Machine Learning Priority Rule (MLPR) For Solving Resource-Constrained Project Scheduling Problems

This paper introduces a machine learning priority rule for solving non-preemptive resource-constrained project scheduling problems (RCPSP). The objective is to find a schedule of the project’s tasks that minimizes the total completion time of the project satisfying the precedence and resource constraints. Priority rule based scheduling technique is a scheduling method for constructing feasible schedules of the jobs of projects. This approach is made up of two parts: a priority rule to determine the activity list and a schedule generation scheme which constructs the feasible schedule of the constructed activity list. Different scheduling methods use one of these schemes to construct schedules to obtain the overall project completion time. Quite a number of priority rules are available; selecting the best one for a particular input problem is extremely difficult. We present a machine learning priority rule which assembles a set of priority rules, and uses machine learning strategies to choose the one with the best performance at every point in time to construct an activity list of a project. The one with better performance is used most frequently. This removes the problem of manually searching for the best priority rule amongst the dozens of rules that are available. We used our approach to solve a fictitious project with 11 activities from Pm Knowledge Center. Four priority rules were combined. We used serial schedule generation scheme to generate our schedules. Our result showed that the total completion time of the project obtained with our approach competes favorably with the completion times gotten with the component priority rules. We then went further and compared our algorithm with 9 other available priority rules. Our results showed that the completion time got using our algorithm compete favorably with the total 13 priority rules available in the literature

Solving Project Delays and Abandonment Using Hybrid Resource-Constrained Project Scheduling Models

Resource-constrained project scheduling models either the single-mode or the multi-mode case finds minimum schedule that minimizes the completion time of a project with constant per period renewable resource. That the level of provided resources, each period must be constant, does not reflect a real-life situation and hence makes these models inappropriate for solving project delays and abandonment. We present a Hybrid resource-constrained project scheduling problem (Hybrid RCPSP), the single-mode case and the multi-mode case for solving delays and abandonment of projects. These models are combination of the existing single-mode and multi-mode RCPSP models with some added assumptions. Our method essentially formulates the network project as a Hybrid RCPSP (single-mode or the multi-mode) and then finds the minimal schedule that minimizes the completion time of the project using priority rule based scheduling technique, while the level of the renewable resource availability varies. The idea is that if a completion time of the project can be minimized then, that project cannot be delayed or abandoned. We performed our method on a real-life building construction project (a fenced three-bedroom bungalow), a fictitious single-mode and multi-mode network projects. Our result of the real-life building construction project, show that to solve project delays and abandonment, the level of per period available resource should vary and our result of the fictitious Single-Mode and Multi-mode RCPSP show that no matter how small (even at zero level in some time periods), the per period amount and how long the length of the period, the projects will not be delayed or even abandone

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

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

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

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

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

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. Thirtytwo (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 approac