The cognitive effects of computational thinking: A systematic review and meta-analytic study

In this paper, we review and meta-analyze the findings of experimental studies published between 2006 and 2022 that examined the effects of coding and programming interventions on children's core and higher order executive functions (response inhibition, working memory, cognitive flexibility, planning and problem solving). The systematic review and meta-analysis aimed to address three research questions: 1) Which executive functions are most impacted by the teaching of CT? 2) Which instructional modality (educational robotics/virtual coding/unplugged coding) is most effective in enhancing executive function skills in learners aged 4–16 years? and 3) Does the cognitive effectiveness of coding vary with children's age? A total of 19 studies with 1523 participants met the selection criteria for the systematic review. The meta-analysis included 11 of those studies. The results reveal beneficial effects of structured virtual and tangible coding (educational robotics) activities for preschoolers and first graders, and significant effects of more unstructured virtual coding activities (e.g., Scratch-based) for older students. A multivariate fixed-effects model meta-analysis shows that the teaching of coding significantly improves problem-solving with the highest effect (dppc2 = 0.89), but also planning (dppc2 = 0.36), and inhibition and working memory with lower effects (dppc2 = 0.17, dppc2 = 0.20)

Uni-QSAR: an Auto-ML Tool for Molecular Property Prediction

Recently deep learning based quantitative structure-activity relationship (QSAR) models has shown surpassing performance than traditional methods for property prediction tasks in drug discovery. However, most DL based QSAR models are restricted to limited labeled data to achieve better performance, and also are sensitive to model scale and hyper-parameters. In this paper, we propose Uni-QSAR, a powerful Auto-ML tool for molecule property prediction tasks. Uni-QSAR combines molecular representation learning (MRL) of 1D sequential tokens, 2D topology graphs, and 3D conformers with pretraining models to leverage rich representation from large-scale unlabeled data. Without any manual fine-tuning or model selection, Uni-QSAR outperforms SOTA in 21/22 tasks of the Therapeutic Data Commons (TDC) benchmark under designed parallel workflow, with an average performance improvement of 6.09\%. Furthermore, we demonstrate the practical usefulness of Uni-QSAR in drug discovery domains

Concepts to Interfere with Protein-Protein Complex Formations: Data Analysis, Structural Evidence and Strategies for Finding Small Molecule Modulators

(1) Analyzing protein-protein interactions at the atomic level is critical for our understanding of the principles governing the interactions involved in protein-protein recognition. For this purpose descriptors explaining the nature of different protein-protein complexes are desirable. In this work, we introduce Epic Protein Interface Classification (EPIC) as a framework handling the preparation, processing, and analysis of protein-protein complexes for classification with machine learning algorithms. We applied four different machine learning algorithms: Support Vector Machines (SVM), C4.5 Decision Trees, K Nearest Neighbors (KNN), and Naïve Bayes (NB) algorithm in combination with three feature selection methods, Filter (Relief F), Wrapper, and Genetic Algorithms (GA) to extract discriminating features from the protein-protein complexes. To compare protein-protein complexes to each other, we represented the physicochemical characteristics of their interfaces in four different ways, using two different atomic contact vectors (ACVs), DrugScore pair potential vectors (DPV) and SFCscore descriptor vectors (SDV). We classified two different datasets: (A) 172 protein-protein complexes comprising 96 monomers, forming contacts enforced by the crystallographic packing environment (crystal contacts), and 76 biologically functional homodimer complexes; (B) 345 protein-protein complexes containing 147 permanent complexes and 198 transient complexes. We were able to classify up to 94.8% of the packing enforced/functional and up to 93.6% of the permanent/transient complexes correctly. Furthermore, we were able to extract relevant features from the different protein-protein complexes and introduce an approach for scoring the importance of the extracted features. (2) Since protein-protein interactions play pivotal role in the communication on the molecular level in virtually every biological system and process, the search and design for modulators of such interactions is of utmost interest. In recent years many inhibitors for specific protein-protein interactions have been developed, however, in only a few cases, small and druglike molecules are able to interfere the complex formation of proteins. On the other hand, there a several small molecules known to modulate protein-protein interactions by means of stabilizing an already assembled complex. To achieve this goal, a ligand is binding to a pocket, which is located rim-exposed at the interface of the interacting proteins, e.g. as the phytotoxin Fusicoccin, which stabilizes the interaction of plant H+-ATPase and 14-3-3 protein by nearly a factor of 100. To suggest alternative leads, we performed a virtual screening campaign to discover new molecules putatively stabilizing this complex. Furthermore, we screen a dataset of 198 transient recognition protein-protein complexes for cavities, which are located rim-exposed at their interfaces. We provide evidence for high similarity between such rim-exposed cavities and usual ligand accommodating active sites of enzymes. This analysis suggests that rim-exposed cavities at protein-protein interfaces are druggable targets. Therefore, the principle of stabilizing protein-protein interactions seems to be a promising alternative to the approach of the competitive inhibition of such interactions by small molecules. (3) AffinDB is a database of affinity data for structurally resolved protein-ligand complexes from the PDB. It is freely accessible at http://www.agklebe.de/affinity. Affinity data are collected from the scientific literature, both from primary sources describing the original experimental work of affinity determination and from secondary references which report affinity values determined by others. AffinDB currently contains over 730 affinity entries covering more than 450 different protein-ligand complexes. Besides the affinity value, PDB summary information and additional data are provided, including the experimental conditions of the affinity measurement (if available in the corresponding reference); 2D drawing, SMILES code, and molecular weight of the ligand; links to other databases, and bibliographic information. AffinDB can be queried by PDB code or by any combination of affinity range, temperature and pH-value of the measurement, ligand molecular weight, and publication data (author, journal, year). Search results can be saved as tabular reports in text files. The database is supposed to be a valuable resource for researchers interested in biomolecular recognition and the development of tools for correlating structural data with affinities, as needed, for example, in structure-based drug design

