PEnBayes: A Multi-Layered Ensemble Approach for Learning Bayesian Network Structure from Big Data.

Discovering the Bayesian network (BN) structure from big datasets containing rich causal relationships is becoming increasingly valuable for modeling and reasoning under uncertainties in many areas with big data gathered from sensors due to high volume and fast veracity. Most of the current BN structure learning algorithms have shortcomings facing big data. First, learning a BN structure from the entire big dataset is an expensive task which often ends in failure due to memory constraints.

Small angle X-ray scattering-assisted protein structure prediction in CASP13 and emergence of solution structure differences.

Small angle X-ray scattering (SAXS) measures comprehensive distance information on a protein's structure, which can constrain and guide computational structure prediction algorithms. Here, we evaluate structure predictions of 11 monomeric and oligomeric proteins for which SAXS data were collected and provided to predictors in the 13th round of the Critical Assessment of protein Structure Prediction (CASP13). The category for SAXS-assisted predictions made gains in certain areas for CASP13 compared to CASP12.

DFT Fe-O/O-O Vibrational Frequency Calculations over Catalytic Reaction Cycle States in the Dinuclear Center of Cytochrome Oxidase.

Density functional vibrational frequency calculations have been performed on eight geometry optimized cytochrome oxidase (CO) dinuclear center (DNC) reaction cycle intermediates and on the oxymyoglobin (oxyMb) active site. The calculated Fe-O and O-O stretching modes and their frequency shifts along the reaction cycle have been compared with the available resonance Raman (rR) measurements. The calculations support the proposal that in state [Fe-O···Cu] of CO, O binds with Fe in a similar bent end-on geometry to that in oxyMb.

Molecular Graphics: Bridging Structural Biologists and Computer Scientists.

Visualization of molecular structures is one of the most common tasks carried out by structural biologists, typically using software, such as Chimera, COOT, PyMOL, or VMD. In this Perspective article, we outline how past developments in computer graphics and data visualization have expanded the understanding of biomolecular function, and we summarize recent advances that promise to further transform structural biology. We also highlight how progress in molecular graphics has been impeded by communication barriers between two communities: the computer scientists driving these advances, and the structural and computational biologists who stand to benefit.

Assessment of protein assembly prediction in CASP13.

We present the assembly category assessment in the 13th edition of the CASP community-wide experiment. For the second time, protein assemblies constitute an independent assessment category. Compared to the last edition we see a clear uptake in participation, more oligomeric targets released, and consistent, albeit modest, improvement of the predictions quality.

Artificial Intelligence in Drug Treatment.

The most common applications of artificial intelligence (AI) in drug treatment have to do with matching patients to their optimal drug or combination of drugs, predicting drug-target or drug-drug interactions, and optimizing treatment protocols. This review outlines some of the recently developed AI methods aiding the drug treatment and administration process. Selection of the best drug(s) for a patient typically requires the integration of patient data, such as genetics or proteomics, with drug data, like compound chemical descriptors, to score the therapeutic efficacy of drugs.

Assessing Many-Body Effects of Water Self-Ions. II: HO(HO) Clusters.

The importance of many-body effects in the hydration of the hydronium ion (HO) is investigated through a systematic analysis of the many-body expansion of the interaction energy carried out at the coupled-cluster level of theory for the low-lying isomers of HO(HO) clusters with = 1-5. This is accomplished by partitioning individual fragments extracted from the whole clusters into "groups" that are classified by both the number of HO and water molecules and the hydrogen-bonding connectivity within a given fragment. Effects due to the presence of the Zundel ion, (HO), are analyzed by further partitioning fragment groups by the "context" of their parent clusters.

ANKRD9 is a metabolically-controlled regulator of IMPDH2 abundance and macro-assembly.

Members of a large family of Ankyrin Repeat Domain (ANKRD) proteins regulate numerous cellular processes by binding to specific protein targets and modulating their activity, stability, and other properties. The same ANKRD protein may interact with different targets and regulate distinct cellular pathways. The mechanisms responsible for switches in the ANKRDs' behavior are often unknown.

T-BAS Version 2.1: Tree-Based Alignment Selector Toolkit for Evolutionary Placement of DNA Sequences and Viewing Alignments and Specimen Metadata on Curated and Custom Trees.

The Tree-Based Alignment Selector (T-BAS) toolkit combines phylogenetic-based placement of DNA sequences with alignment and specimen metadata visualization tools in an integrative pipeline for analyzing microbial biodiversity. The release of T-BAS version 2.1 makes available reference phylogenies, supports multilocus sequence placements and permits uploading and downloading trees, alignments, and specimen metadata.

Bringing Molecular Dynamics Simulation Data into View.

Molecular dynamics (MD) simulations monitor time-resolved motions of macromolecules. While visualization of MD trajectories allows an instant and intuitive understanding of dynamics and function, so far mainly static representations are provided in the published literature. Recent advances in browser technology may allow for the sharing of trajectories through interactive visualization on the web.

Recognition of early and late stages of bladder cancer using metabolites and machine learning.

Introduction: Bladder cancer (BCa) is one of the most common and aggressive cancers. It is the sixth most frequently occurring cancer in men and its rate of occurrence increases with age. The current method of BCa diagnosis includes a cystoscopy and biopsy.

