Data integration is the process of combining data generated using a variety of different research methods in order to enable detection of underlying themes and, in computational biology and bioinformatics, biological principles. Data integration is important in biology owing to the large and different 'omics' datasets now available. Uncover the latest and most impactful research in Data integration. Explore pioneering discoveries, insightful ideas and new methods from leading researchers in the field.
Human pose estimation plays a fundamental role in understanding human behaviors and interactions in complex scenes. Among existing approaches, High-Resolution Networks (HRNet) and their variants have demonstrated strong performance in dense...
Secure software updates are essential for maintaining Internet of Things (IoT) systems throughout their operational lifetime. However, software lifecycle information is typically fragmented across software repositories, deployment services, device...
Behavioral health systems in the United States have historically lagged behind other health care sectors in digital infrastructure, interoperability, and advanced electronic health record (EHR) functionality. However, national evaluations of digital...
Reliable disaster assessment is a complex distributed decision problem in which event heterogeneity, unstable modality quality, abnormal client behavior, and predictive miscalibration interact across the learning pipeline. This study proposes...
Multi-source trajectory records in road freight transportation often differ in schema, representation, sampling interval, field availability, and data quality, making records of the same trip difficult to link reliably. This study proposes QMF-TEA,...
Background Clinical trial data sharing is increasingly endorsed by journals, funders, regulators, and research organisations. Nevertheless, access to individual participant data (IPD) remains inconsistent and may not be available when trial results...
Federated learning (FL) is a distributed learning approach in which clients collaboratively train a machine learning model without sharing data. While this distributed learning paradigm has clear benefits in terms of privacy, data heterogeneity...
Medical imaging data are increasingly used beyond patient care to support research, quality assessment, and artificial intelligence development. These applications require imaging examinations to be represented in ways that enable consistent...
Achieving precise chemical composition in the manufacturing of steel is critical for meeting quality standards and customer specifications. During the steelmaking process, Ferro alloys are added to adjust levels of elements like carbon, manganese,...
The increasing demand for task-specific adaptation of large language models (LLMs) on private, geographically distributed data has led to a growing interest in federated fine-tuning of LLMs (FedLLM). However, existing FedLLM methods often face...
Improving railway passenger information systems (PIS) is a potential method to increase railway ridership and customer satisfaction. High-quality information on railway travel can only be provided to travelers if the underlying data are sufficient....
Interoperability remains a persistent limitation in Building Information Modelling (BIM) because Industry Foundation Classes (IFC) exchanges can preserve file syntax while losing or misrepresenting geometry, properties, quantities, and...
Introduction Machine learning models trained on metabolomics data frequently show reduced performance when applied to independent cohorts because of domain shifts introduced by differences in analytical platforms, sample handling, storage conditions,...
Background Bispectral index (BIS) monitoring primarily assesses the current depth of anesthesia and does not indicate future trends. Predicting future low BIS may give anesthesiologists time to assess potentially excessive depth of anesthesia in...
High-precision navigation and positioning systems are critical for applications in autonomous vehicles and mobile mapping, where robust and continuous localization is essential. To test and enhance the performance of algorithms, some research...
Background Efficient computational methods for predicting drug–target interactions (DTI) are crucial for drug discovery, shortening the development cycle, and reducing costs. Although deep learning has achieved remarkable progress in this field,...
The rapid adoption of artificial intelligence (AI) in healthcare, driven by the increasing availability of electronic health records (EHRs), medical imaging systems, genomic data, and wearable devices, has created significant challenges related to...
Studies applying artificial intelligence (AI) to tuberculosis (TB) detection from chest radiographs (CXR) focus on models and metrics, yet the quality, provenance and reproducibility of the data workflow decide the reliability of results; duplicate...
The Internet of Vehicles (IoV) is an emerging concept built upon advancements in the Internet of Things (IoT), providing enhanced communication systems for vehicles, infrastructure, and connected devices. However, the highly dynamic and distributed...
