Enhancing the content of phycoerythrin through the application of microplastics from Porphyridium cruentum produced in wastewater using machine learning methods.

Microalgae can produce secondary metabolites like phycoerythrin (Phy). The effects of some microplastics (MPs), wastewater (WW), and light intensity (LI) parameters, including complex data sets, on Phy concentration from Porphyridium cruentum were investigated using machine learning methods in this study. Also, the deep learning (DL) model was developed to get the maximum phy concentration from the dataset.

Production and use of Scenedesmus acuminatus biomass in synthetic municipal wastewater for integrated biorefineries.

Bioethanol production from algal biomass is a promising alternative for sustainable biofuel production. Algae possess a high photosynthetic capacity and an adaptive ability to thrive under harsh environmental conditions. The potential properties of Scenedesmus acuminatus CCALA 436 were assessed in this research for its bioethanol efficiency, and the effects of growing the algae in wastewater and at different concentrations of mepiquat chloride were studied.

Sequential modelling for carbohydrate and bioethanol production from Chlorella saccharophila CCALA 258: a complementary experimental and theoretical approach for microalgal bioethanol production.

Bioethanol production from microalgal biomass is an attractive concept, and theoretical methods by which bioenergy can be produced indicate saving in both time and efficiency. The aim of the present study was to investigate the efficiencies of carbohydrate and bioethanol production by Chlorella saccharophila CCALA 258 using experimental, semiempirical, and theoretical methods, such as response surface methods (RSMs) and an artificial neural network (ANN) through sequential modeling. In addition, the interactive response surface modeling for determining the optimum conditions for the variables was assessed.

A Drug Decision Support System for Developing a Successful Drug Candidate Using Machine Learning Techniques.

Background: Virtual screening of candidate drug molecules using machine learning techniques plays a key role in pharmaceutical industry to design and discovery of new drugs. Computational classification methods can determine drug types according to the disease groups and distinguish approved drugs from withdrawn ones.

Introduction: Classification models developed in this study can be used as a simple filter in drug modelling to eliminate potentially inappropriate molecules in the early stages.

Classification of nervous system withdrawn and approved drugs with ToxPrint features via machine learning strategies.

Background And Objectives: Early-phase virtual screening of candidate drug molecules plays a key role in pharmaceutical industry from data mining and machine learning to prevent adverse effects of the drugs. Computational classification methods can distinguish approved drugs from withdrawn ones. We focused on 6 data sets including maximum 110 approved and 110 withdrawn drugs for all and nervous system diseases to distinguish approved drugs from withdrawn ones.

Thermo-resistant green microalgae for effective biodiesel production: isolation and characterization of unialgal species from geothermal flora of Central Anatolia.

Oil content and composition, biomass productivity and adaptability to different growth conditions are important parameters in selecting a suitable microalgal strain for biodiesel production. Here, we describe isolation and characterization of three green microalgal species from geothermal flora of Central Anatolia. All three isolates, namely, Scenedesmus sp.