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PubMed1997

Protein model representation and construction.

Crystallographic studies play a major role in current efforts towards protein structure determination. However, despite recent advances in computational tools for molecular modeling and graphics, the task of constructing a protein model from crystallographic data remains complex and time-consuming, requiring extensive expert intervention. This paper describes an approach to automating the process of model construction, where a model is represented as an annotated trace (or partial trace) of the three-dimensional backbone of the structure. Potential models are generated using an evolutionary algorithm, which incorporates multiple fitness functions tailored to different structural levels in the protein. Preliminary experimental results, which demonstrate the viability of the approach, are reported.

Algorithms↗
PubMed2026

Artificial intelligence-based tumour infiltrating lymphocyte quantification in patients with triple-negative breast cancer: an independent validation study.

BACKGROUND: Tumour-infiltrating lymphocytes (TILs) are a robust prognostic marker in patients with triple-negative breast cancer. Artificial intelligence (AI)-derived computational tools assessing TILs could improve efficiency, but require independent validation against clinical outcomes. We aimed to compare the prognostic performance of AI-derived TIL scores with pathologist-scored TILs in a large, prospectively collected dataset pooled from randomised controlled trials. METHODS: CATALINA was an independent, external validation study using prospectively collected long-term clinical outcome data pooled from seven randomised clinical trials conducted at multiple sites. We independently evaluated two previously validated AI pipelines that generate five computationally assessed tumour-infiltrating lymphocyte (cTIL) scores by masked, independent deployment of locked models. cTIL scores were correlated with the mean of the pathologist-scored stromal TILs (sTILs) in 220 digitised haematoxylin and eosin whole slide images in a cohort of patients with early-stage triple-negative or HER-2 positive breast cancer, previously scored by trained pathologists in a TIL-reproducibility study. Prognostic performance was assessed in a separate cohort of patients with early triple-negative breast cancer pooled from seven prospective, randomised adjuvant trials. Multivariable Cox regression models adjusted for clinicopathological factors and study heterogeneity assessed associations of cTIL score and sTIL score with invasive disease-free survival, distant disease-free survival, and overall survival. 5-year discrimination was estimated using time-dependent area under the receiver operating characteristic curve (AUC). FINDINGS: Individual data were collated from 1759 patients, of whom 1356 had complete clinicopathological data, pathologist sTIL scores, and cTIL scores available. Modest correlation (r 0&#xb7;375-0&#xb7;473) was observed between cTIL scores and the mean pathologist sTIL score. Both sTIL and cTIL were independently associated with 5-year invasive disease-free survival, distant disease-free survival, and overall survival after adjustment for clinicopathological factors (hazard ratio for invasive disease-free survival was 0&#xb7;73 [95% CI 0&#xb7;66-0&#xb7;82]; q<0&#xb7;0001, distant disease-free survival was 0&#xb7;70 [0&#xb7;61-0&#xb7;79]; q<0&#xb7;0001, and overall survival was 0&#xb7;72 [0&#xb7;63-0&#xb7;82]; q<0&#xb7;0001 for sTIL scores and 0&#xb7;80 [0&#xb7;73-0&#xb7;89]; q<0&#xb7;0001, 0&#xb7;77 [0&#xb7;69-0&#xb7;86]; q<0&#xb7;0001, and 0&#xb7;79 [0&#xb7;70-0&#xb7;88]; q=0&#xb7;0002, respectively, for percentage_lymphocyte scores). In models adjusted for clinicopathological variables and sTIL score, cTIL score did not maintain a statistically significant prognostic association. Both sTIL and cTIL scores improved the 5-year AUC over clinicopathological variables alone, while cTIL score did not significantly further improve AUC when combined with clinicopathological variables and sTIL score. INTERPRETATION: Two cTIL models deployed entirely without retraining or modification provided statistically significant prognostic information and improved risk discrimination compared with clinicopathological variables alone in this large, platform-based, independent validation study. Although cTIL score did not incrementally improve prognostication compared with models combining clinicopathological variables with sTIL score, these findings support the application of cTILs as a reproducible prognostic biomarker, particularly in settings where routine or widespread pathologist assessment is unavailable. FUNDING: Breast Cancer Research Foundation (USA).

