Background: Bacterial pathogens are deadly for animals and humans. The ease of their dissemination, coupled with their high capacity for ailment and death in infected individuals, makes them a threat to society. Objective: Due to high similarity among genera and species of pathogens, it is sometimes difficult for microbiologists to differentiate between them. Their automatic classification using deep-learning models can help in reliable, and accurate outcomes. Method: Deep-learning models, namely; AlexNet, GoogleNet, ResNet101, and InceptionV3 are used with numerous variations including training model from scratch, fine-tuning without pre-trained weights, fine-tuning along with freezing weights of initial layers, fine-tuning along with adjusting weights of all layers and augmenting the dataset by random translation and reflection. Moreover, as the dataset is small, fine-tuning and data augmentation strategies are applied to avoid overfitting and produce a generalized model. A merged feature vector is produced using two best-performing models and accuracy is calculated by xgboost algorithm on the feature vector by applying cross-validation. Results: Fine-tuned models where augmentation is applied produces the best results. Out of these, two-best-performing deep models i.e. (ResNet101, and InceptionV3) selected for feature fusion, produced a similar validation accuracy of 95.83 with a loss of 0.0213 and 0.1066, and a testing accuracy of 97.92 and 93.75, respectively. The proposed model used xgboost to attained a classification accuracy of 98.17% by using 35-folds cross-validation. Conclusion: The automatic classification using these models can help experts in the correct identification of pathogens. Consequently, they can help in controlling epidemics and thereby minimizing the socio-economic impact on the community.

: Integrating heterogeneous biological databases for unveiling the new intra-molecular and inter-molecular attributes, behaviors, and relationships in the human cellular system has always been a focused research area of computational biology. In this context, a lot of biological data integration systems have been deployed in the last couple of decades. One of the prime and common objectives of all these systems is to better facilitate the end-users for exploring, exploiting, and analyzing the integrated biological data for knowledge extraction. With the advent of especially highthroughput data generation technologies, biological data is growing and dispersing continuously, exponentially, heterogeneously, and geographically. Due to this, biological data integration systems are too facing data integration and data organization-related current and future challenges. The objective of this review is to quantitatively evaluate and compare some of the recent warehouse-based multi-omics data integration systems to check their compliance with the current and future data integration needs. For this, we identified some of the major data integration design characteristics that should be in the multi-omics data integration model to comprehensively address the current and future data integration challenges. Based on these design characteristics and the evaluation criteria, we evaluated some of the recent data warehouse systems and showed categorical and comparative analysis results. Results show that most of the systems exhibit no or partial compliance with the required data integration design characteristics. So, these systems need design improvements to adequately address the current and future data integration challenges while keeping their service level commitments in place.

Aim: To facilitate researchers and practitioners for unveiling the mysterious functional aspects of human cellular system through performing exploratory searching on semantically integrated heterogeneous and geographically dispersed omics annotations. Background: Improving health standards of life is one of the motives which continuously instigates researchers and practitioners to strive for uncovering the mysterious aspects of human cellular system. Inferring new knowledge from known facts always requires reasonably large amount of data in well-structured, integrated and unified form. Due to the advent of especially high throughput and sensor technologies, biological data is growing heterogeneously and geographically at astronomical rate. Several data integration systems have been deployed to cope with the issues of data heterogeneity and global dispersion. Systems based on semantic data integration models are more flexible and expandable than syntax-based ones but still lack aspect-based data integration, persistence and querying. Furthermore, these systems do not fully support to warehouse biological entities in the form of semantic associations as naturally possessed by the human cell. Objective: To develop aspect-oriented formal data integration model for semantically integrating heterogeneous and geographically dispersed omics annotations for providing exploratory querying on integrated data. Method: We propose an aspect-oriented formal data integration model which uses web semantics standards to formally specify its each construct. Proposed model supports aspect-oriented representation of biological entities while addressing the issues of data heterogeneity and global dispersion. It associates and warehouses biological entities in the way they relate with Result: To show the significance of proposed model, we developed a data warehouse and information retrieval system based on proposed model compliant multi-layered and multi-modular software architecture. Results show that our model supports well for gathering, associating, integrating, persisting and querying each entity with respect to its all possible aspects within or across the various associated omics layers. Conclusion: Formal specifications better facilitate for addressing data integration issues by providing formal means for understanding omics data based on meaning instead of syntax