Association of leukocyte DNA methylation changes with dietary folate and alcohol intake in the EPIC Study by Perrier, F, Viallon, V, Ambatipudi, A, Cuenin, C, Hernandez-Vargas, H, Chajes, V, Baglietto, L, Matejcic, M, Moreno-Macias, H, Kuhn, T, Boeing, H, Karakatsani, A, Kotanidou, A, Trichopoulou, A, Sieri, S, Panico, S, Fasanelli, F, Dolle, M, Onland-Moret, C, Sluijs, I, Weiderpass, E, Quiros, JR, Agudo, A, Huerta, JM, Ardanaz, E, Dorronsoro, M, Tong, Y, Tsilidis, K, Riboli, E, Gunter, MJ, Herceg, Z, Ferrari, P, Romieu, I

“…The DMR analysis combined results from the feature-specific analysis for a specific chromosome and using distances between features as weights whereas FL regression combined two penalties to encourage sparsity of single features and the difference between two consecutive features.…”

Large-scale learning of discriminative image representations by Simonyan, K

“…The convexity of the corresponding optimisation problems is achieved by using convex, sparsity-inducing regularisers: the L1 norm and the nuclear (trace) norm. …”

Towards understanding genome regulation via high-resolution analysis of chromatin accessibility by Dai, L

“…Despite its advantages, the analysis of ATAC-seq is challenging and due to the data sparsity and the sub-optimal use of the data, especially the fragment size of the sequencing reads. …”

Power-law phenomena in Bayesian nonparametrics by Panero, F

“…The paper presented in chapter 3 casts light on the asymptotic properties of networks generated under the graphex process, proving the desirable properties of sparsity, power-law degree distributions, clustering and two central limit theorems.…”

Adaptive power control aware depth routing in underwater sensor networks by Ghufran Ahmed, Saiful Islam, Ihsan Ali, Isra Adil Hayder, Abdelmuttlib Ibrahim Abdalla Ahmed, Muhammad Talha, Sultan S. Alshamrani, Ag. Asri Ag. Ibrahim

“…Considering the facts mentioned above, this research presents a controlled transmission power-based sparsity-aware energy-efficient clustering in UWSNs. …”
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Adaptive channel estimation for sparse ultra wideband systems by Nunoo, Solomon

“…In addition, CS-based LMS and NLMS algorithms do not consider the use of the channel sparsity to control the algorithm performance. Therefore, this thesis also proposes a number of Sparseness-Controlled (SC) LMS and NLMS algorithms for estimating sparse UWB channels. …”
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Improved collaborative filtering using clustering and association rule mining on implicit data by Najafabadi, Maryam Khanian

“…To investigate users’ activities that influence the recommender system developed based on the CF technique, a critical observation on the public recommendation datasets has been carried out. To overcome data sparsity problem, this research applies users’ implicit interaction records with items to efficiently process massive data by employing association rules mining (Apriori algorithm). …”
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Preparing glycomics data for robust statistical analysis with GlyCompareCT by Yujie Zhang, Sridevi Krishnan, Bokan Bao, Austin W.T. Chiang, James T. Sorrentino, Song-Min Schinn, Benjamin P. Kellman, Nathan E. Lewis

“…Summary: GlyCompareCT is a portable command-line tool to facilitate downstream glycomic data analyses, by addressing data inherent sparsity and non-independence. Inputting glycan abundances, users can run GlyCompareCT with one line of code to obtain the abundances of a minimal substructure set, named glycomotif, thereby quantifying hidden biosynthetic relationships between measured glycans. …”
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A Tracking Imaging Control Method for Dual-FSM 3D GISC LiDAR by Yu Cao, Xiuqin Su, Xueming Qian, Haitao Wang, Wei Hao, Meilin Xie, Xubin Feng, Junfeng Han, Mingliang Chen, Chenglong Wang

“…It is applied to the target-tracking accuracy control in a 3D GISC LiDAR (three-dimensional ghost imaging LiDAR via sparsity constraint) system. The tracking and pointing imaging control system of the dual-FSM 3D GISC LiDAR proposed in this paper is a staring imaging method with multiple measurements, which mainly solves the problem of high-resolution remote-sensing imaging of high-speed moving targets when the technology is transformed into practical applications. …”
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Prediction of Tuberculosis From Lung Tissue Images of Diversity Outbred Mice Using Jump Knowledge Based Cell Graph Neural Network by Vasundhara Acharya, Diana Choi, Bulent Yener, Gillian Beamer

“…This method differs from the strategies in related cell graph-based works that rely on edge thresholds based on sparsity/density in cell graph construction, emphasizing a biologically informed threshold determination instead. …”
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Towards integration of artificial intelligence into medical devices as a real-time recommender system for personalised healthcare: State-of-the-art and future prospects by Talha Iqbal, Mehedi Masud, Bilal Amin, Conor Feely, Mary Faherty, Tim Jones, Michelle Tierney, Atif Shahzad, Patricia Vazquez