Humans↗
PubMed2005

On the use of qualitative reasoning to simulate and identify metabolic pathways.

MOTIVATION: Perhaps the greatest challenge of modern biology is to develop accurate in silico models of cells. To do this we require computational formalisms for both simulation (how according to the model the state of the cell evolves over time) and identification (learning a model cell from observation of states). We propose the use of qualitative reasoning (QR) as a unified formalism for both tasks. The two most commonly used alternative methods of modelling biochemical pathways are ordinary differential equations (ODEs), and logical/graph-based (LG) models. RESULTS: The QR formalism we use is an abstraction of ODEs. It enables the behaviour of many ODEs, with different functional forms and parameters, to be captured in a single QR model. QR has the advantage over LG models of explicitly including dynamics. To simulate biochemical pathways we have developed 'enzyme' and 'metabolite' QR building blocks that fit together to form models. These models are finite, directly executable, easy to interpret and robust. To identify QR models we have developed heuristic chemoinformatics graph analysis and machine learning procedures. The graph analysis procedure is a series of constraints and heuristics that limit the number of ways metabolites can combine to form pathways. The machine learning procedure is generate-and-test inductive logic programming. We illustrate the use of QR for modelling and simulation using the example of glycolysis. AVAILABILITY: All data and programs used are available on request.

Algorithms↗
PubMed2006

Implementing Gaussian process inference with neural networks.

Gaussian processes compare favourably with backpropagation neural networks as a tool for regression, and Bayesian neural networks have Gaussian process behaviour when the number of hidden neurons tends to infinity. We describe a simple recurrent neural network with connection weights trained by one-shot Hebbian learning. This network amounts to a dynamical system which relaxes to a stable state in which it generates predictions identical to those of Gaussian process regression. In effect an infinite number of hidden units in a feed-forward architecture can be replaced by a merely finite number, together with recurrent connections.

Algorithms↗
PubMed2005

How to pretend that correlated variables are independent by using difference observations.

In many areas of data modeling, observations at different locations (e.g.,time frames or pixel locations) are augmented by differences of nea r by observations (e.g., delta features in speech recognition, Gabor jets in image analysis). These augmented observations are then often modeled as being independent. How can this make sense?We provide two interpretations,showing (1) that the likelihood of data generated from an auto regressive process can be computed in terms of "independent" augmented observations and (2) that the augmented observations can be given a coherent treatment in terms of the products of experts model (Hinton, 1999).

Algorithms↗
PubMed2025

Multi-Omics and Integrative Analytics in Natural Products Discovery.

Natural products (NPs) have long been an essential source of new bioactive compounds for drug discovery; however, traditional methods for screening and isolating these compounds can be slow and often yield diminishing returns. Fortunately, advanced multi-omics and computational approaches present powerful solutions to these challenges. This review highlights innovative methodologies that integrate metabolomics, genomics, transcriptomics, and proteomics with bioinformatics and analytical chemistry to accelerate NP discovery. For instance, untargeted metabolomics platforms like high-resolution liquid chromatography-tandem mass spectrometry (LC-MS/MS) and Global Natural Products Social (GNPS) molecular networking allow for comprehensive profiling of new compounds, while targeted isotope-labeling strategies enhance this process. Additionally, genome and metagenome mining tools such as antibiotics and secondary metabolite analysis shell (antiSMASH), Deep Biosynthetic Gene Cluster (DeepBGC), and Pipeline for Reconstructing Integrated Syntheses of Metabolites (PRISM) quickly identify biosynthetic gene clusters (BGCs) in both cultured and uncultured organisms, often using heterologous expression to validate products. Transcriptomic analyses, including RNA sequencing (RNA-seq), co-expression networks, and fluxomics, help clarify how pathways are regulated, while quantitative proteomics techniques like tandem mass tags/isobaric tags for relative and absolute quantitation (TMT/iTRAQ) and label-free methods, along with chemoproteomics approaches such as cellular thermal shift assay and thermal proteome profiling (TPP), uncover molecular targets and their mechanisms of action. This review also places significant emphasis on the role of artificial intelligence (AI) and machine learning (ML) in integrating multi-omics data, spanning activities from constructing gene-metabolite correlation networks to leveraging knowledge graphs and graph neural networks for data fusion and functional prediction. Finally, this review concludes by discussing the synergistic benefits of multi-omics for natural-product discovery, addressing current technical challenges, and exploring future directions toward high-throughput, intelligent data integration for next-generation NP research.

