General Workflow Research Articles

Pocket Crafter: a 3D generative modeling based workflow for the rapid generation of hit molecules in drug discovery

We present a user-friendly molecular generative pipeline called Pocket Crafter, specifically designed to facilitate hit finding activity in the drug discovery process. This workflow utilized a three-dimensional (3D) generative modeling method Pocket2Mol, for the de novo design of molecules in spatial perspective for the targeted protein structures, followed by filters for chemical-physical properties and drug-likeness, structure–activity relationship analysis, and clustering to generate top virtual hit scaffolds. In our WDR5 case study, we acquired a focused set of 2029 compounds after a targeted searching within Novartis archived library based on the virtual scaffolds. Subsequently, we experimentally profiled these compounds, resulting in a novel chemical scaffold series that demonstrated activity in biochemical and biophysical assays. Pocket Crafter successfully prototyped an effective end-to-end 3D generative chemistry-based workflow for the exploration of new chemical scaffolds, which represents a promising approach in early drug discovery for hit identification.

Benchmarking feature selection and feature extraction methods to improve the performances of machine-learning algorithms for patient classification using metabolomics biomedical data

ObjectiveClassification tasks are an open challenge in the field of biomedicine. While several machine-learning techniques exist to accomplish this objective, several peculiarities associated with biomedical data, especially when it comes to omics measurements, prevent their use or good performance achievements. Omics approaches aim to understand a complex biological system through systematic analysis of its content at the molecular level. On the other hand, omics data are heterogeneous, sparse and affected by the classical “curse of dimensionality” problem, i.e. having much fewer observation, samples (n) than omics features (p). Furthermore, a major problem with multi-omics data is the imbalance either at the class or feature level. The objective of this work is to study whether feature extraction and/or feature selection techniques can improve the performances of classification machine-learning algorithms on omics measurements. Methods:Among all omics, metabolomics has emerged as a powerful tool in cancer research, facilitating a deeper understanding of the complex metabolic landscape associated with tumorigenesis and tumor progression. Thus, we selected three publicly available metabolomics datasets, and we applied several feature extraction techniques both linear and non-linear, coupled or not with feature selection methods, and evaluated the performances regarding patient classification in the different configurations for the three datasets. Results:We provide general workflow and guidelines on when to use those techniques depending on the characteristics of the data available. To further test the extension of our approach to other omics data, we have included a transcriptomics and a proteomics data. Overall, for all datasets, we showed that applying supervised feature selection improves the performances of feature extraction methods for classification purposes. Scripts used to perform all analyses are available at: https://github.com/Plant-Net/Metabolomic_project/.

DiffraPy: An open-source Python software for seismic diffraction imaging

Seismic diffraction events, often overlooked as noise in conventional seismic imaging methods, contain valuable high-resolution information about subsurface structures and heterogeneities. Furthermore, the diffractive component of the wavefield is masked by the stronger reflection events. Although the literature presents innovative methods for separating diffracted events from the total wavefield, there are currently limited options of open-source alternatives in seismic diffraction processing and imaging. In this study, we introduce DiffraPy, an open-source Python package specifically designed for performing both conventional and diffraction-oriented migrations. The main workflow involves implementing an anti-stationary phase filter in the Kirchhoff migration for enhancing energy outside the Fresnel zone. Additionally, we propose using the semblance matrix obtained from the dip field calculation as an additional weight in conventional migration. The code provides functionality for forward modeling seismic data, allowing for new testing and experimentation. We demonstrate the general workflow of the code using a toy model and evaluate its capabilities on a synthetic salt model. In both cases, the program successfully suppressed the majority of reflected energy in the diffraction-oriented image. The final image highlights the responses of small lenses, discontinuities, and faults, providing valuable insights into the subsurface for interpretation. Despite the method sensitivity to variations in the correct velocity model, we demonstrate that our code is capable of imaging diffractors even when utilizing incorrect migration velocities of up to 5%. Our wavenumber analysis reveals the diffraction image preservation of smaller wavelengths, indicating higher resolution compared to conventional products. Future implementations may include extension to 3D datasets and improve the computational efficiency of our code. We expect our code to offer the geosciences community the first freely accessible and well-documented Python alternative for testing, reproducing, and exploring diffraction imaging examples.

