Discovery Methods Research Articles

A transformer-based weakly supervised computational pathology method for clinical-grade diagnosis and molecular marker discovery of gliomas

A transformer-based weakly supervised computational pathology method for clinical-grade diagnosis and molecular marker discovery of gliomas

Using Multiscale Molecular Modeling to Analyze Possible NS2b-NS3 Protease Inhibitors from Philippine Medicinal Plants.

Within the field of Philippine folkloric medicine, the utilization of indigenous plants like Euphorbia hirta (tawa-tawa), Carica papaya (papaya), and Psidium guajava (guava) as potential dengue remedies has gained attention. Yet, limited research exists on their comprehensive effects, particularly their anti-dengue activity. This study screened 2944 phytochemicals from various Philippine plants for anti-dengue activity. Absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiling provided 1265 compounds demonstrating pharmacokinetic profiles suitable for human use. Molecular docking targeting the dengue virus NS2b-NS3 protease's catalytic triad (Asp 75, Ser 135, and His 51) identified ten ligands with higher docking scores than reference compounds idelalisib and nintedanib. Molecular dynamics simulations confirmed the stability of eight of these ligand-protease complexes. Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) analysis highlighted six ligands, including veramiline (-80.682 kJ/mol), cyclobranol (-70.943 kJ/mol), chlorogenin (-63.279 kJ/mol), 25beta-Hydroxyverazine (-61.951 kJ/mol), etiolin (-59.923 kJ/mol), and ecliptalbine (-56.932 kJ/mol) with favorable binding energies, high oral bioavailability, and drug-like properties. This integration of traditional medical knowledge with advanced computational drug discovery methods paves new pathways for the development of treatments for dengue.

Discovering Causal Models with Optimization: Confounders, Cycles, and Instrument Validity

We propose a new optimization-based method for learning causal structures from observational data, a process known as causal discovery. Our method takes as input observational data over a set of variables and returns a graph in which causal relations are specified by directed edges. We consider a highly general search space that accommodates latent confounders and feedback cycles, which few extant methods do. We formulate the discovery problem as an integer program, and propose a solution technique that exploits the conditional independence structure in the data to identify promising edges for inclusion in the output graph. In the large-sample limit, our method recovers a graph that is (Markov) equivalent to the true data-generating graph. Computationally, our method is competitive with the state-of-the-art, and can solve in minutes instances that are intractable for alternative causal discovery methods. We leverage our method to develop a procedure for investigating the validity of an instrumental variable and demonstrate it on the influential quarter-of-birth and proximity-to-college instruments for estimating the returns to education. In particular, our procedure complements existing instrument tests by revealing the precise causal pathways that undermine instrument validity, highlighting the unique merits of the graphical perspective on causality. This paper was accepted by J. George Shanthikumar, data science. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.02066 .

IP-GCN: A deep learning model for prediction of insulin using graph convolutional network for diabetes drug design

Insulin is a kind of protein that regulates the blood sugar levels is significant to prevent complications associated with diabetes, such as cancer, neurodegenerative disorders, cardiovascular disease, and kidney damage. Insulin protein (IP) plays an active role in drug discovery, medicine, and therapeutic methods. Unlike experimental protocols, computational predictors are fast and can predict IP accurately. This work introduces a model, called IP-GCN for IP prediction. The patterns from IP are extracted by K-spaced position specific scoring matrix (KS-PSSM) and the model training is accomplished using powerful deep learning tool, called Graph Convolutional Network (GCN). Additionally, we implemented Pseudo Amino Acid Composition (PseAAC) and Dipeptide Composition (DPC) for feature encoding to assess the predictive performance of GCN. To evaluate the efficacy of our novel approach, we compare its performance with well-known deep/machine learning algorithms such as Convolutional Neural Network (CNN), Extremely Randomized Tree (ERT), and Support Vector Machine (SVM). Predictive results demonstrate that the proposed predictor (IP-GCN) secured the best performance on both training and testing datasets. The novel computational would be fruitful in diabetes drug discovery and contributes to research for therapeutic interventions in various Insulin protein associated diseases.

