Corrigendum to: Machine learning approach to gene essentiality prediction: a review

Essential genes are critical for the growth and survival of any organism. The machine learning approach complements the experimental methods to minimize the resources required for essentiality assays. Previous studies revealed the need to discover relevant features that significantly classify essential genes, improve on the generalizability of prediction models across organisms, and construct a robust gold standard as the class label for the train data to enhance prediction. Findings also show that a significant limitation of the machine learning approach is predicting conditionally essential genes. The essentiality status of a gene can change due to a specific condition of the organism. This review examines various methods applied to essential gene prediction task, their strengths, limitations and the factors responsible for effective computational prediction of essential genes.We discussed categories of features and how they contribute to the classification performance of essentiality prediction models. Five categories of features, namely, gene sequence, protein sequence, network topology, homology and gene ontology-based features, were generated for Caenorhabditis elegans to perform a comparative analysis of their essentiality prediction capacity. Gene ontology-based feature category outperformed other categories of features majorly due to its high correlation with the genes’ biological functions. However, the topology feature category provided the highest discriminatory power making it more suitable for essentiality prediction. The major limiting factor of machine learning to predict essential genes conditionality is the unavailability of labeled data for interest conditions that can train a classifier. Therefore, cooperative machine learning could further exploit models that can perform well in conditional essentiality predictions. Short abstract Identification of essential genes is imperative because it provides an understanding of the core structure and function, accelerating drug targets’ discovery, among other functions. Recent studies have applied machine learning to complement the experimental identification of essential genes. However, several factors are limiting the performance of machine learning approaches. This review aims to present the standard procedure and resources available for predicting essential genes in organisms, and also highlight the factors responsible for the current limitation in using machine learning for conditional gene essentiality prediction. The choice of features and ML technique was identified as an important factor to predict essential genes effectively

Antibiotic susceptibility pattern of Klebsiella pneumoniae and Pseudomonas aeruginosa isolated from some drinking wells in Ondo town southwest Nigeria

Antibiotic-resistant bacteria (ARB) strains have become a global health threat. This study aimed to determine the antibiotic susceptibility pattern of Klebsiella pneumoniae and Pseudomonas aeruginosa isolated from wells in Ondo town, Southwest Nigeria. Twenty-eight well water samples were analyzed for the presence of K. pneumoniae and P. aeruginosa by standard pour plate technique. The bacterial isolates were tested against eight commonly use antibiotics using Kirby Bauer disc diffusion method. The percentage occurrence of K. pneumoniae and P. aeruginosa in the well water samples were 17.86% and 21.43%, respectively. Two multi-drug resistant strains of K. pneumoniae were isolated, which were resistant to at least three classes of antibiotics. Fifty percent of the P. aeruginosa isolates were resistant to caftazidime, cefuroxime, nitrofurantoin, and ampicillin. None of the isolates was fully susceptible to cefuroxime, but have all showed resistance to \u3b2-lactam (ceftazidime, cefuroxime augmentin, and ampicillin) antibiotics. Cefuroxime may not be effective an effective drug in the treatment of K. pneumoniae and P. aeruginosa implicated infections in these communities in Ondo. Also, the over-use of antibiotics should be discouraged in order to curtail the menace of antibiotic resistance

Human Empowerment through Skills Acquisition:Issues, Impacts and Consequences - A Non-Parametric View

