Correction: Heuristic-enabled active machine learning: A case study of predicting essential developmental stage and immune response genes in Drosophila melanogaster.

[This corrects the article DOI: 10.1371/journal.pone.0288023.].

Breast Cancer Phenotypes in Africa: A Scoping Review and Meta-Analysis.

PURPOSE: Africans have been associated with more aggressive forms of breast cancer (BC). However, there is a lack of data regarding the incidence and distribution of different subtypes on the basis of phenotypic classification. This scoping review and meta-analysis was undertaken to determine the distribution pattern of BC phenotypes (luminal, human epidermal growth factor receptor 2 [HER2]+, and triple-negative breast cancer [TNBC]) across the African region. METHODS: Four online databases (PubMed, Scopus, ProQuest, and EBSCOhost) were accessed to identify studies published between 2000 and 2022 reporting the representation of receptor status (estrogen receptor, progesterone receptor, and HER2) in African patients with BC. Furthermore, the meta-analysis was carried out using a random-effects model and pooled using the inverse variance method and logit transformation. 95% CI and I2 statistics were calculated using the Clopper-Pearson method to estimate between-study heterogeneity. RESULTS: A total of 2,734 records were retrieved, of which 2,133 were retained for further screening. After the screening, 63 studies were finally selected for the scoping review and meta-analysis. The pooled frequency of luminal, HER2-positive (HER2+), and TNBC was estimated at 56.30%, 12.61%, and 28.10%, respectively. Northern Africa had the highest frequency of the luminal subtype, while West Africa showed higher frequencies of HER2+ and TNBC subtypes. The review also had a representation of only 24 countries in Africa. CONCLUSION: Our results highlight the disparity in the representation of molecular subtypes among the people in different regions of Africa. There is a need to incorporate routine molecular subtyping into the management of African patients with BC.

Mathematical modeling of malaria transmission dynamics in humans with mobility and control states.

Malaria importation is one of the hypothetical drivers of malaria transmission dynamics across the globe. Several studies on malaria importation focused on the effect of the use of conventional malaria control strategies as approved by the World Health Organization (WHO) on malaria transmission dynamics but did not capture the effect of the use of traditional malaria control strategies by vigilant humans. In order to handle the aforementioned situation, a novel system of Ordinary Differential Equations (ODEs) was developed comprising the human and the malaria vector compartments. Analysis of the system was carried out to assess its quantitative properties. The novel computational algorithm used to solve the developed system of ODEs was implemented and benchmarked with the existing Runge-Kutta numerical solution method. Furthermore, simulations of different vigilant conditions useful to control malaria were carried out. The novel system of malaria models was well-posed and epidemiologically meaningful based on its quantitative properties. The novel algorithm performed relatively better in terms of model simulation accuracy than Runge-Kutta. At the best model-fit condition of 98% vigilance to the use of conventional and traditional malaria control strategies, this study revealed that malaria importation has a persistent impact on malaria transmission dynamics. In lieu of this, this study opined that total vigilance to the use of the WHO-approved and traditional malaria management tools would be the most effective control strategy against malaria importation.

Heuristic-enabled active machine learning: A case study of predicting essential developmental stage and immune response genes in Drosophila melanogaster.

Computational prediction of absolute essential genes using machine learning has gained wide attention in recent years. However, essential genes are mostly conditional and not absolute. Experimental techniques provide a reliable approach of identifying conditionally essential genes; however, experimental methods are laborious, time and resource consuming, hence computational techniques have been used to complement the experimental methods. Computational techniques such as supervised machine learning, or flux balance analysis are grossly limited due to the unavailability of required data for training the model or simulating the conditions for gene essentiality. This study developed a heuristic-enabled active machine learning method based on a light gradient boosting model to predict essential immune response and embryonic developmental genes in Drosophila melanogaster. We proposed a new sampling selection technique and introduced a heuristic function which replaces the human component in traditional active learning models. The heuristic function dynamically selects the unlabelled samples to improve the performance of the classifier in the next iteration. Testing the proposed model with four benchmark datasets, the proposed model showed superior performance when compared to traditional active learning models (random sampling and uncertainty sampling). Applying the model to identify conditionally essential genes, four novel essential immune response genes and a list of 48 novel genes that are essential in embryonic developmental condition were identified. We performed functional enrichment analysis of the predicted genes to elucidate their biological processes and the result evidence our predictions. Immune response and embryonic development related processes were significantly enriched in the essential immune response and embryonic developmental genes, respectively. Finally, we propose the predicted essential genes for future experimental studies and use of the developed tool accessible at http://heal.covenantuniversity.edu.ng for conditional essentiality predictions.