Detecting dynamical causality via intervened reservoir computing

An abundance of complex dynamical phenomena exists in nature and human society, requiring sophisticated analytical tools to understand and explain. Causal analysis through observational time series data is essential in comprehending complex systems when controlled experiments are not feasible or ethical. Although data-based causal discovery methods have been widely used, there is still a lack of direct ways more aligned with the intuitive definition of causality, i.e., whether interventions on one element lead to changes in the subsequent development of others. To solve this problem, we propose the method of intervened reservoir computing (IRC) based on constructing a neural network replica of the original system and applying interventions to it. This approach enables controlled trials, thus observing the intervened evolution, in the digital twins of the underlying systems. Simulated and real-world data are used to test our approach and demonstrate its accuracy in inferring causal networks. Given the importance of causality in understanding complex dynamics, we anticipate that IRC could serve as a powerful tool for various disciplines to decipher the intrinsic mechanisms of natural systems from observational data.

Optimizing Ex Vivo CAR-T Cell-Mediated Cytotoxicity Assay through Multimodality Imaging.

CAR-T cell-based therapies have demonstrated remarkable efficacy in treating malignant cancers, especially liquid tumors, and are increasingly being evaluated in clinical trials for solid tumors. With the FDA's initiative to advance alternative methods for drug discovery and development, full human ex vivo assays are increasingly essential for precision CAR-T development. However, prevailing ex vivo CAR-T cell-mediated cytotoxicity assays are limited by their use of radioactive materials, lack of real-time measurement, low throughput, and inability to automate, among others. To address these limitations, we optimized the assay using multimodality imaging methods, including bioluminescence, impedance tracking, phase contrast, and fluorescence, to track CAR-T cells co-cultured with CD19, CD20, and HER2 luciferase reporter cancer cells in real-time. Additionally, we varied the ratio of CAR-T cells to cancer cells to determine optimal cytotoxicity readouts. Our findings demonstrated that the CAR-T cell group effectively attacked cancer cells, and the optimized assay provided superior temporal and spatial precision measurements of ex vivo CAR-T killing of cancer cells, confirming the reliability, consistency, and high throughput of the optimized assay.

Identifying assessment criteria for in vitro studies: a method and item bank.

To support the development of appraisal tools for assessing the quality of in vitro studies, we developed a method for literature-based discovery of study assessment criteria, used the method to create an item bank of assessment criteria of potential relevance to in vitro studies, and analyzed the item bank to discern and critique current approaches for appraisal of in vitro studies. We searched four research indexes and included any document that identified itself as an appraisal tool for in vitro studies, was a systematic review that included a critical appraisal step, or was a reporting checklist for in vitro studies. We abstracted, normalized, and categorized all criteria applied by the included appraisal tools to create an "item bank" database of issues relevant to the assessment of in vitro studies. The resulting item bank consists of 676 unique appraisal concepts from 67 appraisal tools. We believe this item bank is the single most comprehensive resource of its type to date, should be of high utility for future tool development exercises, and provides a robust methodology for grounding tool development in the existing literature. Although we set out to develop an item bank specifically targeting in vitro studies, we found that many of the assessment concepts we discovered are readily applicable to other study designs. Item banks can be of significant value as a resource; however, there are important challenges in developing, maintaining, and extending them of which researchers should be aware.

High-throughput discovery of MHC class I- and II-restricted T cell epitopes using synthetic cellular circuits.

Antigen discovery technologies have largely focused on major histocompatibility complex (MHC) class I-restricted human T cell receptors (TCRs), leaving methods for MHC class II-restricted and mouse TCR reactivities relatively undeveloped. Here we present TCR mapping of antigenic peptides (TCR-MAP), an antigen discovery method that uses a synthetic TCR-stimulated circuit in immortalized T cells to activate sortase-mediated tagging of engineered antigen-presenting cells (APCs) expressing processed peptides on MHCs. Live, tagged APCs can be directly purified for deconvolution by sequencing, enabling TCRs with unknown specificity to be queried against barcoded peptide libraries in a pooled screening context. TCR-MAP accurately captures self-reactivities or viral reactivities with high throughput and sensitivity for both MHC class I-restricted and class II-restricted TCRs. We elucidate problematic cross-reactivities of clinical TCRs targeting the cancer/testis melanoma-associated antigen A3 and discover targets of myocarditis-inciting autoreactive T cells in mice. TCR-MAP has the potential to accelerate T cell antigen discovery efforts in the context of cancer, infectious disease and autoimmunity.