A passage-free, simplified, and scalable novel method for iPSC generation in three-dimensional culture

Induced pluripotent stem cells (iPSCs) have immense potential for use in disease modeling, etiological studies, and drug discovery. However, the current workflow for iPSC generation and maintenance poses challenges particularly during the establishment phase when specialized skills are required. Although three-dimensional culture systems offer scalability for maintaining established iPSCs, the enzymatic dissociation step is complex and time-consuming. In this study, a novel approach was developed to address these challenges by enabling iPSC generation, maintenance, and differentiation without the need for two-dimensional culture or enzymatic dissociation. This streamlined method offers a more convenient workflow, reduces variability and labor for technicians, and opens up avenues for advancements in iPSC research and broader applications.

Cryo-electron microscopy-based drug design.

Structure-based drug design (SBDD) has gained popularity owing to its ability to develop more potent drugs compared to conventional drug-discovery methods. The success of SBDD relies heavily on obtaining the three-dimensional structures of drug targets. X-ray crystallography is the primary method used for solving structures and aiding the SBDD workflow; however, it is not suitable for all targets. With the resolution revolution, enabling routine high-resolution reconstruction of structures, cryogenic electron microscopy (cryo-EM) has emerged as a promising alternative and has attracted increasing attention in SBDD. Cryo-EM offers various advantages over X-ray crystallography and can potentially replace X-ray crystallography in SBDD. To fully utilize cryo-EM in drug discovery, understanding the strengths and weaknesses of this technique and noting the key advancements in the field are crucial. This review provides an overview of the general workflow of cryo-EM in SBDD and highlights technical innovations that enable its application in drug design. Furthermore, the most recent achievements in the cryo-EM methodology for drug discovery are discussed, demonstrating the potential of this technique for advancing drug development. By understanding the capabilities and advancements of cryo-EM, researchers can leverage the benefits of designing more effective drugs. This review concludes with a discussion of the future perspectives of cryo-EM-based SBDD, emphasizing the role of this technique in driving innovations in drug discovery and development. The integration of cryo-EM into the drug design process holds great promise for accelerating the discovery of new and improved therapeutic agents to combat various diseases.

Tutorial on survival modeling with applications to omics data.

Identification of genomic, molecular and clinical markers prognostic of patient survival is important for developing personalized disease prevention, diagnostic and treatment approaches. Modern omics technologies have made it possible to investigate the prognostic impact of markers at multiple molecular levels, including genomics, epigenomics, transcriptomics, proteomics and metabolomics, and how these potential risk factors complement clinical characterization of patient outcomes for survival prognosis. However, the massive sizes of the omics datasets, along with their correlation structures, pose challenges for studying relationships between the molecular information and patients' survival outcomes. We present a general workflow for survival analysis that is applicable to high-dimensional omics data as inputs when identifying survival-associated features and validating survival models. In particular, we focus on the commonly used Cox-type penalized regressions and hierarchical Bayesian models for feature selection in survival analysis, which are especially useful for high-dimensional data, but the framework is applicable more generally. A step-by-step R tutorial using The Cancer Genome Atlas survival and omics data for the execution and evaluation of survival models has been made available at https://ocbe-uio.github.io/survomics.

