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Found 6 papers, 1 papers with code
AMPCliff: quantitative definition and benchmarking of activity cliffs in antimicrobial peptides
Our analysis reveals that these models are capable of detecting AMP AC events and the pre-trained protein language ESM2 model demonstrates superior performance across the evaluations.
A method for incremental discovery of financial event types based on anomaly detection
Event datasets in the financial domain are often constructed based on actual application scenarios, and their event types are weakly reusable due to scenario constraints; at the same time, the massive and diverse new financial big data cannot be limited to the event types defined for specific scenarios.
A deep learning method for the long-term prediction of plant electrical signals under salt stress to identify salt tolerance
Furthermore, we developed a quantitative model, named the NaCl stress concentration discrimination model (SCDM), to investigate the relationship between the electrical signals, NaCl stress concentration, and time dependence, and used a salt tolerance classification model (STCM) to discover the most appropriate NaCl stress concentration for distinguishing the salt tolerance of wheat.
Towards Increased Reliability, Transparency and Accessibility in Crosslinking Mass Spectrometry
Crosslinking mass spectrometry (Crosslinking MS) has substantially matured as a method over the last two decades through parallel development in multiple labs, demonstrating its applicability for protein structure determination, conformation analysis and mapping protein interactions in complex mixtures.
Using a one-dimensional convolutional neural network with a conditional generative adversarial network to classify plant electrical signals
This paper proposes a model, based on a one-dimensional convolutional neural network (1D-CNN) with a conditional generative adversarial network (CGAN), which can quickly and effectively identify the salt tolerance of the seedlings using plant electrical signals at the early seedling stage.
e-Distance Weighted Support Vector Regression
We propose a novel support vector regression approach called e-Distance Weighted Support Vector Regression (e-DWSVR). e-DWSVR specifically addresses two challenging issues in support vector regression: first, the process of noisy data; second, how to deal with the situation when the distribution of boundary data is different from that of the overall data.
