SubRF_Seq: Identification of Sub-Golgi Protein Types with Random Forest with Partial Sequence Information

Abstract

In the recent years, the subject of Golgi classification has been studied intensively. It has been scientifically proven that Golgi can synthesize many substances, such as polysaccharides, and it can also combine proteins with sugars or lipids with glycoproteins and lipoproteins. In some cells (such as liver cells), the Golgi apparatus is also involved in the synthesis and secretion of lipoproteins. Therefore, the loss of Golgi protein function may have severe effects on the human body. For example, Alzheimer’s disease and diabetes are related to the loss of Golgi protein function. Because the classification of Golgi proteins has a specific effect on the treatment of these diseases, many scholars have studied the classification of Golgi proteins, but the data sets they used were complete Golgi sequences. The focus of this article is whether there is redundancy in the Golgi protein classification or, in other words, whether a part of the entire Golgi protein sequence can be used to complete the Golgi protein classification. Besides, we have adopted a new method to deal with the problem of sample imbalance. After experiments, our model has certain observability.

Automatic 3D Pollen Recognition Based on Convolutional Neural Network

“…Confocal-E dataset is a classic 3D pollen dataset that includes 5360 pollen grains from 27 different categories of pollen images collected by confocal laser scanning microscopy in Germany [13]. e pollen images, including Secale, Poaceae, and Fagus, are divided into three groups by sensitization, namely, highly allergenic, moderate allergenic, and lowly allergenic [26][27][28][29][30][31]. e dataset is augmented by taking different transformations, especially rotation transform, in order to validate the geometric invariance of the proposed method, which aims at increasing the volume of labeled training sets by applying transformations while preserving their class labels.…”

Discrimination of Golgi Proteins Through Efficient Exploitation of Hybrid Feature Spaces Coupled With SMOTE and Ensemble of Support Vector Machine

“…MCC produces its output in the range of −1 and +1 where the former is returned for inverse predictions and the later is for perfect predictions whereas 0 is returned for average random predictions. MCC is calculated using (11).…”

“…Here, precision [42] is the number of true positives divided by the sum of true positives and false positives predicted by the classifier whereas recall [11] is the number of true positives divided by the sum of all positives actually present in the positive class.…”

“…The high-ranked features have been exploited by kNN classifier for their discriminative power that achieved 95.85% performance accuracy with 10-fold cross-validation. Cui et al [11] have come up with a new idea of using part of the Golgi protein sequence instead of using the complete sequence. They used Enhanced Amino Acid Content Encoding [12] to encode the Golgi protein sequences in parts.…”