Designing workflows for materials characterization

Experimental science is enabled by the combination of synthesis, imaging, and functional characterization organized into evolving discovery loop. Synthesis of new material is typically followed by a set of characterization steps aiming to provide feedback for optimization or discover fundamental mechanisms. However, the sequence of synthesis and characterization methods and their interpretation, or research workflow, has traditionally been driven by human intuition and is highly domain specific. Here, we explore concepts of scientific workflows that emerge at the interface between theory, characterization, and imaging. We discuss the criteria by which these workflows can be constructed for special cases of multiresolution structural imaging and functional characterization, as a part of more general material synthesis workflows. Some considerations for theory–experiment workflows are provided. We further pose that the emergence of user facilities and cloud labs disrupts the classical progression from ideation, orchestration, and execution stages of workflow development. To accelerate this transition, we propose the framework for workflow design, including universal hyperlanguages describing laboratory operation, ontological domain matching, reward functions and their integration between domains, and policy development for workflow optimization. These tools will enable knowledge-based workflow optimization; enable lateral instrumental networks, sequential and parallel orchestration of characterization between dissimilar facilities; and empower distributed research.

Autonomous convergence of STM control parameters using Bayesian optimization

Scanning tunneling microscopy (STM) is a widely used tool for atomic imaging of novel materials and their surface energetics. However, the optimization of the imaging conditions is a tedious process due to the extremely sensitive tip–surface interaction, thus limiting the throughput efficiency. In this paper, we deploy a machine learning (ML)-based framework to achieve optimal atomically resolved imaging conditions in real time. The experimental workflow leverages the Bayesian optimization (BO) method to rapidly improve the image quality, defined by the peak intensity in the Fourier space. The outcome of the BO prediction is incorporated into the microscope controls, i.e., the current setpoint and the tip bias, to dynamically improve the STM scan conditions. We present strategies to either selectively explore or exploit across the parameter space. As a result, suitable policies are developed for autonomous convergence of the control parameters. The ML-based framework serves as a general workflow methodology across a wide range of materials.

Conceptual Framework for Flexible Workflow Generation in Federated ERP Systems Mediated by a FERP Mall

Conceptual Framework for Flexible Workflow Generation in Federated ERP Systems Mediated by a FERP Mall

Environmental performance driven optimization of urban modular housing layout in Singapore

ABSTRACT With the growing size of cities and the need to renew residential areas, architects are calling for more efficient use of already settled areas. This paper presents a performance-driven architectural design (PDAD) workflow for shape generation and genetic optimization based on environmental data, using public housing in the Singapore region as a case study. It integrates three-dimensional cellular automata, parametric performance simulation, genetic optimization algorithms, and hierarchical clustering algorithms. The results show that the average value of Useful Daylight Illuminance (UDI) is 85.19%, the average value of Energy Use Intensity (EUI) is 159.41 kWh/m2, and the average value of Predicted Mean Vote (PMV) is 0.64 for the optimal set of solutions produced after genetic optimization. The optimal solution set was further classified into 4 categories by a hierarchical clustering algorithm and was visualized for further evaluation and selection by the architect. The study helps architects to integrate data analysis results with human decision-making as a design research method in the early stages of design and leads to further discussion on bottom-up design approaches in the urban renewal process.

Orthoscopic elemental image synthesis for 3D light field display using lens design software and real-world captured neural radiance field.

The elemental images (EIs) generation of complex real-world scenes can be challenging for conventional integral imaging (InIm) capture techniques since the pseudoscopic effect, characterized by a depth inversion of the reconstructed 3D scene, occurs in this process. To address this problem, we present in this paper a new approach using a custom neural radiance field (NeRF) model to form real and/or virtual 3D image reconstruction from a complex real-world scene while avoiding distortion and depth inversion. One of the advantages of using a NeRF is that the 3D information of a complex scene (including transparency and reflection) is not stored by meshes or voxel grid but by a neural network that can be queried to extract desired data. The Nerfstudio API was used to generate a custom NeRF-related model while avoiding the need for a bulky acquisition system. A general workflow that includes the use of ray-tracing-based lens design software is proposed to facilitate the different processing steps involved in managing NeRF data. Through this workflow, we introduced a new mapping method for extracting desired data from the custom-trained NeRF-related model, enabling the generation of undistorted orthoscopic EIs. An experimental 3D reconstruction was conducted using an InIm-based 3D light field display (LFD) prototype to validate the effectiveness of the proposed method. A qualitative comparison with the actual real-world scene showed that the 3D reconstructed scene is accurately rendered. The proposed work can be used to manage and render undistorted orthoscopic 3D images from custom-trained NeRF-related models for various InIm applications.