Biological Products↗
PubMed1998

Evaluation of a semantic data model for chest radiology: application of a new methodology.

An essential step toward the effective processing of the medical language is the development of representational models that formalize the language semantics. These models, also known as semantic data models, help to unlock the meaning of descriptive expressions, making them accessible to computer systems. The present study tries to determine the quality of a semantic data model created to encode chest radiology findings. The evaluation methodology relied on the ability of physicians to extract information from textual and encoded representations of chest X-ray reports, whilst answering questions associated with each report. The evaluation demonstrated that the encoded reports seemed to have the same information content of the original textual reports. The methodology generated useful data regarding the quality of the data model, demonstrating that certain segments were creating ambiguous representations and that some details were not being represented.

Adult↗
PubMed2002

Automated fault-management in a simulated spaceflight micro-world.

BACKGROUND: As human spaceflight missions extend in duration and distance from Earth, a self-sufficient crew will bear far greater onboard responsibility and authority for mission success. This will increase the need for automated fault management (FM). Human factors issues in the use of such systems include maintenance of cognitive skill, situational awareness (SA), trust in automation, and workload. This study examine the human performance consequences of operator use of intelligent FM support in interaction with an autonomous, space-related, atmospheric control system. METHODS: An expert system representing a model-base reasoning agent supported operators at a low level of automation (LOA) by a computerized fault finding guide, at a medium LOA by an automated diagnosis and recovery advisory, and at a high LOA by automate diagnosis and recovery implementation, subject to operator approval or veto. Ten percent of the experimental trials involved complete failure of FM support. RESULTS: Benefits of automation were reflected in more accurate diagnoses, shorter fault identification time, and reduced subjective operator workload. Unexpectedly, fault identification times deteriorated more at the medium than at the high LOA during automation failure. Analyses of information sampling behavior showed that offloading operators from recovery implementation during reliable automation enabled operators at high LOA to engage in fault assessment activities CONCLUSIONS: The potential threat to SA imposed by high-level automation, in which decision advisories are automatically generated, need not inevitably be counteracted by choosing a lower LOA. Instead, freeing operator cognitive resources by automatic implementation of recover plans at a higher LOA can promote better fault comprehension, so long as the automation interface is designed to support efficient information sampling.

Adult↗
PubMedDate not supplied

RECAP--retrosynthetic combinatorial analysis procedure: a powerful new technique for identifying privileged molecular fragments with useful applications in combinatorial chemistry.

The use of combinatorial chemistry for the generation of new lead molecules is now a well established strategy in the drug discovery process. Central to the use of combinatorial chemistry is the design and availability of high quality building blocks which are likely to afford hits from the libraries that they generate. Herein we describe "RECAP" (Retrosynthetic Combinatorial Analysis Procedure), a new computational technique designed to address this building block issue. RECAP electronically fragments molecules based on chemical knowledge. When applied to databases of biologically active molecules this allows the identification of building block fragments rich in biologically recognized elements and privileged motifs and structures. This allows the design of building blocks and the synthesis of libraries rich in biological motifs. Application of RECAP to the Derwent World Drug Index (WDI) and the molecular fragments/ building blocks that this generates are discussed. We also describe a WDI fragment knowledge base which we have built which stores the drug motifs and mention its potential application in structure based drug design programs.

