb mitral cell spiking with ortho- and retronasal stimulation revealed by data-driven models. by Michelle F Craft, Andrea K Barreiro, Shree Hari Gautam, Woodrow L Shew, Cheng Ly

“…These differences may ultimately shape how ortho and retro odors are coded.…”
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Genome-wide data-mining of candidate human splice translational efficiency polymorphisms (STEPs) and an online database. by Christopher A Raistrick, Ian N M Day, Tom R Gaunt

“…Our study found 3324 candidate STEPs lying in motif sequences of 5' non-coding introns and further mining revealed 170 with transcript evidence of intron retention. 21 potential STEPs had EST evidence of intron retention or exon extension, as well as population frequency data for comparison.…”
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AAMS: Application-Aware MCS Selection With Mode Switching for QoE-Driven Large-Scale Media Transmission in 6G Mobile Networks by Somin Park, Minji Choi, Jin-Hyun Ahn, Dong Ho Kim, Cheolwoo You

“…Large-scale media data, such as 3D point cloud videos, have received increasing attention as a key content type for 6G mobile communication services. …”
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From fragmented to functional: Improving rehabilitation data in Georgia's health information systems for better decision-making by Nino Kotrikadze, Akaki Zoidze, George Gotsadze

“…Multiple recording modules lack standardization, with variations in the volume and type of data reported. Key challenges include the absence of unified coding systems, incomplete data capture, and reliance on unstructured formats, hindering data analysis and integration. …”
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Genetic markers for knee osteoarthritis presence are not associated with disease progression - data from the IMI-APPROACH cohort. by Mieke L M Bentvelzen, Paco M J Welsing, Philippe Moingeon, Simon C Mastbergen, Margreet Kloppenburg, Francisco J Blanco, Ida K Haugen, Francis Berenbaum, Hae-Won Uh, Mylène P Jansen, Said El Bouhaddani

“…Ten SNPs are located near protein coding genes PLCL2, CDYL2, and NTNG1, and several SNPs are located in or near long non-coding RNAs (lncRNA).…”
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Mortality from chronic ischaemic heart disease in the Russian Federation: are there enough data for analysis and decision-making? by R. N. Shepel, I. V. Samorodskaya, E. P. Kakorina, O. M. Drapkina

“…The share of unspecified codes and forms of CIHD as the cause of death has decreased. …”
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Insight into public sentiment and demand in China’s public health emergency response: a weibo data analysis by Yanping Wang, Min Wei, Peng Wang, Yiran Gao, Tian Yu, Nan Meng, Huan Liu, Xin Zhang, Kexin Wang, Qunhong Wu

“…Methods The study used Python tools to collect public opinion data from Weibo regarding policy adjustments, social issues, and livelihood concerns. …”
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Сoronary artery disease mortality rates in the Russian Federation and a number of regions: dynamics and structure specifics by D. Sh. Vaisman, E. N. Enina

“…MSOffice Excel 2019 was used for data processing. The correct coding of death causes was determined by expert analysis.Results. …”
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Validation of tRNA-derived fragments as diagnostic biomarkers in suspected acute stroke; limitations in analysis and quantification methods by Tamar Woudenberg, M. Leontien van der Bent, Daphne A.L. van den Homberg, T. Truc My Nguyen, Marieke J.H. Wermer, Ido R. van den Wijngaard, Paul H.A. Quax, A. Yaël Nossent, Nyika D. Kruyt

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Minimax Bayesian Neural Networks by Junping Hong, Ercan Engin Kuruoglu

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Towards efficient and reliable utilization of automated data collection: Media scrapers applied to news on climate change by Erkki Mervaala, Jari Lyytimäki

“…While easing or even removing some of the key problems, such as laborious and time-consuming data collection and potential errors and biases related to subjective coding of materials and distortions caused by focus on small samples, automated methods also bring in new risks such as poor understanding of contexts of the data or non-recognition of underlying systematic errors or missing information. …”
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A Differential Evolutionary-Based XGBoost for Solving Classification of Physical Fitness Test Data of College Students by Baoyue Liang, Weifu Qin, Zuowen Liao