Candidate ferroelectrics via ab initio high-throughput screening of polar materials

Ferroelectrics are a class of polar and switchable functional materials with diverse applications, from microelectronics to energy conversion. Computational searches for new ferroelectric materials have been constrained by accurate prediction of the polarization and switchability with electric field, properties that, in principle, require a comparison with a nonpolar phase whose atomic-scale unit cell is continuously deformable from the polar ground state. For most polar materials, such a higher-symmetry nonpolar phase does not exist or is unknown. Here, we introduce a general high-throughput workflow that screens polar materials as potential ferroelectrics. We demonstrate our workflow on 1978 polar structures in the Materials Project database, for which we automatically generate a nonpolar reference structure using pseudosymmetries, and then compute the polarization difference and energy barrier between polar and nonpolar phases, comparing the predicted values to known ferroelectrics. Focusing on a subset of 182 potential ferroelectrics, we implement a systematic ranking strategy that prioritizes candidates with large polarization and small polar-nonpolar energy differences. To assess stability and synthesizability, we combine information including the computed formation energy above the convex hull, the Inorganic Crystal Structure Database id number, a previously reported machine learning-based synthesizability score, and ab initio phonon band structures. To distinguish between previously reported ferroelectrics, materials known for alternative applications, and lesser-known materials, we combine this ranking with a survey of the existing literature on these candidates through Google Scholar and Scopus databases, revealing ~130 promising materials uninvestigated as ferroelectric. Our workflow and large-scale high-throughput screening lays the groundwork for the discovery of novel ferroelectrics, revealing numerous candidates materials for future experimental and theoretical endeavors.

Digital surface model generation from high‐resolution satellite stereos based on hybrid feature fusion network

AbstractRecent studies have demonstrated that deep learning‐based stereo matching methods (DLSMs) can far exceed conventional ones on most benchmark datasets by both improving visual performance and decreasing the mismatching rate. However, applying DLSMs on high‐resolution satellite stereos with broad image coverage and wide terrain variety is still challenging. First, the broad coverage of satellite stereos brings a wide disparity range, while DLSMs are limited to a narrow disparity range in most cases, resulting in incorrect disparity estimation in areas with contradictory disparity ranges. Second, high‐resolution satellite stereos always comprise various terrain types, which is more complicated than carefully prepared datasets. Thus, the performance of DLSMs on satellite stereos is unstable, especially for intractable regions such as texture‐less and occluded regions. Third, generating DSMs requires occlusion‐aware disparity maps, while traditional occlusion detection methods are not always applicable for DLSMs with continuous disparity. To tackle these problems, this paper proposes a novel DLSM‐based DSM generation workflow. The workflow comprises three steps: pre‐processing, disparity estimation and post‐processing. The pre‐processing step introduces low‐resolution terrain to shift unmatched disparity ranges into a fixed scope and crops satellite stereos to regular patches. The disparity estimation step proposes a hybrid feature fusion network (HF2Net) to improve the matching performance. In detail, HF2Net designs a cross‐scale feature extractor (CSF) and a multi‐scale cost filter. The feature extractor differentiates structural‐context features in complex scenes and thus enhances HF2Net's robustness to satellite stereos, especially on intractable regions. The cost filter filters out most matching errors to ensure accurate disparity estimation. The post‐processing step generates initial DSM patches with estimated disparity maps and then refines them for the final large‐scale DSMs. Primary experiments on the public US3D dataset showed better accuracy than state‐of‐the‐art methods, indicating HF2Net's superiority. We then created a self‐made Gaofen‐7 dataset to train HF2Net and conducted DSM generation experiments on two Gaofen‐7 stereos to further demonstrate the effectiveness and practical capability of the proposed workflow.

