整洁
发表于 2025-3-25 06:45:55
,The Need of Standardised Metadata to Encode Causal Relationships: Towards Safer Data-Driven Machinees as a crucial step for proper machine learning solutions development, validation, and data sharing. Such practices include detailing the data acquisition process, aiming for automatic integration of causal relationships and actionable metadata.
Mystic
发表于 2025-3-25 11:20:04
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机密
发表于 2025-3-25 12:18:08
Development of Bayesian Network for Multiple Sclerosis Risk Factor Interaction Analysis, prediction, such as using machine learning and other statistical methods. However, many of these methods cannot properly capture complex relationships between variables that affect results of odds ratios unless independence between risk factors is assumed. This work addresses this limitation using
光亮
发表于 2025-3-25 17:33:17
Real-Time Automatic Plankton Detection, Tracking and Classification on Raw Hologram,s been used to detect and count various microscopic objects and has been applied in submersible equipment to monitor the . distribution of plankton. To count and classify plankton, conventional methods require a holographic reconstruction step to decode the hologram before identifying the objects. H
野蛮
发表于 2025-3-25 21:26:37
The First , Model of Leg Movement Activity During Sleep, not showing significant periodic leg movements (PLM). To test a single generator hypothesis behind PLM—a single pacemaker possibly resulting from two (or more) interacting spinal/supraspinal generators—we added a periodic excitatory input to the control model. We describe the onset of a movement in
BRINK
发表于 2025-3-26 02:37:43
Transfer Learning and Magnetic Resonance Imaging Techniques for the Deep Neural Network-Based Diagnm scratch, transfer learning methods have allowed retraining deep networks, which were already trained on massive data repositories, using a smaller dataset from a new application domain, and have demonstrated high performance in several application areas. In the context of a diagnosis of neurodegen
采纳
发表于 2025-3-26 06:47:45
Improving Bacterial sRNA Identification By Combining Genomic Context and Sequence-Derived Features, The large diversity of sRNAs in terms of their length, sequence, and function poses a challenge for computational sRNA prediction. There are several bacterial sRNA prediction tools. Most of them use sequence-derived features or rely on phylogenetic conservation. Recently, a new sRNA predictor (sRNA
BARB
发表于 2025-3-26 08:45:00
High-Dimensional Multi-trait GWAS By Reverse Prediction of Genotypes Using Machine Learning Methodse correlated traits simultaneously, and have higher statistical power than independent univariate analyses of traits. Reverse regression, where genotypes of genetic variants are regressed on multiple traits simultaneously, has emerged as a promising approach to perform multi-trait GWAS in high-dimen
TAP
发表于 2025-3-26 16:28:05
,A Non-Negative Matrix Tri-Factorization Based Method for Predicting Antitumor Drug Sensitivity,t of Non-Negative Matrix Tri-Factorization method, which allows the integration of different data types for the prediction of missing associations. To test our method we retrieved a dataset from the Cancer Cell Line Encyclopedia (CCLE), containing the connections among cell lines and drugs by means
machination
发表于 2025-3-26 17:59:40
A Rule-Based Approach for Generating Synthetic Biological Pathways, However, applying deep learning models to a wider variety of domains is often limited by available labeled data. To address this problem, conventional approaches supplement more samples by augmenting existing datasets. However, these up-sampling methods usually only create derivations of the source