A Big Ring on the sky

We present the discovery of `A Big Ring on the Sky' (BR), the second ultra-large large-scale structure (uLSS) found in Mg II-absorber catalogues, following the previously reported Giant Arc (GA). In cosmological terms the BR is close to the GA — at the same redshift z ∼ 0.8 and with a separation on the sky of only ∼ 12°. Two extraordinary uLSSs in such close configuration raises the possibility that together they form an even more extraordinary cosmological system. The BR is a striking circular, annulus-like, structure of diameter ∼ 400 Mpc (proper size, present epoch). The method of discovery is as described in the GA paper, but here using the new Mg II-absorber catalogues restricted to DR16Q quasars. Using the Convex Hull of Member Spheres (CHMS) algorithm, we estimate that the annulus and inner absorbers of the BR have departures from random expectations, at the density of the control field, of up to 5.2σ. We present the discovery of the BR, assess its significance using the CHMS, Minimal Spanning Tree (MST), FilFinder and Cuzick & Edwards (CE) methods, discuss it in the context of the GA+BR system, and suggest some implications for the origins of uLSS and for our understanding of cosmology. For example, it may be that unusual geometric patterns, such as these uLSSs, have an origin in cosmic strings.

Twin Scholarships of Glycomedicine and Precision Medicine in Times of Single-Cell Multiomics.

Systems biology and multiomics research expand the prospects of planetary health innovations. In this context, this mini-review unpacks the twin scholarships of glycomedicine and precision medicine in the current era of single-cell multiomics. A significant growth in glycan research has been observed over the past decade, unveiling and establishing co- and post-translational modifications as dynamic indicators of both pathological and physiological conditions. Systems biology technologies have enabled large-scale and high-throughput glycoprofiling and access to data-intensive biological repositories for global research. These advancements have established glycans as a pivotal third code of life, alongside nucleic acids and amino acids. However, challenges persist, particularly in the simultaneous analysis of the glycome and transcriptome in single cells owing to technical limitations. In addition, holistic views of the complex molecular interactions between glycomics and other omics types remain elusive. We underscore and call for a paradigm shift toward the exploration of integrative glycan platforms and analysis methods for single-cell multiomics research and precision medicine biomarker discovery. The integration of multiple datasets from various single-cell omics levels represents a crucial application of systems biology in understanding complex cellular processes and is essential for advancing the twin scholarships of glycomedicine and precision medicine.

ANALYSIS OF HOW AND WHY STYLISTIC LANGUAGE IMPACTS ADVERTISING WITH A JOURNALISTIC APPROACH

Objective and Background: The Internet of Things (IoT) devices store a great volume of data. Therefore, with the advancements in technology, some new protection guidelines have to be developed to preserve data privacy and collection. The present study aimed to determine a secure protective framework using the BTD structure in a specified zone, and provide the idea of hiding the sensory data of an operator device to preserve data privacy without losing the data integrity. Methodology: Two simple and enhanced methods were used. The BTD framework and estimated ground truth were used in both modes. The enhanced method allows the users to use a random weight (variance) when combining their sensory data with the estimated ground truth provided by the agent. We ran the BTD framework in the CloudSim (network simulation framework). Each simulation is run for 500 virtual minutes with 12 iterations. The target zone is set to 50×50 meters. The test results were collected on real-world data traces from sensory systems. In addition to the BTD structure, the Voting method has been also used, and to do so, majority voting and CRH truth discovery method (Conflict Resolution on Heterogeneous Data) which does not take any measures to break the security of the sensor during the process is used. Findings: The comparative results show a 0.70-0.71 error level between the BTD and other functions, at least. As long as there is at least one reliable source, the BTD will have an error level of 0. Simulations show that BTD has a worst-case error of 0.05 and a weighted variance of +/- 5%. Based on previous frameworks, the BTD is improved by three requirements of a population identified in the IoT: Preservation of privacy for the device user, data integrity for the data collection group, and low-cost computation on the user device.

Exploring latent discrimination through an Object-Relational Causal Inference method

This paper addresses the critical but often overlooked problem of exploring latent sensitive attributes in machine-learning datasets where they are not explicitly present. We propose a method named Object-Relational Causal Inference (ORCI), solving the problem with good reusability. We mathematically demonstrate the enhancement of data fairness by uncovering latent sensitive attributes and build a framework to tackle the latent discrimination exploration challenge. Firstly, we highlight the limitations of classical causal discovery methods and introduce an approximate exhaustive search algorithm called BIC-based Exhaustive Search (BICES). Then, we integrate the Object Relational (OR) approach into causal modeling, enabling the subsequent structured probabilistic inference based on the OR causal model to derive fair and effective latent sensitive attributes. We conduct simulations and experiments on synthetic and real-world cases across various machine-learning tasks, with discrete and continuous data, and in diverse settings. Results show that our method successfully uncovers latent sensitive attributes, enhancing the data fairness while preserving data utility. Besides, our detailed theoretical derivations and rich experiments demonstrate the generalization of our method.