One of the major Millennium Development Goals (MDGs) which Nigeria is set to achieve in 2015 is the eradication of extreme poverty and hunger with the target of halving the proportion of people who earn less than a dollar a day through skills acquisition. The study was on non-parametric view on issues, impacts and consequences of human empowerment through skills acquisition in Nigeria. The aim of this study was to assess the impact and input of various skills acquisition encouraged and engaged by Nigerians especially the youths. The specific objectives are: to identify skills most learned by Nigerians, identify the major contributions of human empowerment through skills acquisition and access if opinion on skills acquisition is gender and education dependent. The study was cross-sectional in nature conducted in Yaba and Akoka areas using a 21-item questionnaire tagged “Human Empowerment through Skills acquisition Questionnaire’ (HETSAQ), designed by the researchers and administered for the purpose. The researchers obtained a very high response rate from the field. Descriptive statistics were presented in tables and charts while Friedman rank test was used to test the hypothesis that skills acquisition has no significant impact on the recipients and to rank the perceived impacts and the Chi square test used to ascertain the influence of education on the responses obtained from respondents. Results showed that most respondents have learnt one skill or the other and would prefer to be empowered in areas like computer skills, hair dressing, tailoring and soap making, etc. It was discovered that skills acquisition have significant contribution to society through human empowerment and such opinions have no gender bias but differed significantly by educational attainment. From the discoveries, we therefore conclude that skills acquisition contributes greatly in elimination of joblessness in Nigeria, development of positive attitude towards work, developing entrepreneurial ability, builds self-reliant young people, leads to technological advancement, reduce poverty and crime rate in the society and these were the verdict of both men and women included in the study. Keywords: skills acquisition, entrepreneurial ability, Friedman test, human empowerment, joblessness

Performance evaluation of features for gene essentiality prediction

Essential genes are subset of genes required by an organism for growth and sustenance of life and as well responsible for phenotypic changes when their activities are altered. They have been utilized as drug targets, disease control agent, etc. Essential genes have been widely identified especially in microorganisms, due to the extensive experimental studies on some of them such as Escherichia coli and Saccharomyces cerevisiae. Experimental approach has been a reliable method to identify essential genes. However, it is complex, costly, labour and time intensive. Therefore, computational approach has been developed to complement the experimental approach in order to minimize resources required for essentiality identification experiments. Machine learning approaches have been widely used to predict essential genes in model organisms using different categories of features with varying degrees of accuracy and performance. However, previous studies have not established the most important categories of features that provide the distinguishing power in machine learning essentiality predictions. Therefore, this study evaluates the discriminating strength of major categories of features used in essential gene prediction task as well as the factors responsible for effective computational prediction. Four categories of features were considered and kfold cross-validation machine learning technique was used to build the classification model. Our results show that ontology features with an AUROC score of 0.936 has the most discriminating power to classify essential and non-essential genes. This studyconcludes that more ontology related features will further improve the performance of machine learning approach and also sensitivity, precision and AUPRC are realistic measures of performance in essentiality prediction

Comparative Analysis of Pre and Post-migration Livelihood Outcomes of Households with Absentee Heads in Osun State, Nigeria

Migration is often linked with a deleterious impact on rural area production and development. Although, the change of location for better opportunities also affect the lives and livelihoods of the migrant households’ in the rural communities. The study was a comparative analysis of the pre and post-migration living outcomes of absentee households’ heads in Osun State, Nigeria. A multistage sampling procedure involving the simple random sampling was used to select 120 absentee household heads. Data collected with interview schedule was subjected to descriptive statistics, t-test and correlation analyses. Findings showed that more men (76.7%) migrated, leaving women to become the interim household heads. It was found that migrants have a higher average monthly income level (₦44,400). Prior to migration, most families were in the lower financial well-being category (83.3%), while only 55% remained in that category after migration. This follows the result of the t-test which revealed that a significant difference (t=0.00; p<0.05) exists between the well-being of migrant’s household before and after migration. Thus, it was concluded that unless the rural push factors are removed, rural-urban migration will continue at an increasing rate because benefits and opportunities acquired in the process influence the well-being of the rural households. The study recommends that enabling environment, facilities and opportunities should be created in the rural communities to transform livelihoods and improve the wellbeing of the people via interventions by national and international agencies

In-silicoValidation of the Essentiality of Reactions in Plasmodium Falciparum Metabolic Network