Artificial Intelligence↗
PubMedDate not supplied

Decision support computer program for cancer pain management.

The purpose of the study was to develop an initial version of computer software that could assist nurses' decision making about cancer pain reported by women from diverse cultural groups. This cross-sectional study included two phases: (1) data collection and (2) development of computer software. Data were collected using an Internet survey and e-mail group discussions of 19 faculty members from 10 countries who were self-identified experts in oncology nursing. The data were analyzed using descriptive statistics and content analysis. The findings indicated ethnic, gender, geographic, and age differences in cancer pain descriptions. Based on the collected data, a decision support computer program for cancer pain management, including (1) a knowledge base generation module, (2) a decision-making module, and (3) a self-adaptation module, was developed. Based on the study findings, suggestions for future research and practice related to cancer pain and expert systems were proposed.

Artificial Intelligence↗
PubMed1996

Making generic guidelines site-specific.

Health care providers are more likely to follow a clinical guideline if the guideline's recommendations are consistent with the way in which their organization does its work. Unfortunately, developing guidelines that are specific to an organization is expensive, and limits the ability to share guidelines among different institutions. We describe a methodology that separates the site-independent information of guidelines from site-specific information, and that facilitates the development of site-specific guidelines from generic guidelines. We have used this methodology in a prototype system that assists developers in creating generic guidelines that are sharable across different sites. When combined with site information, generic guidelines can be used to generate site-specific guidelines that are responsive to organizational change and that can be implemented at a level of detail that makes site-specific computer-based workflow management and simulation possible.

Artificial Intelligence↗
PubMed2026

BioMedGraphica: an all-in-one platform for joint textual biomedical prior knowledge and numeric graph generation.

MOTIVATION: Multiomics data analysis is essential for scientific discovery in precision medicine. However, translating analysis results of omics data analysis into novel scientific hypotheses remains a significant challenge. Human experts must manually review analysis results and generate new hypotheses based on extensive and interconnected biomedical prior knowledge, which is subjective and not scalable. While large language models can accelerate the discovery, their reasoning improves when grounded in structured, auditable, and comprehensive biomedical prior knowledge. However, biomedical knowledge is scattered across heterogeneous databases that use diverse and inconsistent nomenclature systems, making it difficult to integrate resources into a unified format for scalable analysis. This fragmentation limits the ability of artificial intelligence systems to fully leverage biomedical data for scientific discovery. RESULTS: We developed BioMedGraphica, a novel all-in-one platform that harmonizes fragmented biomedical resources by integrating 11 entity types and 30 relation types from 43 databases into a unified textual prior knowledge graph containing 2 306 921 entities and 27 232 091 relations. In addition, we present a novel textual-numeric graph (TNG) data structure concept, where textual information captures prior biological knowledge (e.g. transcription start sites, functions, mechanisms), numeric values represent quantitative biomedical features, and the integrated relations can help uncover mechanisms. By bridging prior knowledge with user-specific data, TNG is a novel and ideal data structure for developing novel graph analysis models. AVAILABILITY AND IMPLEMENTATION: The code is available at: https://github.com/FuhaiLiAiLab/BioMedGraphica and BioMedGraphica knowledge graph database can be downloaded from huggingface dataset: https://huggingface.co/datasets/FuhaiLiAiLab/BioMedGraphica.

Humans↗
PubMed1998

Why discourse structures in medical reports matter for the validity of automatically generated text knowledge bases.

