On Multilabel Classification Methods of Incompletely Labeled Biomedical Text Data

Multilabel classification is often hindered by incompletely labeled training datasets; for some items of such dataset (or even for all of them) some labels may be omitted. In this case, we cannot know if any item is labeled fully and correctly. When we train a classifier directly on incompletely labeled dataset, it performs ineffectively. To overcome the problem, we added an extra step, training set modification, before training a classifier. In this paper, we try two algorithms for training set modification: weighted k-nearest neighbor (WkNN) and soft supervised learning (SoftSL). Both of these approaches are based on similarity measurements between data vectors. We performed the experiments on AgingPortfolio (text dataset) and then rechecked on the Yeast (nontext genetic data). We tried SVM and RF classifiers for the original datasets and then for the modified ones. For each dataset, our experiments demonstrated that both classification algorithms performed considerably better when preceded by the training set modification step

Controlled release from a mechanically-stimulated thermosensitive self-heating composite hydrogel

Temperature has been extensively explored as a trigger to control the delivery of a payload from environment-sensitive polymers. The need for an external heat source only allows limited spatiotem- poral control over the delivery process. We propose a new approach by using the dissipative properties of a hydrogel matrix as an internal heat source when the material is mechanically loaded. The system is comprised of a highly dissipative hydrogel matrix and thermo-sensitive nanoparticles that shrink upon an increase in temperature. Exposing the hydrogel to a cyclic mechanical loading for a period of 5 min leads to an increase of temperature of the nanoparticles. The concomitant decrease in the volume of the nanoparticles increases the permeability of the hydrogel network facilitating the release of its payload. As a proof-of-concept, we showed that the payload of the hydrogel is released after 5e8 min following the initiation of the mechanical loading. This delivery method would be particularly suited for the release of growth factor as it has been shown that cell receptor to growth factor is activated 5e20 min following a mechanical loading

On Multilabel Classification Methods of Incompletely Labeled Biomedical Text Data

Multilabel classification is often hindered by incompletely labeled training datasets; for some items of such dataset (or even for all of them) some labels may be omitted. In this case, we cannot know if any item is labeled fully and correctly. When we train a classifier directly on incompletely labeled dataset, it performs ineffectively. To overcome the problem, we added an extra step, training set modification, before training a classifier. In this paper, we try two algorithms for training set modification: weighted k-nearest neighbor (WkNN) and soft supervised learning (SoftSL). Both of these approaches are based on similarity measurements between data vectors. We performed the experiments on AgingPortfolio (text dataset) and then rechecked on the Yeast (nontext genetic data). We tried SVM and RF classifiers for the original datasets and then for the modified ones. For each dataset, our experiments demonstrated that both classification algorithms performed considerably better when preceded by the training set modification step