In silico Structure Prediction, Molecular Docking, and Dynamic Simulation of Plasmodium falciparum AP2-I Transcription Factor.

Plasmodium falciparum Apicomplexan Apetala 2 Invasion (PfAP2-I) transcription factor (TF) is a protein that regulates the expression of a subset of gene families involved in P. falciparum red blood cell (RBC) invasion. Inhibiting PfAP2-I TF with small molecules represents a potential new antimalarial therapeutic target to combat drug resistance, which this study aims to achieve. The 3D model structure of PfAP2-I was predicted ab initio using ROBETTA prediction tool and was validated using Save server 6.0 and MolProbity. Computed Atlas of Surface Topography of proteins (CASTp) 3.0 was used to predict the active sites of the PfAP2-I modeled structure. Pharmacophore modeling of the control ligand and PfAP2-I modeled structure was carried out using the Pharmit server to obtain several compounds used for molecular docking analysis. Molecular docking and postdocking studies were conducted using AutoDock vina and Discovery studio. The designed ligands' toxicity predictions and in silico drug-likeness were performed using the SwissADME predictor and OSIRIS Property Explorer. The modeled protein structure from the ROBETTA showed a validation result of 96.827 for ERRAT, 90.2% of the amino acid residues in the most favored region for the Ramachandran plot, and MolProbity score of 1.30 in the 98th percentile. Five (5) best hit compounds from molecular docking analysis were selected based on their binding affinity (between -8.9 and -11.7 Kcal/mol) to the active site of PfAP2-I and were considered for postdocking studies. For the absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties, compound MCULE-7146940834 had the highest drug score (0.63) and drug-likeness (6.76). MCULE-7146940834 maintained a stable conformation within the flexible protein's active site during simulation. The good, estimated binding energies, drug-likeness, drug score, and molecular dynamics simulation interaction observed for MCULE-7146940834 against PfAP2-I show that MCULE-7146940834 can be considered a lead candidate for PfAP2-I inhibition. Experimental validations should be carried out to ascertain the efficacy of these predicted best hit compounds.

SysBiolPGWAS: simplifying post-GWAS analysis through the use of computational technologies and integration of diverse omics datasets.

MOTIVATION: Post-genome-wide association studies (pGWAS) analysis is designed to decipher the functional consequences of significant single-nucleotide polymorphisms (SNPs) in the era of GWAS. This can be translated into research insights and clinical benefits such as the effectiveness of strategies for disease screening, treatment and prevention. However, the setup of pGWAS (pGWAS) tools can be quite complicated, and it mostly requires big data. The challenge however is, scientists are required to have sufficient experience with several of these technically complex and complicated tools in order to complete the pGWAS analysis. RESULTS: We present SysBiolPGWAS, a pGWAS web application that provides a comprehensive functionality for biologists and non-bioinformaticians to conduct several pGWAS analyses to overcome the above challenges. It provides unique functionalities for analysis involving multi-omics datasets and visualization using various bioinformatics tools. SysBiolPGWAS provides access to individual pGWAS tools and a novel custom pGWAS pipeline that integrates several individual pGWAS tools and data. The SysBiolPGWAS app was developed to be a one-stop shop for pGWAS analysis. It targets researchers in the area of the human genome and performs its analysis mainly in the autosomal chromosomes. AVAILABILITY AND IMPLEMENTATION: SysBiolPGWAS web app was developed using JavaScript/TypeScript web frameworks and is available at: https://spgwas.waslitbre.org/. All codes are available in this GitHub repository https://github.com/covenant-university-bioinformatics.

