With the advent of the era of Industry 4.0 and the continuous development of point cloud data acquisition technology, point cloud data has been widely used in unmanned distribution of intelligent logistics. This paper designs a 3D point cloud classification model with coordinate attention, blueprint separation involution neural network (BICANet). Firstly, the combination of 2D features and 3D features is adopted to maintain the spatial structure of the point cloud. Secondly, the Involution network is introduced to reduce the amount of redundant data for neural network computation and improve the whole network computation efficiency. At the same time, to further enhance the network feature learning capability, the blueprint separation convolution is combined with coordinate attention. The experimental results prove that the overall accuracy of BICANet in Vaihingen and GML B datasets reaches 86.0% and 98.8%, respectively. It is highly competitive with the currently available methods.

MOTIVATION: Alternative splicing creates the considerable proteomic diversity and complexity on relatively limited genome. Proteoforms translated from alternatively spliced isoforms of a gene actually execute the biological functions of this gene, which reflect the functional knowledge of genes at a finer granular level. Recently, some computational approaches have been proposed to differentiate isoform functions using sequence and expression data. However, their performance is far from being desirable, mainly due to the imbalance and lack of annotations at isoform-level, and the difficulty of modeling gene-isoform relations. RESULT: We propose a deep multi-instance learning-based framework (DMIL-IsoFun) to differentiate the functions of isoforms. DMIL-IsoFun firstly introduces a multi-instance learning convolution neural network trained with isoform sequences and gene-level annotations to extract the feature vectors and initialize the annotations of isoforms, and then uses a class-imbalance Graph Convolution Network to refine the annotations of individual isoforms based on the isoform co-expression network and extracted features. Extensive experimental results show that DMIL-IsoFun improves the Smin and Fmax of state-of-the-art solutions by at least 29.6% and 40.8%. The effectiveness of DMIL-IsoFun is further confirmed on a testbed of human multiple-isoform genes, and maize isoforms related with photosynthesis. AVAILABILITY AND IMPLEMENTATION: The code and data are available at http://www.sdu-idea.cn/codes.php?name=DMIL-Isofun. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Maize (Zea mays ssp. mays L.) is the most widely grown and yield crop in the world, as well as an important model organism for fundamental research of the function of genes. The functions of Maize proteins are annotated using the Gene Ontology (GO), which has more than 40000 terms and organizes GO terms in a direct acyclic graph (DAG). It is a huge challenge to accurately annotate relevant GO terms to a Maize protein from such a large number of candidate GO terms. Some deep learning models have been proposed to predict the protein function, but the effectiveness of these approaches is unsatisfactory. One major reason is that they inadequately utilize the GO hierarchy. RESULTS: To use the knowledge encoded in the GO hierarchy, we propose a deep Graph Convolutional Network (GCN) based model (DeepGOA) to predict GO annotations of proteins. DeepGOA firstly quantifies the correlations (or edges) between GO terms and updates the edge weights of the DAG by leveraging GO annotations and hierarchy, then learns the semantic representation and latent inter-relations of GO terms in the way by applying GCN on the updated DAG. Meanwhile, Convolutional Neural Network (CNN) is used to learn the feature representation of amino acid sequences with respect to the semantic representations. After that, DeepGOA computes the dot product of the two representations, which enable to train the whole network end-to-end coherently. Extensive experiments show that DeepGOA can effectively integrate GO structural information and amino acid information, and then annotates proteins accurately. CONCLUSIONS: Experiments on Maize PH207 inbred line and Human protein sequence dataset show that DeepGOA outperforms the state-of-the-art deep learning based methods. The ablation study proves that GCN can employ the knowledge of GO and boost the performance. Codes and datasets are available at http://mlda.swu.edu.cn/codes.php?name=DeepGOA .

Gene Ontology (GO) uses a series of standardized and controlled GO terms to describe the molecular functions, biological process roles and cellular locations of gene products (i.e., proteins and RNAs), it structurally organizes GO terms in a direct acyclic graph (DAG). GO has more than 40000 terms and each protein is only annotated with several or dozens of these terms. It is a difficult challenge to accurately annotate relevant GO terms to a protein from such a large number of candidate GO terms. Some deep learning models have been proposed to utilize the GO hierarchy for protein function prediction, but they inadequately utilize GO hierarchy. To use the knowledge encoded in the GO hierarchy, we propose a deep Graph Convolutional Network (GCN) based model (DeepGOA) to predict GO annotations of proteins. DeepGOA firstly utilizes GO annotations and hierarchy to measure the correlations between GO terms and to accordingly update the edge weights of the DAG, and then applies GCN on the updated DAG to learn the semantic representation and latent inter-relations of GO terms. At the same time, it uses Convolutional Neural Network (CNN) to learn the feature representation of amino acids sequences with respect to the semantic representations. After that, DeepGOA computes the dot product of two representations, which enables training the whole network end-to-end in a coherent fashion. Experiments on two model species (Human and Corn) show that DeepGOA outperforms the state-of-the-art deep learning based methods. The ablation study proves that GCN can employ the knowledge of GO and boost the performance.