Concepts to Interfere with Protein-Protein Complex Formations: Data Analysis, Structural Evidence and Strategies for Finding Small Molecule Modulators

(1) Analyzing protein-protein interactions at the atomic level is critical for our understanding of the principles governing the interactions involved in protein-protein recognition. For this purpose descriptors explaining the nature of different protein-protein complexes are desirable. In this work, we introduce Epic Protein Interface Classification (EPIC) as a framework handling the preparation, processing, and analysis of protein-protein complexes for classification with machine learning algorithms. We applied four different machine learning algorithms: Support Vector Machines (SVM), C4.5 Decision Trees, K Nearest Neighbors (KNN), and Naïve Bayes (NB) algorithm in combination with three feature selection methods, Filter (Relief F), Wrapper, and Genetic Algorithms (GA) to extract discriminating features from the protein-protein complexes. To compare protein-protein complexes to each other, we represented the physicochemical characteristics of their interfaces in four different ways, using two different atomic contact vectors (ACVs), DrugScore pair potential vectors (DPV) and SFCscore descriptor vectors (SDV). We classified two different datasets: (A) 172 protein-protein complexes comprising 96 monomers, forming contacts enforced by the crystallographic packing environment (crystal contacts), and 76 biologically functional homodimer complexes; (B) 345 protein-protein complexes containing 147 permanent complexes and 198 transient complexes. We were able to classify up to 94.8% of the packing enforced/functional and up to 93.6% of the permanent/transient complexes correctly. Furthermore, we were able to extract relevant features from the different protein-protein complexes and introduce an approach for scoring the importance of the extracted features. (2) Since protein-protein interactions play pivotal role in the communication on the molecular level in virtually every biological system and process, the search and design for modulators of such interactions is of utmost interest. In recent years many inhibitors for specific protein-protein interactions have been developed, however, in only a few cases, small and druglike molecules are able to interfere the complex formation of proteins. On the other hand, there a several small molecules known to modulate protein-protein interactions by means of stabilizing an already assembled complex. To achieve this goal, a ligand is binding to a pocket, which is located rim-exposed at the interface of the interacting proteins, e.g. as the phytotoxin Fusicoccin, which stabilizes the interaction of plant H+-ATPase and 14-3-3 protein by nearly a factor of 100. To suggest alternative leads, we performed a virtual screening campaign to discover new molecules putatively stabilizing this complex. Furthermore, we screen a dataset of 198 transient recognition protein-protein complexes for cavities, which are located rim-exposed at their interfaces. We provide evidence for high similarity between such rim-exposed cavities and usual ligand accommodating active sites of enzymes. This analysis suggests that rim-exposed cavities at protein-protein interfaces are druggable targets. Therefore, the principle of stabilizing protein-protein interactions seems to be a promising alternative to the approach of the competitive inhibition of such interactions by small molecules. (3) AffinDB is a database of affinity data for structurally resolved protein-ligand complexes from the PDB. It is freely accessible at http://www.agklebe.de/affinity. Affinity data are collected from the scientific literature, both from primary sources describing the original experimental work of affinity determination and from secondary references which report affinity values determined by others. AffinDB currently contains over 730 affinity entries covering more than 450 different protein-ligand complexes. Besides the affinity value, PDB summary information and additional data are provided, including the experimental conditions of the affinity measurement (if available in the corresponding reference); 2D drawing, SMILES code, and molecular weight of the ligand; links to other databases, and bibliographic information. AffinDB can be queried by PDB code or by any combination of affinity range, temperature and pH-value of the measurement, ligand molecular weight, and publication data (author, journal, year). Search results can be saved as tabular reports in text files. The database is supposed to be a valuable resource for researchers interested in biomolecular recognition and the development of tools for correlating structural data with affinities, as needed, for example, in structure-based drug design