Predicting host dependency factors of pathogens in Drosophila melanogaster using machine learning.

Pathogens causing infections, and particularly when invading the host cells, require the host cell machinery for efficient regeneration and proliferation during infection. For their life cycle, host proteins are needed and these Host Dependency Factors (HDF) may serve as therapeutic targets. Several attempts have approached screening for HDF producing large lists of potential HDF with, however, only marginal overlap. To get consistency into the data of these experimental studies, we developed a machine learning pipeline. As a case study, we used publicly available lists of experimentally derived HDF from twelve different screening studies based on gene perturbation in Drosophila melanogaster cells or in vivo upon bacterial or protozoan infection. A total of 50,334 gene features were generated from diverse categories including their functional annotations, topology attributes in protein interaction networks, nucleotide and protein sequence features, homology properties and subcellular localization. Cross-validation revealed an excellent prediction performance. All feature categories contributed to the model. Predicted and experimentally derived HDF showed a good consistency when investigating their common cellular processes and function. Cellular processes and molecular function of these genes were highly enriched in membrane trafficking, particularly in the trans-Golgi network, cell cycle and the Rab GTPase binding family. Using our machine learning approach, we show that HDF in organisms can be predicted with high accuracy evidencing their common investigated characteristics. We elucidated cellular processes which are utilized by invading pathogens during infection. Finally, we provide a list of 208 novel HDF proposed for future experimental studies.

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.

Essential gene prediction in Drosophila melanogaster using machine learning approaches based on sequence and functional features.

Genes are termed to be essential if their loss of function compromises viability or results in profound loss of fitness. On the genome scale, these genes can be determined experimentally employing RNAi or knockout screens, but this is very resource intensive. Computational methods for essential gene prediction can overcome this drawback, particularly when intrinsic (e.g. from the protein sequence) as well as extrinsic features (e.g. from transcription profiles) are considered. In this work, we employed machine learning to predict essential genes in Drosophila melanogaster. A total of 27,340 features were generated based on a large variety of different aspects comprising nucleotide and protein sequences, gene networks, protein-protein interactions, evolutionary conservation and functional annotations. Employing cross-validation, we obtained an excellent prediction performance. The best model achieved in D. melanogaster a ROC-AUC of 0.90, a PR-AUC of 0.30 and a F1 score of 0.34. Our approach considerably outperformed a benchmark method in which only features derived from the protein sequences were used (P < 0.001). Investigating which features contributed to this success, we found all categories of features, most prominently network topological, functional and sequence-based features. To evaluate our approach we performed the same workflow for essential gene prediction in human and achieved an ROC-AUC = 0.97, PR-AUC = 0.73, and F1 = 0.64. In summary, this study shows that using our well-elaborated assembly of features covering a broad range of intrinsic and extrinsic gene and protein features enabled intelligent systems to predict well the essentiality of genes in an organism.

Computational Identification of Metabolic Pathways of Plasmodium falciparum using the k-Shortest Path Algorithm.

Plasmodium falciparum, a malaria pathogen, has shown substantial resistance to treatment coupled with poor response to some vaccines thereby requiring urgent, holistic, and broad approach to prevent this endemic disease. Understanding the biology of the malaria parasite has been identified as a vital approach to overcome the threat of malaria. This study is aimed at identifying essential proteins unique to malaria parasites using a reconstructed iPfa genome-scale metabolic model (GEM) of the 3D7 strain of Plasmodium falciparum by filling gaps in the model with nineteen (19) metabolites and twenty-three (23) reactions obtained from the MetaCyc database. Twenty (20) currency metabolites were removed from the network because they have been identified to produce shortcuts that are biologically infeasible. The resulting modified iPfa GEM was a model using the k-shortest path algorithm to identify possible alternative metabolic pathways in glycolysis and pentose phosphate pathways of Plasmodium falciparum. Heuristic function was introduced for the optimal performance of the algorithm. To validate the prediction, the essentiality of the reactions in the reconstructed network was evaluated using betweenness centrality measure, which was applied to every reaction within the pathways considered in this study. Thirty-two (32) essential reactions were predicted among which our method validated fourteen (14) enzymes already predicted in the literature. The enzymatic proteins that catalyze these essential reactions were checked for homology with the host genome, and two (2) showed insignificant similarity, making them possible drug targets. In conclusion, the application of the intelligent search technique to the metabolic network of P. falciparum predicts potential biologically relevant alternative pathways using graph theory-based approach.

