A Model for Building Student Physical Health Information Management in a Big Data Environment by Yan Luo, Zhibin Nie

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Multimodal consumer choice prediction using EEG signals and eye tracking by Syed Muhammad Usman, Shehzad Khalid, Aimen Tanveer, Ali Shariq Imran, Muhammad Zubair

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A hybrid-ABC approach to the multi-controller placement problem of software-defined networks by Bilal Babayigit, Mohammed Abubaker, Banu Ulu

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A biomechanical study of relationship between sternum defect patterns and thoracic respiration by Tomohisa Nagasao, Shogo Kasai, Yusuke Shimizu, Yoshiaki Sakamoto, Asako Hatano, Tadaaki Morotomi, Hisao Ogata, Kazuo Kishi

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MGDrivE 2: A simulation framework for gene drive systems incorporating seasonality and epidemiological dynamics. by Sean L Wu, Jared B Bennett, Héctor M Sánchez C, Andrew J Dolgert, Tomás M León, John M Marshall

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Anatomical-electrical coupling of cardiac axes: Definitions and population variability for advancing personalised ECG interpretation. by Mohammad Kayyali, Ana Mincholé, Shuang Qian, Alistair Young, Devran Ugurlu, Elliot Fairweather, Steven Niederer, John Whitaker, Martin Bishop, Pablo Lamata

“…Five anatomical and four electrical axis definitions were computed, with the optimal pair of definitions selected based on their mutual alignment in 3D space within 28,000 healthy subjects. …”
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Using the antibody-antigen binding interface to train image-based deep neural networks for antibody-epitope classification. by Daniel R Ripoll, Sidhartha Chaudhury, Anders Wallqvist

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G-OnRamp: Generating genome browsers to facilitate undergraduate-driven collaborative genome annotation. by Luke Sargent, Yating Liu, Wilson Leung, Nathan T Mortimer, David Lopatto, Jeremy Goecks, Sarah C R Elgin

“…Despite advances in computational gene prediction algorithms, most eukaryotic genomes still benefit from manual gene annotation. …”
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Behavioral modeling of human choices reveals dissociable effects of physical effort and temporal delay on reward devaluation. by Miriam C Klein-Flügge, Steven W Kennerley, Ana C Saraiva, Will D Penny, Sven Bestmann

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BuDDI: Bulk Deconvolution with Domain Invariance to predict cell-type-specific perturbations from bulk. by Natalie R Davidson, Fan Zhang, Casey S Greene

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CINner: Modeling and simulation of chromosomal instability in cancer at single-cell resolution. by Khanh N Dinh, Ignacio Vázquez-García, Andrew Chan, Rhea Malhotra, Adam Weiner, Andrew W McPherson, Simon Tavaré

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Machine learning in big data: A performance benchmarking study of Flink-ML and Spark MLlib by Messaoud MEZATI, Ines AOURIA

“…This paper provides a comparative analysis of these two frameworks, evaluating their performance, scalability, streaming capabilities, iterative computation efficiency, and ease of integration with external deep learning frameworks. …”
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Transcriptomic profiling in canines and humans reveals cancer specific gene modules and biological mechanisms common to both species. by Gregory J Tawa, John Braisted, David Gerhold, Gurmit Grewal, Christina Mazcko, Matthew Breen, Gurusingham Sittampalam, Amy K LeBlanc

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Low-dimensional spike rate models derived from networks of adaptive integrate-and-fire neurons: Comparison and implementation. by Moritz Augustin, Josef Ladenbauer, Fabian Baumann, Klaus Obermayer

“…The low-dimensional models also well reproduce stable oscillatory spike rate dynamics that are generated either by recurrent synaptic excitation and neuronal adaptation or through delayed inhibitory synaptic feedback. The computational demands of the reduced models are very low but the implementation complexity differs between the different model variants. …”
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Forecasting the Bitcoin price using the various Machine Learning: A systematic review in data-driven marketing by Payam Boozary, Sogand Sheykhan, Hamed GhorbanTanhaei

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Comorbidities in the diseasome are more apparent than real: What Bayesian filtering reveals about the comorbidities of depression. by Peter Marx, Peter Antal, Bence Bolgar, Gyorgy Bagdy, Bill Deakin, Gabriella Juhasz

“…To investigate and filter this effect, we computed and compared pairwise approaches with a systems-based method, which constructs a sparse Bayesian direct multimorbidity map (BDMM) by systematically eliminating disease-mediated comorbidity relations. …”
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Reconstruction of reservoir rock using attention-based convolutional recurrent neural network by Indrajeet Kumar, Anugrah Singh

“…This deep learning method is computationally efficient, fast, and reliable for synthetic image realizations. …”
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Boosting GWAS using biological networks: A study on susceptibility to familial breast cancer. by Héctor Climente-González, Christine Lonjou, Fabienne Lesueur, GENESIS study group, Dominique Stoppa-Lyonnet, Nadine Andrieu, Chloé-Agathe Azencott

“…The most connected gene was CUL3, a regulator of several genes linked to cancer progression. Lastly, we evaluated the biases of each method and the impact of their parameters on the outcome. …”
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ArthroPhase: a novel dataset and method for phase recognition in arthroscopic video by Ali Bahari Malayeri, Matthias Seibold, Nicola A. Cavalcanti, Jonas Hein, Sascha Jecklin, Lazaros Vlachopoulos, Sandro Fucentese, Sandro Hodel, Philipp Fürnstahl

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A burst-dependent algorithm for neuromorphic on-chip learning of spiking neural networks by Michael Stuck, Xingyun Wang, Richard Naud

“…The field of neuromorphic engineering addresses the high energy demands of neural networks through brain-inspired hardware for efficient neural network computing. For on-chip learning with spiking neural networks, neuromorphic hardware requires a local learning algorithm able to solve complex tasks. …”
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