“…Implementing AI as a recommender system improves this prediction accuracy and solves cold start and data sparsity problems. However, most of the methods and algorithms are tested in a simulated setting which cannot recapitulate the influencing factors of the real world. …”
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Autoencoder Neural Network-Based STAP Algorithm for Airborne Radar with Inadequate Training Samples by Jing Liu, Guisheng Liao, Jingwei Xu, Shengqi Zhu, Filbert H. Juwono, Cao Zeng

“…Then, an autoencoder neural network with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>l</mi><mn>2</mn></msub></semantics></math></inline-formula> regularization and the sparsity regularization is proposed for the unique matrix to be decoded and encoded. …”
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Clinical Uses and Short-Term Safety Profile of Ethiodized Poppy Seed Oil Contrast Agent in the Diagnosis and Treatment of Vascular Anomalies and Tumors by Robert K. Clemens, Tim Sebastian, Cindy Kerr, Ahmad I. Alomari

“…Background: There is a sparsity of data on the use of ethiodized poppy seed oil (EPO) contrast agent (Lipiodol) in patients. …”
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A microbial causal mediation analytic tool for health disparity and applications in body mass index by Chan Wang, Jiyoung Ahn, Thaddeus Tarpey, Stella S. Yi, Richard B. Hayes, Huilin Li

“…However, no analytic framework can be directly used to analyze microbiome as a mediator between health disparity and clinical outcome, due to the non-manipulable nature of the exposure and the unique structure of microbiome data, including high dimensionality, sparsity, and compositionality. Methods Considering the modifiable and quantitative features of the microbiome, we propose a microbial causal mediation model framework, SparseMCMM_HD, to uncover the mediating role of microbiome in health disparities, by depicting a plausible path from a non-manipulable exposure (e.g., ethnicity or region) to the outcome through the microbiome. …”
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Fault diagnosis method for mine hoisting motor based on VMD and CNN-BiLSTM by LI Jingzhao, HE Na, ZHANG Jinwei, WANG Qing, LI Huashun

“…The experimental results show the following points. ① Each IMF component of VMD decomposition has an independent center frequency and uniform distribution, and exhibits sparsity in the frequency domain. It can effectively avoid modal aliasing problems. …”
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Review on Compressive Sensing Algorithms for ECG Signal for IoT Based Deep Learning Framework by Subramanyam Shashi Kumar, Prakash Ramachandran

“…Compressive Sensing (CS) has recently attracted more interest due to its compactness and its feature of the faithful reconstruction of signals from fewer linear measurements, which facilitates less than Shannon’s sampling rate by exploiting the signal sparsity. The most common biomedical signal that is to be analyzed is the ECG signal, as the prediction of heart failure at an early stage can save a human life. …”
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The performance of deep generative models for learning joint embeddings of single-cell multi-omics data by Eva Brombacher, Eva Brombacher, Eva Brombacher, Eva Brombacher, Eva Brombacher, Maren Hackenberg, Maren Hackenberg, Clemens Kreutz, Clemens Kreutz, Clemens Kreutz, Harald Binder, Harald Binder, Martin Treppner, Martin Treppner

“…For example, the challenge of sparsity in single-omics data might be partly resolved by compensating for missing information across modalities. …”
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Patient Representation From Structured Electronic Medical Records Based on Embedding Technique: Development and Validation Study by Yanqun Huang, Ni Wang, Zhiqiang Zhang, Honglei Liu, Xiaolu Fei, Lan Wei, Hui Chen

“… BackgroundThe secondary use of structured electronic medical record (sEMR) data has become a challenge due to the diversity, sparsity, and high dimensionality of the data representation. …”
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Whole genome sequencing unravels cryptic circulation of divergent dengue virus lineages in the rainforest region of Nigeria by Bernard Anyebe Onoja, Judith Uche Oguzie, Uwem Etop George, Kaego Emmanuel Asoh, Philip Ajayi, Toluwanimi Faithful Omofaye, Imafidon Oluwatoyin Igeleke, Philomena Eromon, Soumare Harouna, Edyth Parker, Adekunle Johnson Adeniji, Christian T. Happi

“…ABSTRACTDengue is often misclassified and underreported in Africa due to inaccurate differential diagnoses of nonspecific febrile illnesses such as malaria, sparsity of diagnostic testing and poor clinical and genomic surveillance. …”
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Improved Frequency Domain Turbo Equalization with Expectation Propagation Interference Cancellation in Underwater Acoustic Communications by Bin Jiang, Yue Tang, Yinan Zhao, Jianrong Bao, Chao Liu, Xianghong Tang

“…A selective zero-attracting (SZA) improved proportionate normal least mean square (SZA-IPNLMS) algorithm is adopted by utilizing the sparsity of the UWAC channel to estimate it using a training sequence. …”
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