Pills of Multi-Target H2S Donating Molecules for Complex Diseases.

Among the various drug discovery methods, a very promising modern approach consists in designing multi-target-directed ligands (MTDLs) able to modulate multiple targets of interest, including the pathways where hydrogen sulfide (H2S) is involved. By incorporating an H2S donor moiety into a native drug, researchers have been able to simultaneously target multiple therapeutic pathways, resulting in improved treatment outcomes. This review gives the reader some pills of successful multi-target H2S-donating molecules as worthwhile tools to combat the multifactorial nature of complex disorders, such as inflammatory-based diseases and cancer, as well as cardiovascular, metabolic, and neurodegenerative disorders.

Ligandability at the Membrane Interface of GPx4 Revealed through a Reverse Micelle Fragment Screening Platform.

While they account for a large portion of drug targets, membrane proteins present a unique challenge for drug discovery. Peripheral membrane proteins (PMPs), a class of water-soluble proteins that bind to membranes, are also difficult targets, particularly those that function only when bound to membranes. The protein-membrane interface in PMPs is often where functional interactions and catalysis occur, making it a logical target for inhibition. However, protein-membrane interfaces are underexplored spaces in inhibitor design, and there is a need for enhanced methods for small-molecule ligand discovery. In an effort to better initiate drug discovery efforts for PMPs, this study presents a screening methodology using membrane-mimicking reverse micelles (mmRM) and NMR-based fragment screening to assess ligandability at the protein-membrane interface. The proof-of-principle target, glutathione peroxidase 4 (GPx4), is a lipid hydroperoxidase that is essential for the oxidative protection of membranes and thereby the prevention of ferroptosis. GPx4 inhibition is promising for therapy-resistant cancer therapy, but current inhibitors are generally covalent ligands with limited clinical utility. Presented here is the discovery of noncovalent small-molecule ligands for membrane-bound GPx4 revealed through the mmRM fragment screening methodology. The fragments were tested against GPx4 under bulk aqueous conditions and displayed little to no binding to the protein without embedment into the membrane. The 9 hits had varying affinities and partitioning coefficients and revealed properties of fragments that bind within the protein-membrane interface. Additionally, a secondary screen confirmed the potential to progress the fragments by enhancing the affinity from >200 to ∼15 μM with the addition of certain hydrophobic groups. This study presents an advancement of screening capabilities for membrane-associated proteins, reveals ligandability within the GPx4 protein-membrane interface, and may serve as a starting point for developing noncovalent inhibitors of GPx4.

Deuterated Alkyl Sulfonium Salt Reagents; Importance of H/D Exchange Methods in Drug Discovery.

Deuterated drugs (heavy drugs) have recently been spotlighted as a new modality for small-molecule drugs because the pharmacokinetics of pharmaceutical drugs can be enhanced by replacing C-H bonds with more stable C-D bonds at metabolic positions. Therefore, deuteration methods for drug candidates are a hot topic in medicinal chemistry. Among them, the H/D exchange reaction (direct transformation of C-H bonds to C-D bonds) is a useful and straightforward method for creating novel deuterated target molecules, and over 20 reviews on the synthetic methods related to H/D exchange reactions have been published in recent years. Although various deuterated drug candidates undergo clinical trials, approved deuterated drugs possess CD3 groups in the same molecule. However, less diversification, except for the CD3 group, is a problem for future medicinal chemistry. Recently, we developed various deuterated alkyl (dn-alkyl) sulfonium salts based on the H/D exchange reaction of the corresponding hydrogen form using D2O as an inexpensive deuterium source to introduce CD3, CH3CD2, and ArCH2CD2 groups into drug candidates. This concept summarises recent reviews related to H/D exchange reactions and novel reagents that introduce the CD3 group, and our newly developed electrophilic dn-alkyl reagents are discussed.