Time varying causal network reconstruction of a mouse cell cycle.

Background: Biochemical networks are often described through static or time-averaged measurements of the component macromolecules. Temporal variation in these components plays an important role in both describing the dynamical nature of the network as well as providing insights into causal mechanisms. Few methods exist, specifically for systems with many variables, for analyzing time series data to identify distinct temporal regimes and the corresponding time-varying causal networks and mechanisms.

Polycomb repressive 2 complex-Molecular mechanisms of function.

Numerous molecular processes conduct epigenetic regulation of protein transcription to maintain cell specification. In this review, we discuss molecular mechanisms of the Polycomb group of proteins and its enzymatic role in epigenetics. More specifically, we focus on the Polycomb repressive complex 2 (PRC2) and the effects of its repressive marker.

Specific Ion Effects on Hydrogen-Bond Rearrangements in the Halide-Dihydrate Complexes.

Small aqueous ionic clusters represent ideal systems to investigate the microscopic hydrogen-bonding structure and dynamics in ion hydration shells. In this context, halide-dihydrate complexes are the smallest systems where the interplay between halide-water and water-water interactions can be studied simultaneously. Here, quantum molecular dynamics simulations unravel specific ion effects on the temperature-dependent structural transition in X(HO) complexes (X = Cl, Br, and I), which is induced by the breaking of the water-water hydrogen bond.

Topological and functional comparison of community detection algorithms in biological networks.

Background: Community detection algorithms are fundamental tools to uncover important features in networks. There are several studies focused on social networks but only a few deal with biological networks. Directly or indirectly, most of the methods maximize modularity, a measure of the density of links within communities as compared to links between communities.

'Fit-for-purpose?' - challenges and opportunities for applications of blockchain technology in the future of healthcare.

Blockchain is a shared distributed digital ledger technology that can better facilitate data management, provenance and security, and has the potential to transform healthcare. Importantly, blockchain represents a data architecture, whose application goes far beyond Bitcoin - the cryptocurrency that relies on blockchain and has popularized the technology. In the health sector, blockchain is being aggressively explored by various stakeholders to optimize business processes, lower costs, improve patient outcomes, enhance compliance, and enable better use of healthcare-related data.

Assessment of Density Functional Theory in Predicting Interaction Energies between Water and Polycyclic Aromatic Hydrocarbons: from Water on Benzene to Water on Graphene.

The interactions of water with polycyclic aromatic hydrocarbons, from benzene to graphene, are investigated using various exchange-correlation functionals selected across the hierarchy of density functional theory (DFT) approximations. The accuracy of the different functionals is assessed through comparisons with random phase approximation (RPA) and coupled-cluster with single, double, and perturbative triple excitations [CCSD(T)] calculations. Diffusion Monte Carlo (DMC) data reported in the literature are also used for comparison.

A demonstration of modularity, reuse, reproducibility, portability and scalability for modeling and simulation of cardiac electrophysiology using Kepler Workflows.

Multi-scale computational modeling is a major branch of computational biology as evidenced by the US federal interagency Multi-Scale Modeling Consortium and major international projects. It invariably involves specific and detailed sequences of data analysis and simulation, often with multiple tools and datasets, and the community recognizes improved modularity, reuse, reproducibility, portability and scalability as critical unmet needs in this area. Scientific workflows are a well-recognized strategy for addressing these needs in scientific computing.

Ion-mediated hydrogen-bond rearrangement through tunnelling in the iodide-dihydrate complex.

A microscopic picture of hydrogen-bond structure and dynamics in ion hydration shells remains elusive. Small ion-dihydrate molecular complexes are ideal systems with which to investigate the interplay and competition between ion-water and water-water interactions. Here, state-of-the-art quantum dynamics simulations provide evidence for tunnelling in hydrogen-bond rearrangements in the iodide-dihydrate complex and show that it can be controlled through isotopic substitutions.

A Comparison of E-Cigarette Use Patterns and Smoking Cessation Behavior among Vapers by Primary Place of Purchase.

Background: E-cigarettes are purchased through multiple channels, including general retail, online, and specialty smoke and vape shops. We examine how e-cigarette users' primary purchase place relates to e-cigarette use and smoking cessation behaviors.

Methods: Probability-based samples of the U.

High frequency of shared clonotypes in human B cell receptor repertoires.

The human genome contains approximately 20 thousand protein-coding genes, but the size of the collection of antigen receptors of the adaptive immune system that is generated by the recombination of gene segments with non-templated junctional additions (on B cells) is unknown-although it is certainly orders of magnitude larger. It has not been established whether individuals possess unique (or private) repertoires or substantial components of shared (or public) repertoires. Here we sequence recombined and expressed B cell receptor genes in several individuals to determine the size of their B cell receptor repertoires, and the extent to which these are shared between individuals.

BioJava 5: A community driven open-source bioinformatics library.

BioJava is an open-source project that provides a Java library for processing biological data. The project aims to simplify bioinformatic analyses by implementing parsers, data structures, and algorithms for common tasks in genomics, structural biology, ontologies, phylogenetics, and more. Since 2012, we have released two major versions of the library (4 and 5) that include many new features to tackle challenges with increasingly complex macromolecular structure data.