The automatic analysis of medical full-texts currently suffers from neglecting text coherence phenomena such as reference relations between discourse units. This has unwarranted effects on the description adequacy of medical knowledge bases automatically generated from texts. The resulting representation bias can be characterized in terms of artificially fragmented, incomplete and invalid knowledge structures. We discuss three types of textual phenomena (pronominal and nominal anaphora, as well as textual ellipsis) and outline basic methodologies how to deal with them.

Artificial Intelligence↗
PubMed2026

Use of synthetic data, a novel paradigm for immunopathology.

The complexity and heterogeneity of autoimmune diseases are only partially captured by current analytic tools, even when deep learning techniques are employed to intercept patterns beyond existing dogma. Synthetic data offer&#xa0;a newer paradigm through machine-generated reconstructions of real-world data that faithfully attempt to recapitulate biological and clinical patterns without creating duplicates and maintaining the&#xa0;privacy of the original ones. Synthetic data act as a&#xa0;magnifying lens, allowing predictions otherwise not possible on disease classification, progression, and therapeutic response. This approach has several advantages and is currently underutilized. Firstly, it provides cohort enrichment and equilibrates group imbalances. Second, it generates synthetic arms for both in vitro studies and human clinical trials, relevant to disentangle the rarity and heterogeneity of autoimmune diseases. Third, the platform allows applications beyond tabular registries, including medical images, genomics, and flow cytometry data. Last, 'digital twins' act through dynamic bidirectional links with the biological/clinical system counterpart, lending themselves to transformative opportunities for precision medicine. Herein, we discuss the current status of this fast-moving novel component of artificial intelligence and its implications for autoimmune diseases.

Humans↗
PubMed1996

A computer-based statistical pattern recognition for Doppler spectral waveforms of intracranial blood flow.

A computer-based statistical pattern recognition system has been developed for the analysis of transcranial Doppler (TCD) spectral waveforms of the intracranial middle cerebral artery with varying degrees of increased intracranial pressure. This system extracts multidimensional features from TCD waveforms and performs a cluster analysis of those features. The system can automatically recognize the pattern of spectral waveform and classify it as a normal, abnormal, or borderline subclass of TCD spectral waveform. An optimum decision function was generated based on the Bayes Gaussian classifier. The accuracy of the Bayes Gaussian model the spectral waveforms reaches 100% by estimating posterior probability and using the resubstituting method of estimating misclassification in the training TCD data.

Artificial Intelligence↗
PubMed2003

Genome-wide discovery of transcriptional modules from DNA sequence and gene expression.

In this paper, we describe an approach for understanding transcriptional regulation from both gene expression and promoter sequence data. We aim to identify transcriptional modules--sets of genes that are co-regulated in a set of experiments, through a common motif profile. Using the EM algorithm, our approach refines both the module assignment and the motif profile so as to best explain the expression data as a function of transcriptional motifs. It also dynamically adds and deletes motifs, as required to provide a genome-wide explanation of the expression data. We evaluate the method on two Saccharomyces cerevisiae gene expression data sets, showing that our approach is better than a standard one at recovering known motifs and at generating biologically coherent modules. We also combine our results with binding localization data to obtain regulatory relationships with known transcription factors, and show that many of the inferred relationships have support in the literature.

Algorithms↗
PubMed2000

A data preprocessing framework for supporting probability-learning in dynamic decision modeling in medicine.

Data preprocessing is needed when real-life clinical databases are used as the data sources to learn the probabilities for dynamic decision models. Data preprocessing is challenging as it involves extensive manual effort and time in developing the data operation scripts. This paper presents a framework to facilitate automated and interactive generation of the problem-specific data preprocessing scripts. The framework has three major components: 1) A model parser that parses the decision model definition, 2) A graphical user interface that facilitates the interaction between the user and the system, and 3) A script generator that automatically generates the specific database scripts for the data preprocessing. We have implemented a prototype system of the framework and evaluated its effectiveness via a case study in the clinical domain. Preliminary results demonstrate the practical promise of the framework.

Artificial Intelligence↗