Gene Expression Profiling Analysis Reveals Putative Phytochemotherapeutic Target for Castration-Resistant Prostate Cancer.

Prostate cancer is the leading cause of cancer death among men globally, with castration development resistant contributing significantly to treatment failure and death. By analyzing the differentially expressed genes between castration-induced regression nadir and castration-resistant regrowth of the prostate, we identified soluble guanylate cyclase 1 subunit alpha as biologically significant to driving castration-resistant prostate cancer. A virtual screening of the modeled protein against 242 experimentally-validated anti-prostate cancer phytochemicals revealed potential drug inhibitors. Although, the identified four non-synonymous somatic point mutations of the human soluble guanylate cyclase 1 gene could alter its form and ligand binding ability, our analysis identified compounds that could effectively inhibit the mutants together with wild-type. Of the identified phytochemicals, (8'R)-neochrome and (8'S)-neochrome derived from the Spinach (Spinacia oleracea) showed the highest binding energies against the wild and mutant proteins. Our results identified the neochromes and other phytochemicals as leads in pharmacotherapy and as nutraceuticals in management and prevention of castration-resistance prostate cancers.

Potential Anti-Cancer Flavonoids Isolated From Caesalpinia bonduc Young Twigs and Leaves: Molecular Docking and In Silico Studies.

Tyrosine kinase (TK), vascular endothelial growth factor (VEGF), and matrix metalloproteinases (MMP) are important cancer therapeutic target proteins. Based on reported anti-cancer and cytotoxic activities of Caesalpinia bonduc, this study isolated phytochemicals from young twigs and leaves of C bonduc and identified the interaction between them and cancer target proteins (TK, VEGF, and MMP) in silico. AutoDock Vina, iGEMDOCK, and analysis of pharmacokinetic and pharmacodynamic properties of the isolated bioactives as therapeutic molecules were performed. Seven phytochemicals (7-hydroxy-4'-methoxy-3,11-dehydrohomoisoflavanone, 4,4'-dihydroxy-2'-methoxy-chalcone, 7,4'-dihydroxy-3,11-dehydrohomoisoflavanone, luteolin, quercetin-3-methyl, kaempferol-3-O-ß-d-xylopyranoside and kaempferol-3-O-α-l-rhamnopyranosyl-(1 → 2)-ß-D-xylopyranoside) were isolated. Molecular docking analysis showed that the phytochemicals displayed strong interactions with the proteins compared with their respective drug inhibitors. Pharmacokinetic and pharmacodynamic properties of the compounds were promising suggesting that they can be developed as putative lead compounds for developing new anti-cancer drugs.

Genomic Interventions in Medicine.

Lately, the term "genomics" has become ubiquitous in many scientific articles. It is a rapidly growing aspect of the biomedical sciences that studies the genome. The human genome contains a torrent of information that gives clues about human origin, evolution, biological function, and diseases. In a bid to demystify the workings of the genome, the Human Genome Project (HGP) was initiated in 1990, with the chief goal of sequencing the approximately 3 billion nucleotide base pairs of the human DNA. Since its completion in 2003, the HGP has opened new avenues for the application of genomics in clinical practice. This review attempts to overview some milestone discoveries that paved way for the initiation of the HGP, remarkable revelations from the HGP, and how genomics is influencing a paradigm shift in routine clinical practice. It further highlights the challenges facing the implementation of genomic medicine, particularly in Africa. Possible solutions are also discussed.

In Silico Knockout Screening of Plasmodium falciparum Reactions and Prediction of Novel Essential Reactions by Analysing the Metabolic Network.

