Multimodal data integration for oncology in the era of deep neural networks: a review

Frontiers in Artificial Intelligence, Journal Year: 2024, Volume and Issue: 7

Published: July 25, 2024

Cancer research encompasses data across various scales, modalities, and resolutions, from screening diagnostic imaging to digitized histopathology slides types of molecular clinical records. The integration these diverse for personalized cancer care predictive modeling holds the promise enhancing accuracy reliability screening, diagnosis, treatment. Traditional analytical methods, which often focus on isolated or unimodal information, fall short capturing complex heterogeneous nature data. advent deep neural networks has spurred development sophisticated multimodal fusion techniques capable extracting synthesizing information disparate sources. Among these, Graph Neural Networks (GNNs) Transformers have emerged as powerful tools learning, demonstrating significant success. This review presents foundational principles learning including oncology taxonomy strategies. We delve into recent advancements in GNNs oncology, spotlighting key studies their pivotal findings. discuss unique challenges such heterogeneity complexities, alongside opportunities it a more nuanced comprehensive understanding cancer. Finally, we present some latest pan-cancer By surveying landscape our goal is underline transformative potential Transformers. Through technological methodological innovations presented this review, aim chart course future promising field. may be first that highlights current state applications using transformers, sources, sets stage evolution, encouraging further exploration care.

Language: Английский

---

Vision-language models for medical report generation and visual question answering: a review

Frontiers in Artificial Intelligence, Journal Year: 2024, Volume and Issue: 7

Published: Nov. 19, 2024

Medical vision-language models (VLMs) combine computer vision (CV) and natural language processing (NLP) to analyze visual textual medical data. Our paper reviews recent advancements in developing VLMs specialized for healthcare, focusing on publicly available designed report generation question answering (VQA). We provide background NLP CV, explaining how techniques from both fields are integrated into VLMs, with data often fused using Transformer-based architectures enable effective learning multimodal Key areas we address include the exploration of 18 public datasets, in-depth analyses pre-training strategies 16 noteworthy comprehensive discussion evaluation metrics assessing VLMs' performance VQA. also highlight current challenges facing VLM development, including limited availability, concerns privacy, lack proper metrics, among others, while proposing future directions these obstacles. Overall, our review summarizes progress harness improved healthcare applications.

Language: Английский

---

Innovations in heart failure management: The role of cutting-edge biomarkers and multi-omics integration

Journal of Molecular and Cellular Cardiology Plus, Journal Year: 2025, Volume and Issue: 11, P. 100290 - 100290

Published: March 1, 2025

Heart failure (HF) remains a major cause of morbidity and mortality worldwide represents challenge for diagnosis, prognosis treatment due to its heterogeneity. Traditional biomarkers such as BNP NT-proBNP are valuable but insufficient capture the complexity HF, especially phenotypes HF with preserved ejection fraction (HFpEF). Recent advances in multi-omics technology novel cell-free DNA (cfDNA), microRNAs (miRNAs), ST2 galectin-3 offer transformative potential management. This review explores integration these innovative into clinical practice highlights their benefits, improved diagnostic accuracy, enhanced risk stratification non-invasive monitoring capabilities. By leveraging approaches, including lipidomics metabolomics, clinicians can uncover new pathways, refine classification phenotypes, develop personalized therapeutic strategies tailored individual patient profiles. Remarkable proteomics metabolomics have identified associated key mechanisms mitochondrial dysfunction, inflammation fibrosis, paving way targeted therapies early interventions. Despite promising results, significant challenges remain translating findings routine care, high costs, technical limitations need large-scale validation studies. report argues an integrative, multi-omics-based model overcome obstacles emphasizes importance collaboration between researchers, policy makers. linking science practical applications, approaches redefine management lead better outcomes more sustainable healthcare systems.

Language: Английский

---

Leveraging multi-omics and machine learning approaches in malting barley research: From farm cultivation to the final products

Current Plant Biology, Journal Year: 2024, Volume and Issue: 39, P. 100362 - 100362

Published: June 22, 2024

This study focuses on the potential of multi-omics and machine learning approaches in improving our understanding malting processes cultivation systems barley. The omics approach has been used to explore biomarkers associated with desired sensory characteristics barley, enabling applications specific treatments modify diastatic power, enzyme activity, color, aroma compounds. Moreover, integration barley researches significantly enhanced knowledge physiology, cultivation, processing for more efficient sustainable production industry. cutting-edge vision high-throughput phenotyping technologies additionally revolutionize assessment physical biochemical traits In addition, harnessing integrative predict consumer acceptability, assess physicochemical colorimetric properties malt extracts discussed. Current survey showed that ML-driven predictive maintenance is revolutionizing industry by not only enhancing equipment performance but also minimizing operational costs reducing unplanned downtime. promises advancements opens avenues future

Language: Английский

---

Integrating accounting models with supply chain management in the aerospace industry: A strategic approach to enhancing efficiency and reducing costs in the U.S

World Journal of Advanced Research and Reviews, Journal Year: 2024, Volume and Issue: 21(3), P. 1476 - 1489

