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Image classification is a complex process and an important direction in the field of image processing. Image classification methods require learning and training stages. Using machine learning classification models in image classification gives better results. Decision Tree, Random Forest, Gradient Boosting, Bagging Classifier, Multi-Layer Perceptron (MLP) Classifier, and Support Vector Machine (SVM) are different machine-learning classification models. The goal of this paper is to analyze the machine learning classification models. These models classify 12 kinds of plant seedlings, of which 3 are crop seedlings and 9 are weed seedlings. This paper suggests that, when using a V2 Plant Seedlings dataset, the accuracy of SVM is 0.71 and the accuracy of other models is less compared to SVM. The experimental results in this paper show that the machine learning model SVM has a better solution effect and higher recognition accuracy. This paper focuses on model building, training, and assessing the quality of the model by generating a confusion matrix and a classification report. [ABSTRACT FROM AUTHOR]

Aims: Neuronal disorders have affected more than 15% of the world's population, signifying the importance of continued design and development of drugs that can cross the Blood-Brain Barrier (BBB)., Background: BBB limits the permeability of external compounds by 98% to maintain and regulate brain homeostasis. Hence, BBB permeability prediction is vital to predict the activity of a drug-like substance., Objective: Here, we report about developing BBBper (Blood-Brain Barrier permeability prediction) using machine learning tool., Method: A supervised machine learning-based online tool, based on physicochemical parameters to predict the BBB permeability of given chemical compounds was developed. The user-end webpage was developed in HTML and linked with back-end server by a python script to run user queries and results., Result: BBBper uses a random forest algorithm at the back end, showing 97% accuracy on the external dataset, compared to 70-92% accuracy of currently available web-based BBB permeability prediction tools., Conclusion: The BBBper web tool is freely available at http://bbbper.mdu.ac.in., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Epileptic seizures are directly linked with an anomalous influx of extracellular calcium or sodium anions through voltage-gated channels disturb the chemical and electrical gradients, resulting in seizures or jerking moments. Voltage-gated calcium channel (VGCC) subunit α2δ-1 is the binding site for gabapentinoids used to treat epilepsy and neuropathic pain. However, this class of drugs showed severe side effects associated with CNS and respiratory depression. Hence, we screened a total of 2583 phytochemicals from the Comprehensive Marine Natural Products Database for their drug likeliness and pharmacokinetics (ADME/T) properties. The selected phytochemicals were docked with the VGCC α2δ-1 protein target and the marketed AED Pregabalin is used as standard. The docking results helped to select 45 docked compounds with better binding affinity, among which Acanthiline A showed the maximum binding affinity with the binding energy of -11.9 kcal/mol, thus reflecting its potential anti-epileptic activity., Competing Interests: The authors declare no conflict of interest., (© 2024 Biomedical Informatics.)

α-Synuclein aggregation into toxic oligomeric species is central to Parkinson's disease pathogenesis. Anle138b is a recently identified inhibitor of α-synuclein oligomerization showing promise in preclinical studies. This study employed computational approaches to elucidate Anle138b's mechanism of oligomer-specific action. The inhibitory potential of Anle138b against α-synuclein oligomers was evaluated by performing molecular docking studies using AutoDock Tools, followed by their binding pocket analysis. Further, protein-protein docking studies were performed using Hex8.0 to validate the aggregation inhibitory potential of Anle138b. Molecular docking revealed increasing binding affinity of Anle138b against higher order α-synuclein oligomers (dimer to decamer). Anle138b occupied oligomeric cavity and interacted with residues Thr54, Gly73, Val74 and Thr75 across several oligomers. Protein-protein docking showed that Anle138b interferes with α-synuclein decamer formation. These results highlight the oligomer-directed inhibitory mechanism of Anle138b, without hindering the monomeric forms and provide molecular insights to advance its therapeutic development for Parkinson's and related synucleinopathies., Competing Interests: The authors declare no conflict of interest., (© 2024 Biomedical Informatics.)