Mining Abnormal Patterns in Moving Target Trajectories Based on Multi-Attribute Classification

As a type of time series data, trajectory data objectively record the location information and corresponding time information of an object’s activities. It not only describes the spatial activity trajectory of a moving object but also contains the unique attributes, states, and behavioral characteristics of the moving object itself. It can also reflect the interaction relationship between the object’s activities and various elements in the environment to a certain extent. Therefore, mining from moving target trajectory data to discover implicit, effective, and potentially useful spatiotemporal behavior patterns of moving targets, such as anomaly detection, will have significant research significance. This paper proposes a method for mining abnormal patterns in the trajectory of moving targets based on multi-attribute classification. Firstly, to explore the activity location patterns of single moving targets, a frequent sequence discovery method for moving targets based on sequence patterns is proposed. Furthermore, for moving target trajectory data sets containing multiple attributes, numerical attributes are extracted, and the data are clustered according to attribute classification to extract a set of normal behavior patterns of moving targets. Then, combining the activity location patterns and normal behavior patterns of the moving target, the original trajectory data are compared with them to achieve the goal of detecting abnormal behavior of the moving target. Finally, an incremental anomaly detection scheme is proposed to address the characteristics of fast updates and large numbers of data in trajectory data sets. This involves synchronously updating the frequency of moving target activity patterns and the range of values for normal behavior patterns while updating the trajectory data set, in order to meet the needs of database updates and improve the accuracy and credibility of results.

UCAD: commUnity disCovery method in Attribute-based multicoloreD networks

UCAD: commUnity disCovery method in Attribute-based multicoloreD networks

KGRED: Knowledge-graph-based rule discovery for weakly supervised data labeling

In weakly supervised learning, labeling rules can automatically label data to train models. However, due to insufficient prior knowledge, rule discovery often suffers from semantic drift. Since misclassified rules are generated from wrongly matched sentences, the sentences matched by rules shift from the target labels to other labels. It is worth noting that rules do not exist in isolation. The multi-dimensional semantic associations among rules can impose semantic constraints for rule generation, as well as enrich the semantic information of rules for rule matching. Therefore, we propose a Knowledge-Graph-based RulE Discovery method (KGRED), which can leverage the multi-dimensional semantic associations among rules to alleviate semantic drift in rule discovery. Specifically, to decrease misclassified rules, we design a label-aware rule generation approach to attentively propagate prior knowledge from seed rules to candidate rules based on rule KG. To reduce wrongly-matched sentences, we present a cross-attention-based semantic matching mechanism to refine the semantic information of sentences while enriching that of rules. Moreover, we propose an inconsistency-directed active learning strategy to verify rules that perform inconsistently in rule generation and matching. Experiments on two public datasets prove that KGRED can achieve at least 5.1 % gain in F1 score compared to state-of-the-art methods.

Legal Discovery Method for Non-Muslim Heirs as Recipients of Wasiat Wajibah

Legal Discovery Method for Non-Muslim Heirs as Recipients of Wasiat Wajibah

Discovering the effective connectome of the brain with dynamic Bayesian DAG learning

Understanding the complex mechanisms of the brain can be unraveled by extracting the Dynamic Effective Connectome (DEC). Recently, score-based Directed Acyclic Graph (DAG) discovery methods have shown significant improvements in extracting the causal structure and inferring effective connectivity. However, learning DEC through these methods still faces two main challenges: one with the fundamental impotence of high-dimensional dynamic DAG discovery methods and the other with the low quality of fMRI data. In this paper, we introduce Bayesian Dynamic DAG learning with M-matrices Acyclicity characterization (BDyMA) method to address the challenges in discovering DEC. The presented dynamic DAG enables us to discover direct feedback loop edges as well. Leveraging an unconstrained framework in the BDyMA method leads to more accurate results in detecting high-dimensional networks, achieving sparser outcomes, making it particularly suitable for extracting DEC. Additionally, the score function of the BDyMA method allows the incorporation of prior knowledge into the process of dynamic causal discovery which further enhances the accuracy of results. Comprehensive simulations on synthetic data and experiments on Human Connectome Project (HCP) data demonstrate that our method can handle both of the two main challenges, yielding more accurate and reliable DEC compared to state-of-the-art and traditional methods. Additionally, we investigate the trustworthiness of DTI data as prior knowledge for DEC discovery and show the improvements in DEC discovery when the DTI data is incorporated into the process.