Plasmodium falciparum are instrumental in causing malaria and have developed complex life cycles, thus, it becomes very possible for the malaria parasite to take advantage of the uniqueness of its pathways to design therapeutic strategies. Despite the colossal efforts put in to fight malaria the disease still affects up to over 200 million people every year amongst which close to half a million dies. The treatment of the disease, could be done successfully if the essential enzymes of this parasite is precisely targeted. Nevertheless, the development of the parasite to resisting existing drugs now makes it a core responsibility to discover novel drugs. In this study, existing essential reactions from different literature are considered and evaluated to determine reactions that are common in all literature and evaluated to determine their essentiality level. This study evaluates essential reactions that has been predicted in literature computationally and validates its essentiality based on the reconstructed metabolic network and identifies 10 essential reactions that are common to all existing literature of which all this reaction were validated to be essential by our method. This study has established a simple novel in-silico method that validates predicted essential reactions in a metabolic network which makes validation of predicted anti-malarial drug target cheaper, easier and faster. This study in-silico model serves as a valuable tool for validation of Plasmodium falciparum metabolic states under various perturbations

Extreme Pathway Analysis of Mycobacterium Tuberculosis

Tuberculosis is a multisystem disorder characterized by the formation of swelling that is filled with blood which is caused from breakage in the wall of a blood vessel. This breakage can occur in different organs of the body and is caused by a bacterium known as Mycobacterium Tuberculosis (mtb) which can be represented as a metabolic network with genes, proteins and enzymes that are interconnected. This interconnection defines the uniqueness of any bacteria. The analysis of a metabolic network system is achievable through different computational techniques depending on what information is available. The flux balance analysis is mostly used for analyzing this type of network because of the little amount of information required. The application of flux balance analysis to mtb involves the conversion of the metabolic network into a stoichiometric matrix where the rows represents the metabolites and the columns represent the reactions. In this study, the stoichiometric matrix is an 828 by 1027 matrix. The analysis generated a linear problem having more unknowns than the number of equations. This type of problem is normally solved using an extreme pathways algorithm to extract independent paths and simplex method for optimization of biomass. Here, the extreme pathways analysis was used in the categorization of the metabolite of mtb while biomass was employed as the objective function using default constraints. The output represents three categories of metabolite: 31 metabolites that form part of the biomass component that are inactive, 14 metabolites that remain active and 32 metabolites that are activated after the optimization process

Machine Learning and Sentiment Analysis: Examining the Contextual Polarity of Public Sentiment on Malaria Disease in Social Networks

Malaria, a major deadly disease which is still a threat to human life’s even though numerous efforts has been put to fight it, still affects over two hundred million people each year amongst which over a million individuals dies. Twitter happens to be an important and comprehensive source of information that is quite subjective to individual sentiments towards public health care. In this study, we extracted tweets from the social network twitter, we pre-processed the tweets extracted and built a model to fit our data using a machine learning approach for text classification to determine the contextual polarity of every tweet on the subject of malaria in the bid to harvest peoples’ opinion towards malaria and understand how well research and recent development in the aid to tackle malaria has affected the opinions of the public towards the subject malaria. This study finds that tweets extracted, pre-processed and classified in this study were majorly classified as negative (-ve) due to the fact that tweets tweeted were majorly about different occurrence of death, misinformation and need for donations to save a life, hence a major awareness is needed

A Multi-Phase Assessment of the Effects of COVID-19 on Food Systems and Rural Livelihoods in Nigeria

The first case of COVID-19 in Nigeria was reported on 27 February 2020. By 30 March 2020, Nigeria had recorded 131 confirmed cases and two deaths. To mitigate the impending health crisis, the Nigerian Government quickly commenced a series of COVID-19 lockdowns across states in Nigeria on 30 March 2020. These lockdowns lasted for three months before a gradual relaxation began on 1 July 2021. However, infection and death cases in the country increased substantially during the months of substantial relaxation of restrictions between October 2020 and March 2021. This paper presents the results of the rapid assessment study in Nigeria between July 2020 and February 2021, which sought to document and understand the differential impacts of the COVID-19 pandemic on agricultural commercialisation, food and nutrition security, employment, poverty, and well-being in rural households