Malaria is an infectious disease that affects close to half a million individuals every year and Plasmodium falciparum is a major cause of malaria. The treatment of this disease could be done effectively if the essential enzymes of this parasite are specifically targeted. Nevertheless, the development of the parasite in resisting existing drugs now makes discovering new drugs a core responsibility. In this study, a novel computational model that makes the prediction of new and validated antimalarial drug target cheaper, easier, and faster has been developed. We have identified new essential reactions as potential targets for drugs in the metabolic network of the parasite. Among the top seven (7) predicted essential reactions, four (4) have been previously identified in earlier studies with biological evidence and one (1) has been with computational evidence. The results from our study were compared with an extensive list of seventy-seven (77) essential reactions with biological evidence from a previous study. We present a list of thirty-one (31) potential candidates for drug targets in Plasmodium falciparum which includes twenty-four (24) new potential candidates for drug targets.

Inter-Species/Host-Parasite Protein Interaction Predictions Reviewed.

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.

Cluster analysis of Plasmodium RNA-seq time-course data identifies stage-specific co-regulated biological processes and regulatory elements.

In this study, we interpreted RNA-seq time-course data of three developmental stages of Plasmodium species by clustering genes based on similarities in their expression profile without prior knowledge of the gene function. Functional enrichment of clusters of upregulated genes at specific time-points reveals potential targetable biological processes with information on their timings. We identified common consensus sequences that these clusters shared as potential points of coordinated transcriptional control. Five cluster groups showed upregulated profile patterns of biological interest. This included two clusters from the Intraerythrocytic Developmental Cycle (cluster 4 = 16 genes, and cluster 9 = 32 genes), one from the sexual development stage (cluster 2 = 851 genes), and two from the gamete-fertilization stage in the mosquito host (cluster 4 = 153 genes, and cluster 9 = 258 genes). The IDC expressed the least numbers of genes with only 1448 genes showing any significant activity of the 5020 genes (~29%) in the experiment. Gene ontology (GO) enrichment analysis of these clusters revealed a total of 671 uncharacterized genes implicated in 14 biological processes and components associated with these stages, some of which are currently being investigated as drug targets in on-going research. Five putative transcription regulatory binding motifs shared by members of each cluster were also identified, one of which was also identified in a previous study by separate researchers. Our study shows stage-specific genes and biological processes that may be important in antimalarial drug research efforts. In addition, timed-coordinated control of separate processes may explain the paucity of factors in parasites.

Clustering Algorithms: Their Application to Gene Expression Data.

Gene expression data hide vital information required to understand the biological process that takes place in a particular organism in relation to its environment. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks and the volume of genes present increase the challenges of comprehending and interpretation of the resulting mass of data, which consists of millions of measurements; these data also inhibit vagueness, imprecision, and noise. Therefore, the use of clustering techniques is a first step toward addressing these challenges, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding gene functions, cellular processes, and subtypes of cells, mining useful information from noisy data, and understanding gene regulation. The other benefit of clustering gene expression data is the identification of homology, which is very important in vaccine design. This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge of the appropriate clustering technique that will guarantee stability and high degree of accuracy in its analysis procedure.

Modeling of the Glycolysis Pathway in Plasmodium falciparum using Petri Nets.

Malaria is one of the deadly diseases, which affects a large number of the world's population. The Plasmodium falciparum parasite during erythrocyte stages produces its energy mainly through anaerobic glycolysis, with pyruvate being converted into lactate. The glycolysis metabolism in P. falci-parum is one of the important metabolic pathways of the parasite because the parasite is entirely dependent on it for energy. Also, several glycolytic enzymes have been proposed as drug targets. Petri nets (PNs) have been recognized as one of the important models for representing biological pathways. In this work, we built a qualitative PN model for the glycolysis pathway in P. falciparum and analyzed the model for its structural and quantitative properties using PN theory. From PlasmoCyc files, a total of 11 reactions were extracted; 6 of these were reversible and 5 were irreversible. These reactions were catalyzed by a total number of 13 enzymes. We extracted some of the essential reactions in the pathway using PN model, which are the possible drug targets without which the pathway cannot function. This model also helps to improve the understanding of the biological processes within this pathway.