Quantitative transcriptomic and epigenomic data analysis: a primer.

The advent of microarray and second generation sequencing technology has revolutionized the field of molecular biology, allowing researchers to quantitatively assess transcriptomic and epigenomic features in a comprehensive and cost-efficient manner. Moreover, technical advancements have pushed the resolution of these sequencing techniques to the single cell level. As a result, the bottleneck of molecular biology research has shifted from the bench to the subsequent omics data analysis. Even though most methodologies share the same general strategy, state-of-the-art literature typically focuses on data type specific approaches and already assumes expert knowledge. Here, however, we aim at providing conceptual insight in the principles of genome-wide quantitative transcriptomic and epigenomic (including open chromatin assay) data analysis by describing a generic workflow. By starting from a general framework and its assumptions, the need for alternative or additional data-analytical solutions when working with specific data types becomes clear, and are hence introduced. Thus, we aim to enable readers with basic omics expertise to deepen their conceptual and statistical understanding of general strategies and pitfalls in omics data analysis and to facilitate subsequent progression to more specialized literature.

Axial Compressor Map Generation Leveraging Autonomous Self-Training Artificial Intelligence. Phase 2

Abstract This paper discusses a study performed by SoftInWay as part of a Phase II SBIR project funded by NASA. In contrast with the Phase I project (Burlaka and Moroz, 2023, “Axial Compressor Map Generation Leveraging Autonomous Self-Training Artificial Intelligence,” ASME J. Eng. Gas Turbines Power, 145(1), p. 011001) where three discrete compressors were considered, the Phase II study was focused on addressing the problem of axial compressor long development time and cost with the use of AI models capable of predicting the geometry and performance of various multistage axial compressors with multiple variable vanes. The applicability of the AI models to various compressors enables the opportunity to avoid iterations between engine cycle analysis and compressor design. In this paper, automated compressor design and performance generation workflows are described. The approach for autonomous selection of the architectures and hyperparameters of Machine Learning (ML) models is explained. The uncertainty quantification techniques are considered. The developed ML-powered methods for compressor geometry prediction are discussed. The ML models' accuracy values and representations of typical geometry and performance predictions are given. The utilization of the ML models in engine cycle analysis is discussed.

Automated workflow generation supporting the value stream design of reconfigurable robot assembly cells

The paper addresses the automated generation of value stream optimized assembly workflows for reconfigurable robot cells. The focus is on the generation of asynchronous concurrent action workflows from assembly task descriptions. The main motivation is to enable automated search for optimized solutions within the enormous design spaces of these systems which results from their structural and procedural configurability. The proposed approach conducts task assignment, task sequencing and logical task planning (AI Planning) for multiple agents. The approach combines existing methods from the distinct research fields of robot task sequencing and discrete task planning. The contribution of the research is twofold. Firstly, the paper proposes a generic domain meta model enabling automated workflow generation for multi-agent assembly tasks. Secondly, a workflow generation approach based on artificial intelligence planning combined with the game-theoretic concept of extensive form games is presented. The approach iteratively generates a cooperative strategy for acting agents which is subsequently optimized in terms of individual strategies. The obtained logical action plans are further transformed into parametric concurrent action schedules to support downstream design activities. Both the meta model and the workflow generation approach are applied in a case study and provide valuable information on achievable assembly speedup.

Sample Preparation and Phosphopeptide Enrichment for Plant Phosphoproteomics via Label-Free Mass Spectrometry.

Phosphopeptide enrichment is the main bottleneck of every phosphorylation study. Therefore, in this chapter, a general workflow tries to overbridge the hurdles of plant sample handling from sample collection to protein extraction, protein solubilization, enzymatic digestion, and enrichment step prior to mass spectrometry. The workflow provides information to perform global proteomics as well as phosphoproteomics enabling the researcher to use the protocol in both fields.

