Identifying Essential Proteins in Dynamic PPI Network with Improved FOA

Identification of essential proteins plays an important role for understanding the cellular life activity and development in postgenomic era. Identification of essential proteins from the protein-protein interaction (PPI) networks has become a hot topic in recent years. In this work, fruit fly optimization algorithm (FOA) is extended for identifying essential proteins, the extended algorithm is called EPFOA, which merges FOA with topological properties and biological information for essential proteins identification. The algorithm EPFOA has the advantage of identifying multiple essential proteins simultaneously rather than completely relying on ranking score identification individually. The performance of EPFOA is analyzed on dynamic PPI networks, which are constructed by combining the gene expression data. The experimental results demonstrate that EPFOA is more efficient in detecting essential proteins than the state-of-the-art essential proteins detection methods

Fine-grained food image classification and recipe extraction using a customised Deep Neural Network and NLP

Global eating habits cause health issues leading people to mindful eating. This has directed attention to applying deep learning to food-related data. The proposed work develops a new framework integrating neural network and natural language processing for classification of food images and automated recipe extraction. It address the challenges of intra-class variability and inter-class similarity in food images that have received shallow attention in the literature. Firstly, a customised lightweight deep convolution neural network model, MResNet-50 for classifying food images is proposed. Secondly, automated ingredient processing and recipe extraction is done using natural language processing algorithms: Word2Vec and Transformers in conjunction. Thirdly, a representational semi-structured domain ontology is built to store the relationship between cuisine, food item, and ingredients. The accuracy of the proposed framework on the Food-101 and UECFOOD256 datasets is increased by 2.4% and 7.5%, respectively, outperforming existing models in literature such as DeepFood, CNN-Food, Wiser, and other pre-trained neural networks