PPML-Omics: A privacy-preserving federated machine learning method protects patients' privacy in omic data

AI-powered precision medicine: utilizing genetic risk factor optimization to revolutionize healthcare

The convergence of artificial intelligence (AI) and biomedical data is transforming precision medicine by enabling the use of genetic risk factors (GRFs) for customized healthcare services based on individual needs. Although GRFs play an essential role in disease susceptibility, progression, and therapeutic outcomes, a gap exists in exploring their contribution to AI-powered precision medicine. This paper addresses this need by investigating the significance and potential of utilizing GRFs with AI in the medical field. We examine their applications, particularly emphasizing their impact on disease prediction, treatment personalization, and overall healthcare improvement. This review explores the application of AI algorithms to optimize the use of GRFs, aiming to advance precision medicine in disease screening, patient stratification, drug discovery, and understanding disease mechanisms. Through a variety of case studies and examples, we demonstrate the potential of incorporating GRFs facilitated by AI into medical practice, resulting in more precise diagnoses, targeted therapies, and improved patient outcomes. This review underscores the potential of GRFs, empowered by AI, to enhance precision medicine by improving diagnostic accuracy, treatment precision, and individualized healthcare solutions.

Federated Machine Learning Enables Risk Management and Privacy Protection in Water Quality

Real-time water quality risk management in wastewater treatment plants (WWTPs) requires extensive data, and data sharing is still just a slogan due to data privacy issues. Here we show an adaptive water system federated averaging (AWSFA) framework based on federated learning (FL), where the model does not access the data but uses parameters trained by the raw data. The study collected data from six WWTPs between 2018 and 2024, and developed 10 machine learning models for each effluent indicator, with the best performance bidirectional long-term memory network (BM) as Baseline. Compared to direct training and classical federated averaging (FedAvg), AWSFA reduces the mean absolute percentage error (MAPE) of BM significantly. Analysis of input dimensions, data set size, and interpretability reveals that the performance improvement is not driven by the complexity of algorithm design but by data sharing via parameter sharing. By simulation of possible disturbances in water quality, the model remained robust when 50% of key features were missing. The study provides the way forward for data sharing and privacy preservation of water systems and offers theoretical support for the digital transformation of WWTPs in the era of big data and big model.

From challenges and pitfalls to recommendations and opportunities: Implementing federated learning in healthcare

Federated learning holds great potential for enabling large-scale healthcare research and collaboration across multiple centers while ensuring data privacy and security are not compromised. Although numerous recent studies suggest or utilize federated learning based methods in healthcare, it remains unclear which ones have potential clinical utility. This review paper considers and analyzes the most recent studies up to May 2024 that describe federated learning based methods in healthcare. After a thorough review, we find that the vast majority are not appropriate for clinical use due to their methodological flaws and/or underlying biases which include but are not limited to privacy concerns, generalization issues, and communication costs. As a result, the effectiveness of federated learning in healthcare is significantly compromised. To overcome these challenges, we provide recommendations and promising opportunities that might be implemented to resolve these problems and improve the quality of model development in federated learning with healthcare.

Privacy-preserving approach for IoT networks using statistical learning with optimization algorithm on high-dimensional big data environment

In the present digital scenario, the explosion of Internet of Things (IoT) devices makes massive volumes of high-dimensional data, presenting significant data and privacy security challenges. As IoT networks enlarge, certifying sensitive data privacy while still employing data analytics authority is vital. In the period of big data, statistical learning has seen fast progressions in methodological practical and innovation applications. Privacy-preserving machine learning (ML) training in the development of aggregation permits a demander to firmly train ML techniques with the delicate data of IoT collected from IoT devices. The current solution is primarily server-assisted and fails to address collusion attacks among servers or data owners. Additionally, it needs to adequately account for the complex dynamics of the IoT environment. In a large-sized big data environment, privacy protection challenges are additionally enlarged. The data dimensional can have vague meaningful patterns, making it challenging to certify that privacy-preserving models do not destroy the efficacy and accuracy of statistical methods. This manuscript presents a Privacy-Preserving Statistical Learning with an Optimization Algorithm for a High-Dimensional Big Data Environment (PPSLOA-HDBDE) approach. The primary purpose of the PPSLOA-HDBDE approach is to utilize advanced optimization and ensemble techniques to ensure data confidentiality while maintaining analytical efficacy. In the primary stage, the linear scaling normalization (LSN) method scales the input data. Besides, the sand cat swarm optimizer (SCSO)-based feature selection (FS) process is employed to decrease the high dimensionality problem. Moreover, the recognition of intrusion detection takes place by using an ensemble of temporal convolutional network (TCN), multi-layer auto-encoder (MAE), and extreme gradient boosting (XGBoost) models. Lastly, the hyperparameter tuning of the three models is accomplished by utilizing an improved marine predator algorithm (IMPA) method. An extensive range of experimentations is performed to improve the PPSLOA-HDBDE technique's performance, and the outcomes are examined under distinct measures. The performance validation of the PPSLOA-HDBDE technique illustrated a superior accuracy value of 99.49% over existing models.

