Background and objectives Although pockets of bioinformatics excellence have developed in Africa, generally, large-scale genomic data analysis has been limited by the availability of expertise and infrastructure. H3ABioNet, a Pan African bioinformatics network, was established to build capacity specifically to enable H3Africa researchers to analyse their data in Africa. Since the inception of the H3Africa initiative, H3ABioNet’s role has evolved in response to changing needs from the consortium and the African bioinformatics community. The network set out to develop core bioinformatics infrastructure and capacity for genomics research in various aspects of data collection, transfer, storage and analysis. Methods and results Various resources have been developed to address genomic data management and analysis needs of H3Africa researchers and other scientific communities on the continent. NetMap was developed and used to build an accurate picture of network performance within Africa and between Africa and the rest of the world, and Globus Online has been rolled out to facilitate data transfer. A participant recruitment database was developed to monitor participant enrolment, and data is being harmonized through the use of ontologies and controlled vocabularies. The standardized metadata will be integrated to provide a search facility for H3Africa data and biospecimens. Since H3Africa projects are generating large-scale genomic data, facilities for analysis and interpretation are critical. H3ABioNet is implementing several data analysis platforms that provide a large range of bioinformatics tools or workflows, such as Galaxy, the Job Management System and eBiokits. A set of reproducible, portable and cloud scalable pipelines to support the multiple H3Africa data types are also being developed and dockerized to enable execution on multiple computing infrastructures. In addition, new tools have been developed for analysis of the uniquely divergent African data and for downstream interpretation of prioritized variants. To provide support for these and other bioinformatics queries, an online bioinformatics helpdesk backed by broad consortium expertise has been established. Further support is provided by means of various modes of bioinformatics training. Conclusion For the past 4 years, the development of infrastructure support and human capacity through H3ABioNet, have significantly contributed to the establishment of African scientific networks, data analysis facilities and training programmes. Here, we describe the infrastructure and how it has impacted genomics and bioinformatics research in Africa.

Background Host-parasite protein interactions (HPPI) are those interactions occurring between a parasite and its host. Host-parasite protein interaction enhances the understanding of how parasite can infect its host. The interaction plays an important role in initiating infections, although it is not all host-parasite interactions that result in infection. Identifying the protein-protein interactions (PPIs) that allow a parasite to infect its host has a lot do in discovering possible drug targets. Such PPIs, when altered, would prevent the host from being infected by the parasite and in some cases, result in the parasite inability to complete specific stages of its life cycle and invariably lead to the death of such parasite. It therefore becomes important to understand the workings of host-parasite interactions which are the major causes of most infectious diseases. Objective Many studies have been conducted in literature to predict HPPI, mostly using computational methods with few experimental methods. Computational method has proved to be faster and more efficient in manipulating and analyzing real life data. This study looks at various computational methods used in literature for host-parasite/inter-species protein-protein interaction predictions with the hope of getting a better insight into computational methods used and identify whether machine learning approaches have been extensively used for the same purpose. Methods The various methods involved in host-parasite protein interactions were reviewed with their individual strengths. Tabulations of studies that carried out host-parasite/inter-species protein interaction predictions were performed, analyzing their predictive methods, filters used, potential protein-protein interactions discovered in those studies and various validation measurements used as the case may be. The commonly used measurement indexes for such studies were highlighted displaying the various formulas. Finally, future prospects of studies specific to human-plasmodium falciparum PPI predictions were proposed. Result We discovered that quite a few studies reviewed implemented machine learning approach for HPPI predictions when compared with methods such as sequence homology search and protein structure and domain-motif. The key challenge well noted in HPPI predictions is getting relevant information. Conclusion This review presents useful knowledge and future directions on the subject matter.

This study designed a framework to assist farmers increase their productivity by receiving weather information through decision support system. The system has been developed to keep track of weather information related to agriculture. With the growing population and demands to improve crop productivity; there is the need to make available sustainable resource practice that serves better both the communities and the nation. In satisfying this need, a web-based application which contains informative and insightful agricultural tutelages was developed to aid decision making in agro-processing, stimulate the farmer's climate information and provide useful information required to enhance crop productivity, especially in the rural areas. The application uses Short Message Service (SMS) Technology to disseminate weather forecasting to farmers according to their eWarning setup. Therefore, the Decision Support System with all the ready agricultural and weather information will be a huge advantage to farmers at large and is expected to impact positively on the present economy situation of the nation through increase in smallholder's productivity.

Abstract-In our present environment, heart diseases are very rampart and they describe the various types of diseases that affect the heart. They account for the leading cause of death word-wide especially, in Africa. It is therefore very important for individuals to have adequate knowledge of their heart health in order to avoid the risk of decreased life expectancy. The high mortality rate of heart (cardiovascular) diseases is attributed to the unequal ratio of patients to scarcity of medical experts who can provide medical care, also patients are not always warned to waiting long hours on queue in the hospital, especially in cases of emergency. This paper designed and implemented a Mobile Neuro-fuzzy System that uses the combination of the intelligent technique of Artificial Neural Networks (ANN) and the human-like reasoning style of Fuzzy Logic to diagnose and suggest possible treatments for cardiovascular diseases through interactivity with user. It employs programs like MySQL, PHP, JAVA (Android) and XML (Android Studio) while tools like XAMPP, PhpStorm and Android O/S were used to integrate these techniques together. The system proved to be of enormous advantage in diagnosing heart diseases, as it diagnoses and learns about each user per time, to provide adequate and appropriate results and also makes reliable predictions to users.

Healthcare Informatics focuses on health data, information and knowledge, including their collection, processing, analysis and use. Bioinformatics employ computational tools and techniques to study and analyse large biological databases and to absolutely understand disease and grasp the genetics and proteomics by relating them with healthcare data. The focus is on processing genomic and proteomics data for basic research in biology, but also medicine, drug discovery, and related areas. Analytics in healthcare came as a result of large healthcare data that are being gathered electronically. Data analytics is proficient in terms of healthcare improvement, reduction in cost and safety of lives. Applications of data analytics in healthcare is as a result of the eruption in data to mine understandings so as to make informed decisions. This paper reviews bioinformatics, Healthcare Informatics and Analytics as an imperative for an improved Healthcare System. It looks at the benefits, the contribution of each of them to improving healthcare system, the overlap among bioinformatics, healthcare Informatics and analytics and finally the future prospects of healthcare informatics and analytics.