Alzheimer's disease (AD) is the leading cause of dementia worldwide with therapeutic lacunae till date. The beta-amyloid (Aβ) accumulation triggers AD pathogenesis, though clinical trials lowering Aβ have not altered disease outcomes suggesting other interacting factors to be identified for drug design of AD. Therefore, it is of interest to identify potential hub proteins interlinked with disease-driving pathways using a network-based approach for AD therapeutic designing. Literature mining was done to identify proteins implicated in AD etiology. Protein-protein interactions (PPIs) were retrieved from the STRING database and merged into a single network using Cytoscape 3.10.1. The hub proteins involved in AD etiology were predicted based on the topological algorithms of CytoHubba. Six major proteins, with STRING database identifiers - APP, BACE1, PSEN1, MAPT, APOE4 and TREM2, were identified to be involved in AD pathogenesis. The merged network of PPIs of these proteins contained 51 nodes and 211 edges, as predicted by Analyzer module of Cytoscape. The Amyloid precursor protein (APP) emerged as the highest-scoring hub protein across multiple centrality measures and topological algorithms. Thus, current data provides evidence to support the ongoing investigation of APP's multifaceted functions and therapeutic potential for AD., Competing Interests: The authors declare no conflict of interest., (© 2024 Biomedical Informatics.)

Alzheimer's disease (AD), a debilitating neurodegenerative condition, is characterized by progressive cognitive decline brought about by the deposition of amyloid beta (Aβ) plaques in the brain initiates downstream neuronal dysfunction and death in AD pathogenesis. The β-secretase (BACE-1) enzyme plays a crucial role in generating Aβ from amyloid precursor protein (APP). Hence, we report the virtual screening of marine phytochemicals as BACE-1 inhibitors. 2583 compounds, retrieved from Comprehensive Marine Natural Product Database (CMNPD), were primarily screened for drug-likeliness and blood-brain barrier permeability using admetSAR 2.0 and in-house BBBper tool and resulted in a total of 635 phytochemicals, selected for further docking studies using BACE-1 as target receptor and Atabecestat as standard BACE-1 inhibitor. Seven of 635 compounds docked against BACE-1, showed better binding affinities than Atabecestat, with the red algal metabolite lactodehydrothyrsiferol showing lowest binding energy of -10.83 kcal/mol. These compounds are worth investigating further to assess their neuroprotective efficacy and pharmacokinetic properties. The study also provides a rational framework to uncover novel pharmacophores from marine sources for AD therapy acting through BACE-1 inhibition., Competing Interests: The authors declare no conflict of interest., (© 2024 Biomedical Informatics.)

Epilepsy is one of the most common neurological disorders, affecting millions of patients with a substantial economic and human burden. About 30-40% of epileptic patients remain un-treated after the therapeutic option. Genetic or idiopathic epilepsy count about 40% of total epilepsy patients, showing a maximum percentage for drug-resistant epilepsy. Since the last century basic approach to understanding disease progression and drug discovery has been through the prism, exploring all possible causes and treatment options. Here we report about the gene expression-based drug repositioning study for epilepsy. Epilepsy gene expression data was retrieved from the Gene Expression Omnibus database, while drugs-associated gene expression data was retrieved from the Connectivity map (CMAP). The study predicted309 drug compounds which can alter genetic epilepsy-mediated gene signature using an in-house developed R-script. These compounds were docked against identified epilepsy targets- Voltage-gated sodium channel subunit α2 (Nav1.2); GABA receptor α1-β1; and Voltage-gated calcium channel α1G (Cav3.1)using Carbamazepine, Clonazepam, and Pregabalin as standard drugs, respectively. Twenty-one predicted drug compounds showed better binding affinity than respective standards against the selected epileptic receptors. Among these drug compounds, Ergocalciferol, Oxaprozin, Flunarizine, Triprolidine and Cyproheptadine have been previously reported for anti-epileptic activities and can be potential hits to target idiopathic epilepsy., (© 2022 Biomedical Informatics.)