Published: March 20, 2024

This concept paper proposes the integration of accounting models with supply chain management in aerospace industry as a strategic approach to enhancing efficiency and reducing costs United States. The sector faces numerous challenges, including complex chains, stringent regulatory requirements, cost pressures. By combining principles strategies, this aims optimize operations, improve financial transparency, foster collaboration among stakeholders. Key objectives initiative include streamlining processes, decision-making capabilities, mitigating risks associated disruptions. leveraging such activity-based costing, lean accounting, performance measurement systems, organizations can gain insights into structures, identify inefficiencies, allocate resources effectively across chain. Moreover, integrating enables real-time monitoring metrics, enabling timely interventions adjustments achieve goals. also facilitates better coordination between finance operations teams, leading improved communication, alignment objectives, ultimately, enhanced organizational performance. In context industry, where precision, reliability, cost-efficiency are paramount, integrated offers significant benefits. It companies inventory management, minimize waste, opportunities for savings throughout Additionally, by fostering culture continuous improvement data-driven decision-making, adapt more market dynamics competitive edge. outlines theoretical framework practical implications industry. Through case studies, best practices, implementation it provides roadmap embrace realize its full potential U.S. sector.

Language: Английский

---

Mechanisms and technologies in cancer epigenetics

Frontiers in Oncology, Journal Year: 2025, Volume and Issue: 14

Published: Jan. 7, 2025

Cancer's epigenetic landscape, a labyrinthine tapestry of molecular modifications, has long captivated researchers with its profound influence on gene expression and cellular fate. This review discusses the intricate mechanisms underlying cancer epigenetics, unraveling complex interplay between DNA methylation, histone chromatin remodeling, non-coding RNAs. We navigate through tumultuous seas dysregulation, exploring how these processes conspire to silence tumor suppressors unleash oncogenic potential. The narrative pivots cutting-edge technologies, revolutionizing our ability decode epigenome. From granular insights single-cell epigenomics holistic view offered by multi-omics approaches, we examine tools are reshaping understanding heterogeneity evolution. also highlights emerging techniques, such as spatial long-read sequencing, which promise unveil hidden dimensions regulation. Finally, probed transformative potential CRISPR-based epigenome editing computational analysis transmute raw data into biological insights. study seeks synthesize comprehensive yet nuanced contemporary landscape future directions research.

Language: Английский

---

From tedious to targeted: Optimizing oral cancer research with Consensus AI

Oral Oncology Reports, Journal Year: 2024, Volume and Issue: 10, P. 100383 - 100383

Published: April 12, 2024

Barriers which include subjective biases, overabundance of data, and budget limitations impede oral cancer research. Conventional techniques consume an extensive amount time, are biased, have limitations. Consensus AI, on the other hand, presents a viable alternative by effectively sorting through enormous datasets utilizing several AI algorithms. reduces increases efficiency, improves article selection dependability merging variety models. Research may be able to make more accurate predictions get deeper insights because its capacity combine different data sources apply ensemble learning. The editorial explore role in method optimization.

Language: Английский

---

Self-Normalizing Foundation Model for Enhanced Multi-Omics Data Analysis in Oncology

Published: Jan. 1, 2024

Language: Английский

---

Big Data Management and Analytics in Drug Research

Advances in healthcare information systems and administration book series, Journal Year: 2024, Volume and Issue: unknown, P. 90 - 115

Published: April 22, 2024

Big data plays a crucial role in drug discovery, simplifying and streamlining the complex process by leveraging large datasets both chemical biological aspects. From target validation to clinical trials, big aids various stages of development, enhancing efficiency support through AI applications. This integration with tools significantly improves discovery process, making it less time-consuming more effective. The chapter explores significance research, emphasizing its application hit identification for therapeutic targets success stories associated screening platforms. It delves into foundations elucidating significance, challenges, potential, while navigating intricacies collection, integration, storage, management. highlights importance quality, security, governance.

Language: Английский

---

Machine Learning based Suggestion Method for Land Suitability Assessment and Production Sustainability

Natural and Engineering Sciences, Journal Year: 2024, Volume and Issue: 9(2), P. 55 - 72

Published: Oct. 17, 2024

The global population is projected to increase by an additional two billion 2050, as per the assessment conducted Food and Agriculture Management. However, arable land anticipated expand just 5%. Consequently, intelligent effective agricultural practices are essential enhancing farming production. Evaluating rural Land Suitability (LS) a crucial instrument for growth. Numerous novel methods concepts being adopted in agriculture alternatives gathering processing farm data. swift advancement of wireless Sensor Networks (WSN) has prompted creation economical compact sensor gadgets, with Internet Things (IoT) serving viable automation decision-making farmers. To evaluate LS, this study offers expert system integrating networked sensors Machine Learning (ML) technologies, including neural networks. suggested approach would assist farmers evaluating cultivating across four decision categories: very appropriate, suitable, somewhat inappropriate. This evaluation based on data gathered from various devices training. findings achieved MLP concealed layers demonstrate efficacy multiclass categorization method compared other current models. trained will assess future evaluations categorize post-cultivation.

Language: Английский

---