An AI Agent for Fully Automated Multi-Omic Analyses

With the fast-growing and evolving omics data, the demand for streamlined and adaptable tools to handle bioinformatics analysis continues to grow. In response to this need, Automated Bioinformatics Analysis (AutoBA) is introduced, an autonomous AI agent designed explicitly for fully automated multi-omic analyses based on large language models (LLMs). AutoBA simplifies the analytical process by requiring minimal user input while delivering detailed step-by-step plans for various bioinformatics tasks. AutoBA's unique capacity to self-design analysis processes based on input data variations further underscores its versatility. Compared with online bioinformatic services, AutoBA offers multiple LLM backends, with options for both online and local usage, prioritizing data security and user privacy. In comparison to ChatGPT and open-source LLMs, an automated code repair (ACR) mechanism in AutoBA is designed to improve its stability in automated end-to-end bioinformatics analysis tasks. Moreover, different from the predefined pipeline, AutoBA has adaptability in sync with emerging bioinformatics tools. Overall, AutoBA represents an advanced and convenient tool, offering robustness and adaptability for conventional multi-omic analyses.

Radiogenomics: bridging the gap between imaging and genomics for precision oncology

Genomics allows the tracing of origin and evolution of cancer at molecular scale and underpin modern cancer diagnosis and treatment systems. Yet, molecular biomarker-guided clinical decision-making encounters major challenges in the realm of individualized medicine, consisting of the invasiveness of procedures and the sampling errors due to high tumor heterogeneity. By contrast, medical imaging enables noninvasive and global characterization of tumors at a low cost. In recent years, radiomics has overcomes the limitations of human visual evaluation by high-throughput quantitative analysis, enabling the comprehensive utilization of the vast amount of information underlying radiological images. The cross-scale integration of radiomics and genomics (hereafter radiogenomics) has the enormous potential to enhance cancer decoding and act as a catalyst for digital precision medicine. Herein, we provide a comprehensive overview of the current framework and potential clinical applications of radiogenomics in patient care. We also highlight recent research advances to illustrate how radiogenomics can address common clinical problems in solid tumors such as breast cancer, lung cancer, and glioma. Finally, we analyze existing literature to outline challenges and propose solutions, while also identifying future research pathways. We believe that the perspectives shared in this survey will provide a valuable guide for researchers in the realm of radiogenomics aiming to advance precision oncology.

Comprehensive review on single-cell RNA sequencing: A new frontier in Alzheimer's disease research

Alzheimer's disease (AD) is a multifaceted neurodegenerative condition marked by gradual cognitive deterioration and the loss of neurons. While conventional bulk RNA sequencing techniques have shed light on AD pathology, they frequently obscure the cellular diversity within brain tissues. The advent of single-cell RNA sequencing (scRNA-seq) has transformed our capability to analyze the cellular composition of AD, allowing for the detection of unique cell populations, rare cell types, and gene expression alterations at an individual cell level. This review examines the use of scRNA-seq in AD research, focusing on its contributions to understanding cellular diversity, disease progression, and potential therapeutic targets. We discuss key technological innovations, data analysis techniques, and challenges associated with scRNA-seq in studying AD. Furthermore, we highlight recent studies that have utilized scRNA-seq to identify novel biomarkers, uncover disease-associated pathways, and elucidate the role of non-neuronal cells, such as microglia and astrocytes, in AD pathogenesis. By providing a comprehensive overview of advancements in scRNA-seq for unraveling cellular heterogeneity in AD, this review highlights the transformative impact of scRNA-seq on our comprehension of disease mechanisms and the creation of targeted treatments.

Pre-trained multimodal large language model enhances dermatological diagnosis using SkinGPT-4

Large language models (LLMs) are seen to have tremendous potential in advancing medical diagnosis recently, particularly in dermatological diagnosis, which is a very important task as skin and subcutaneous diseases rank high among the leading contributors to the global burden of nonfatal diseases. Here we present SkinGPT-4, which is an interactive dermatology diagnostic system based on multimodal large language models. We have aligned a pre-trained vision transformer with an LLM named Llama-2-13b-chat by collecting an extensive collection of skin disease images (comprising 52,929 publicly available and proprietary images) along with clinical concepts and doctors' notes, and designing a two-step training strategy. We have quantitatively evaluated SkinGPT-4 on 150 real-life cases with board-certified dermatologists. With SkinGPT-4, users could upload their own skin photos for diagnosis, and the system could autonomously evaluate the images, identify the characteristics and categories of the skin conditions, perform in-depth analysis, and provide interactive treatment recommendations.

Patient privacy in AI-driven omics methods

Artificial intelligence (AI) in omics analysis raises privacy threats to patients. Here, we briefly discuss risk factors to patient privacy in data sharing, model training, and release, as well as methods to safeguard and evaluate patient privacy in AI-driven omics methods.