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Consequences and Mechanisms of Noise‐Induced Cochlear Synaptopathy and Hidden Hearing Loss, With Focuses on Signal Perception in Noise and Temporal Processing by Hui Wang, Steven J Aiken, Jian Wang

“…The review also addresses the difficulty of translating animal data to humans and the need for new research in the future.…”
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Evaluating Neural Network Performance in Predicting Disease Status and Tissue Source of JC Polyomavirus from Patient Isolates Based on the Hypervariable Region of the Viral Genome by Aiden M. C. Pike, Saeed Amal, Melissa S. Maginnis, Michael P. Wilczek

“…Mutations in the hypervariable non-coding control region (NCCR) of the JCPyV genome have been linked to disease outcomes and neuropathogenesis, yet few metanalyses document these associations. …”
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QoS-Aware Link Adaptation for Beyond 5G Networks: A Deep Reinforcement Learning Approach by Ali Parsa, Neda Moghim, Sachin Shetty

“…Traditional link adaptation mechanisms primarily aim to maximize throughput and often lack the flexibility to support emerging applications, such as Extended Reality (XR) and Virtual Reality (VR), which demand simultaneous guarantees for high data rates, ultra low latency, and high reliability. …”
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Parallel algorithm for sensitive sequence recognition from long-read genome data with high error rate by Cheng ZHONG, Hui SUN

“…To solve the problem that existing algorithms were difficult to effectively identify sensitive sequences in genomic data for long-read with high error rate, a recognition algorithm using hybrid CPU and GPU parallel computing, called CGPU-F3SR, was proposed.Firstly, the long-read in genomic data were partitioned into multiple short-read, and the Bloom filtering mechanism was used to avoid repeated calculation of the short-read.Secondly, the k-mer coding strategy was used to extract in parallel the error information of all short-read, the recognition accuracy was promoted by improving the sequence similarity calculation model.Finally, CPU and GPU were used to coordinate and parallel to accelerate the calculation of short-read similarity to improve recognition efficiency.As a result, both two types of sensitive sequences including short tandem repeats and disease related sequences could be identified efficiently and accurately from genome data for long-read with high error rate.The experimental results of recognizing sensitive sequences from genomic data for long-read with length 100～400 kbp each show that, compared with existing parallel algorithm, the average recognition accuracy and precision rate of proposed CPU/GPU parallel algorithm CGPU-F3SR are increased by 7.77% and 43.07% respectively, its average false positive rate is reduced by 7.41%, and its average recognition throughput is increased by 2.44 times.…”
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Parallel algorithm for sensitive sequence recognition from long-read genome data with high error rate by Cheng ZHONG, Hui SUN

“…To solve the problem that existing algorithms were difficult to effectively identify sensitive sequences in genomic data for long-read with high error rate, a recognition algorithm using hybrid CPU and GPU parallel computing, called CGPU-F3SR, was proposed.Firstly, the long-read in genomic data were partitioned into multiple short-read, and the Bloom filtering mechanism was used to avoid repeated calculation of the short-read.Secondly, the k-mer coding strategy was used to extract in parallel the error information of all short-read, the recognition accuracy was promoted by improving the sequence similarity calculation model.Finally, CPU and GPU were used to coordinate and parallel to accelerate the calculation of short-read similarity to improve recognition efficiency.As a result, both two types of sensitive sequences including short tandem repeats and disease related sequences could be identified efficiently and accurately from genome data for long-read with high error rate.The experimental results of recognizing sensitive sequences from genomic data for long-read with length 100～400 kbp each show that, compared with existing parallel algorithm, the average recognition accuracy and precision rate of proposed CPU/GPU parallel algorithm CGPU-F3SR are increased by 7.77% and 43.07% respectively, its average false positive rate is reduced by 7.41%, and its average recognition throughput is increased by 2.44 times.…”
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Survival prediction and analysis of drug-resistance genes in HER2-positive breast cancer by Lin Yang, Songhao Chen, Meixue Wang, Shujia Peng, Huadong Zhao, Ping Yang, Guoqiang Bao, Xianli He

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