Annual Review of Biomedical Data Science

The use of machine learning (ML) in health care raises numerous ethical concerns, especially as models can amplify existing health inequities. Here, we outline ethical considerations for equitable ML in the advancement of health care. Specifically, we frame ethics of ML in health care through the lens of social justice. We describe ongoing efforts and outline challenges in a proposed pipeline of ethical ML in health, ranging from problem selection to post-deployment considerations. We close by summarizing recommendations to address these challenges.

Irene Y. Chen

Emma Pierson

Sherri Rose

Shalmali Joshi

Kadija Ferryman

Marzyeh Ghassemi

Annu. Rev. Biomed. Data Sci.

Ethical Machine Learning in Healthcare

医疗保健中的伦理机器学习

Cellular differentiation is a common underlying feature of all multicellular organisms through which naïve cells progressively become fate restricted and develop into mature cells with specialized functions. A comprehensive understanding of the regulatory mechanisms of cell fate choices during development, regeneration, homeostasis, and disease is a central goal of modern biology. Ongoing rapid advances in single-cell biology are enabling the exploration of cell fate specification at unprecedented resolution. Here, we review single-cell RNA sequencing and sequencing of other modalities as methods to elucidate the molecular underpinnings of lineage specification. We specifically discuss how the computational tools available to reconstruct lineage trajectories, quantify cell fate bias, and perform dimensionality reduction for data visualization are providing new mechanistic insights into the process of cell fate decision. Studying cellular differentiation using single-cell genomic tools is paving the way for a detailed understanding of cellular behavior in health and disease.

None Sagar

Dominic Grün

Deciphering Cell Fate Decision by Integrated Single-Cell Sequencing Analysis

解码细胞命运决定的综合单细胞测序分析

Spatial transcriptomics (ST) brings new dimensions to the analysis of single-cell data. While some methods for data analysis can be ported over without major modifications, they are the exception rather than the rule. Trajectory inference (TI) methods in particular can suffer from significant challenges due to spatial batch effects in ST data. These can add independent sources of noise to each time point. Pioneering methods for TI on ST data have focused primarily on addressing the batch effects in physical arrangement, i.e., where tissues are deformed in different ways at different time points. However, other challenges arise due to the measurement granularity of ST technologies, as well as a bias from slicing. In this review, we examine the sources of these challenges, and we explore how they are addressed with current state-of-the-art STTI methods. We conclude by highlighting some opportunities for future method development.

Matthieu Heitz

Yujia Ma

Sharvaj Kubal

Geoffrey Schiebinger

Spatial Transcriptomics Brings New Challenges and Opportunities for Trajectory Inference

空间转录组学为轨迹推断带来新的挑战与机遇

Coral reefs are home to over two million species and provide habitat for roughly 25% of all marine animals, but they are being severely threatened by pollution and climate change. A large amount of genomic, transcriptomic, and other omics data is becoming increasingly available from different species of reef-building corals, the unicellular dinoflagellates, and the coral microbiome (bacteria, archaea, viruses, fungi, etc.). Such new data present an opportunity for bioinformatics researchers and computational biologists to contribute to a timely, compelling, and urgent investigation of critical factors that influence reef health and resilience.

Lenore J. Cowen

Hollie M. Putnam

Bioinformatics of Corals: Investigating Heterogeneous Omics Data from Coral Holobionts for Insight into Reef Health and Resilience

珊瑚生物信息学：探究珊瑚共生体的异质性组学数据以洞察礁体健康与韧性

Liquid biopsy is the analysis of materials shed by tumors into circulation, such as circulating tumor cells, nucleic acids, and extracellular vesicles (EVs), for the diagnosis and management of cancer. These assays have rapidly evolved with recent FDA approvals of single biomarkers in patients with advanced metastatic disease. However, they have lacked sensitivity or specificity as a diagnostic in early-stage cancer, primarily due to low concentrations in circulating plasma. EVs, membrane-enclosed nanoscale vesicles shed by tumor and other cells into circulation, are a promising liquid biopsy analyte owing to their protein and nucleic acid cargoes carried from their mother cells, their surface proteins specific to their cells of origin, and their higher concentrations over other noninvasive biomarkers across disease stages. Recently, the combination of EVs with non-EV biomarkers has driven improvements in sensitivity and accuracy; this has been fueled by the use of machine learning (ML) to algorithmically identify and combine multiple biomarkers into a composite biomarker for clinical prediction. This review presents an analysis of EV isolation methods, surveys approaches for and issues with using ML in multianalyte EV datasets, and describes best practices for bringing multianalyte liquid biopsy to clinical implementation.

