AIMC Topic: Data Interpretation, Statistical
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Using a Secure, Continually Updating, Web Source Processing Pipeline to Support the Real-Time Data Synthesis and Analysis of Scientific Literature: Development and Validation Study.
Journal of medical Internet research
May 6, 2021
BACKGROUND: The scale and quality of the global scientific response to the COVID-19 pandemic have unquestionably saved lives. However, the COVID-19 pandemic has also triggered an unprecedented "infodemic"; the velocity and volume of data production h...
Utilizing Artificial Intelligence to Manage COVID-19 Scientific Evidence Torrent with Risklick AI: A Critical Tool for Pharmacology and Therapy Development.
Pharmacology
Apr 28, 2021
INTRODUCTION: The SARS-CoV-2 pandemic has led to one of the most critical and boundless waves of publications in the history of modern science. The necessity to find and pursue relevant information and quantify its quality is broadly acknowledged. Mo...
Unbiased Recursive Partitioning Enables Robust and Reliable Outcome Prediction in Acute Spinal Cord Injury.
Journal of neurotrauma
Apr 7, 2021
Neurological disorders usually present very heterogeneous recovery patterns. Nonetheless, accurate prediction of future clinical end-points and robust definition of homogeneous cohorts are necessary for scientific investigation and targeted care. For...
Universal probabilistic programming offers a powerful approach to statistical phylogenetics.
Communications biology
Feb 24, 2021
Statistical phylogenetic analysis currently relies on complex, dedicated software packages, making it difficult for evolutionary biologists to explore new models and inference strategies. Recent years have seen more generic solutions based on probabi...
Explanation and prediction of clinical data with imbalanced class distribution based on pattern discovery and disentanglement.
BMC medical informatics and decision making
Jan 9, 2021
BACKGROUND: Statistical data analysis, especially the advanced machine learning (ML) methods, have attracted considerable interest in clinical practices. We are looking for interpretability of the diagnostic/prognostic results that will bring confide...
Continual Multiview Task Learning via Deep Matrix Factorization.
IEEE transactions on neural networks and learning systems
Jan 4, 2021
The state-of-the-art multitask multiview (MTMV) learning tackles a scenario where multiple tasks are related to each other via multiple shared feature views. However, in many real-world scenarios where a sequence of the multiview task comes, the high...
Quantifying influence of human choice on the automated detection of Drosophila behavior by a supervised machine learning algorithm.
PloS one
Dec 16, 2020
Automated quantification of behavior is increasingly prevalent in neuroscience research. Human judgments can influence machine-learning-based behavior classification at multiple steps in the process, for both supervised and unsupervised approaches. S...
Circular Complex-Valued GMDH-Type Neural Network for Real-Valued Classification Problems.
IEEE transactions on neural networks and learning systems
Nov 30, 2020
Recently, applications of complex-valued neural networks (CVNNs) to real-valued classification problems have attracted significant attention. However, most existing CVNNs are black-box models with poor explanation performance. This study extends the ...
DACH: Domain Adaptation Without Domain Information.
IEEE transactions on neural networks and learning systems
Nov 30, 2020
Domain adaptation is becoming increasingly important for learning systems in recent years, especially with the growing diversification of data domains in real-world applications, such as the genetic data from various sequencing platforms and video fe...
DNN-assisted statistical analysis of a model of local cortical circuits.
Scientific reports
Nov 18, 2020
In neuroscience, computational modeling is an effective way to gain insight into cortical mechanisms, yet the construction and analysis of large-scale network models-not to mention the extraction of underlying principles-are themselves challenging ta...
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