The Rubin Observatory’s Legacy Survey of Space and Time DP0.2 processing campaign at CC-IN2P3

The Vera C. Rubin Observatory, currently in construction in Chile, will start performing the Legacy Survey of Space and Time (LSST) in 2025 for 10 years. Its 8.4-meter telescope will survey the southern sky in less than 4 nights in six optical bands, and repeatedly generate about 2 000 exposures per night, corresponding to a data volume of about 20 TiB every night. Three data facilities are preparing to contribute to the production of the annual data releases: the US Data Facility will process 35% of the raw data, the UK data facility will process 25% of the raw data and the French data facility, operated by CC-IN2P3, will locally process the remaining 40% of the raw data.In the context of the Data Preview 0.2 (DP0.2), the Data Release Production pipelines have been executed on the DC-2 simulated dataset (generated by the Dark Energy Science Collaboration, DESC). This dataset includes 20 000 simulated exposures, representing 300 square degrees of Rubin images with a typical depth of 5 years.DP0.2 ran at the Interim Data Facility (based on Google cloud), and the full exercise was independently replicated at CC-IN2P3. During this exercise, 3 PiB of data and more than 200 million files were produced. In this contribution we will present a detailed description of the system that we set up to perform this processing campaign using CC-IN2P3’s computing and storage infrastructure. Several topics will be addressed: workflow generation and execution, batch job submission, memory and I/O requirements, etc. We will focus on the issues that arose during this campaign and how we addressed them and will present some perspectives after this exercise.

An Analysis of the Interplay and Mutual Benefits of Grounded Theory and Visualization.

Grounded theory (GT) is a research methodology that entails a systematic workflow for theory generation grounded on emergent data. In this paper, we juxtapose GT workflows with typical workflows in visualization and visual analytics (VIS), unveiling the characteristics shared by these workflows. We explore the research landscape of VIS to study where GT is applied to generate VIS theories, explicitly as well as implicitly. We discuss "why" GT can potentially play a significant role in VIS. We outline a "how" methodology for conducting GT research in VIS, which addresses the need for theoretical advancement in VIS while benefiting from other methods and techniques in VIS. We illustrate this "how" methodology with a use case of adopting GT approaches in studying visualization guidelines.

Sentiment Analysis: Comprehensive Reviews, Recent Advances, and Open Challenges.

Sentiment analysis (SA) aims to understand the attitudes and views of opinion holders with computers. Previous studies have achieved significant breakthroughs and extensive applications in the past decade, such as public opinion analysis and intelligent voice service. With the rapid development of deep learning, SA based on various modalities has become a research hotspot. However, only individual modality has been analyzed separately, lacking a systematic carding of comprehensive SA methods. Meanwhile, few surveys covering the topic of multimodal SA (MSA) have been explored yet. In this article, we first take the modality as the thread to design a novel framework of SA tasks to provide researchers with a comprehensive understanding of relevant advances in SA. Then, we introduce the general workflows and recent advances of single-modal in detail, discuss the similarities and differences of single-modal SA in data processing and modeling to guide MSA, and summarize the commonly used datasets to provide guidance on data and methods for researchers according to different task types. Next, a new taxonomy is proposed to fill the research gaps in MSA, which is divided into multimodal representation learning and multimodal data fusion. The similarities and differences between these two methods and the latest advances are described in detail, such as dynamic interaction between multimodalities, and the multimodal fusion technologies are further expanded. Moreover, we explore the advanced studies on multimodal alignment, chatbots, and Chat Generative Pre-trained Transformer (ChatGPT) in SA. Finally, we discuss the open research challenges of MSA and provide four potential aspects to improve future works, such as cross-modal contrastive learning and multimodal pretraining models.