NOVEL ALGORITHMS AND TOOLS FOR LIGAND-BASED DRUG DESIGN

Computer-aided drug design (CADD) has become an indispensible component in modern drug discovery projects. The prediction of physicochemical properties and pharmacological properties of candidate compounds effectively increases the probability for drug candidates to pass latter phases of clinic trials. Ligand-based virtual screening exhibits advantages over structure-based drug design, in terms of its wide applicability and high computational efficiency. The established chemical repositories and reported bioassays form a gigantic knowledgebase to derive quantitative structure-activity relationship (QSAR) and structure-property relationship (QSPR). In addition, the rapid advance of machine learning techniques suggests new solutions for data-mining huge compound databases. In this thesis, a novel ligand classification algorithm, Ligand Classifier of Adaptively Boosting Ensemble Decision Stumps (LiCABEDS), was reported for the prediction of diverse categorical pharmacological properties. LiCABEDS was successfully applied to model 5-HT1A ligand functionality, ligand selectivity of cannabinoid receptor subtypes, and blood-brain-barrier (BBB) passage. LiCABEDS was implemented and integrated with graphical user interface, data import/export, automated model training/ prediction, and project management. Besides, a non-linear ligand classifier was proposed, using a novel Topomer kernel function in support vector machine. With the emphasis on green high-performance computing, graphics processing units are alternative platforms for computationally expensive tasks. A novel GPU algorithm was designed and implemented in order to accelerate the calculation of chemical similarities with dense-format molecular fingerprints. Finally, a compound acquisition algorithm was reported to construct structurally diverse screening library in order to enhance hit rates in high-throughput screening

Data science, analytics and artificial intelligence in e-health : trends, applications and challenges

Acknowledgments. This work has been partially supported by the Divina Pastora Seguros company.More than ever, healthcare systems can use data, predictive models, and intelligent algorithms to optimize their operations and the service they provide. This paper reviews the existing literature regarding the use of data science/analytics methods and artificial intelligence algorithms in healthcare. The paper also discusses how healthcare organizations can benefit from these tools to efficiently deal with a myriad of new possibilities and strategies. Examples of real applications are discussed to illustrate the potential of these methods. Finally, the paper highlights the main challenges regarding the use of these methods in healthcare, as well as some open research lines

Assessing the Utility of a Video-Based Motion Capture Alternative in the Assessment of Lumbar Spine Planar Angular Joint Kinematics

Markerless motion capture is a novel technique to measure human movement kinematics. The purpose of this research is to evaluate the markerless algorithm, DeepLabCut (DLC) against a 3D motion capture system (Vicon Motion Systems Ltd., Oxford, UK) in the analysis of planar spine and elbow flexion-extension movement. Data were acquired concurrently from DLC and Vicon for all movements. A novel DLC model was trained using data derived from a subset of participants (training group). Accuracy and precision were assessed from data derived from the training group as well as in a new set of participants (testing group). Two-way SPM ANOVAs were used to detect significant differences between the training vs. testing sets, capture methods (Vicon vs. DLC), as well as potential higher order interaction effect between these independent variables in the estimation of flexion extension angles and variability. No significant differences were observed in any planar angles, nor were any higher order interactions observed between each motion capture modality and the training vs. testing datasets. Bland Altman plots were also generated to depict the mean bias and level of agreement between DLC and Vicon for both training, and testing datasets. Supplemental analyses, suggest that these results are partially affected by the alignment of each participant’s body segments with respect to each planar reference frame. This research suggests that DLC-derived planar kinematics of both the elbow and lumbar spine are of acceptable accuracy and precision when compared to conventional laboratory gold-standards (Vicon)