Andrew A. Lin

Vivek Nimgaonkar

David Issadore

Erica L. Carpenter

Extracellular Vesicle–Based Multianalyte Liquid Biopsy as a Diagnostic for Cancer

基于细胞外囊泡的多分析物液体活检作为癌症诊断方法

The rapidly growing scale and variety of biomedical data repositories raise important privacy concerns. Conventional frameworks for collecting and sharing human subject data offer limited privacy protection, often necessitating the creation of data silos. Privacy-enhancing technologies (PETs) promise to safeguard these data and broaden their usage by providing means to share and analyze sensitive data while protecting privacy. Here, we review prominent PETs and illustrate their role in advancing biomedicine. We describe key use cases of PETs and their latest technical advances and highlight recent applications of PETs in a range of biomedical domains. We conclude by discussing outstanding challenges and social considerations that need to be addressed to facilitate a broader adoption of PETs in biomedical data science.

Hyunghoon Cho

David Froelicher

Natnatee Dokmai

Anupama Nandi

Shuvom Sadhuka

Matthew M. Hong

Bonnie Berger

Privacy-Enhancing Technologies in Biomedical Data Science

生物医学数据科学中的隐私增强技术

Building longitudinal population cohorts in Africa for coordinated research and surveillance can influence the setting of national health priorities, lead to the introduction of appropriate interventions, and provide evidence for targeted treatment, leading to better health across the continent. However, compared to cohorts from the global north, longitudinal continental African population cohorts remain scarce, are relatively small in size, and lack data complexity. As infections and noncommunicable diseases disproportionately affect Africa's approximately 1.4 billion inhabitants, African cohorts present a unique opportunity for research and surveillance. High genetic diversity in African populations and multiomic research studies, together with detailed phenotyping and clinical profiling, will be a treasure trove for discovery. The outcomes, including novel drug targets, biological pathways for disease, and gene-environment interactions, will boost precision medicine approaches, not only in Africa but across the globe.

Michèle Ramsay

Amelia C. Crampin

Ayaga A. Bawah

Evelyn Gitau

Kobus Herbst

The Value Proposition of Coordinated Population Cohorts Across Africa

非洲协调人口队列的价值主张

Disease trajectories, defined as sequential, directional disease associations, have become an intense research field driven by the availability of electronic population-wide healthcare data and sufficient computational power. Here, we provide an overview of disease trajectory studies with a focus on European work, including ontologies used as well as computational methodologies for the construction of disease trajectories. We also discuss different applications of disease trajectories from descriptive risk identification to disease progression, patient stratification, and personalized predictions using machine learning. We describe challenges and opportunities in the area that eventually will benefit from initiatives such as the European Health Data Space, which, with time, will make it possible to analyze data from cohorts comprising hundreds of millions of patients.

Isabella Friis Jørgensen

Amalie Dahl Haue

Davide Placido

Jessica Xin Hjaltelin

Søren Brunak

Disease Trajectories from Healthcare Data: Methodologies, Key Results, and Future Perspectives

医疗数据中的疾病轨迹：方法论、关键结果与未来展望

Alzheimer's disease (AD) is a critical national concern, affecting 5.8 million people and costing more than $250 billion annually. However, there is no available cure. Thus, effective strategies are in urgent need to discover AD biomarkers for disease early detection and drug development. In this review, we study AD from a biomedical data scientist perspective to discuss the four fundamental components in AD research: genetics (G), molecular multiomics (M), multimodal imaging biomarkers (B), and clinical outcomes (O) (collectively referred to as the GMBO framework). We provide a comprehensive review of common statistical and informatics methodologies for each component within the GMBO framework, accompanied by the major findings from landmark AD studies. Our review highlights the potential of multimodal biobank data in addressing key challenges in AD, such as early diagnosis, disease heterogeneity, and therapeutic development. We identify major hurdles in AD research, including data scarcity and complexity, and advocate for enhanced collaboration, data harmonization, and advanced modeling techniques. This review aims to be an essential guide for understanding current biomedical data science strategies in AD research, emphasizing the need for integrated, multidisciplinary approaches to advance our understanding and management of AD.

Jingxuan Bao

Brian N. Lee

Junhao Wen

Mansu Kim

Shizhuo Mu

Shu Yang

Christos Davatzikos

Qi Long

Marylyn D. Ritchie

Li Shen

Employing Informatics Strategies in Alzheimer's Disease Research: A Review from Genetics, Multiomics, and Biomarkers to Clinical Outcomes

在阿尔茨海默病研究中应用信息学策略：从遗传学、多组学和生物标志物到临床结局的综述

Abbreviation	Annu. Rev. Biomed. Data Sci.
Journal Impact	6.88
Quartiles(Global)	MATHEMATICAL & COMPUTATIONAL BIOLOGY(Q1)

ISSN	2574-3414
h-index	22

Publication Information	Publisher: Annual Reviews Inc.，Publishing cycle: ，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 2018，Proportion of original research papers: 100.00%，Self Citation Rate:， Gold OA Rate: 0.00%

Annual Review of Biomedical Data Science

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Ethical Machine Learning in Healthcare

Deciphering Cell Fate Decision by Integrated Single-Cell Sequencing Analysis

Spatial Transcriptomics Brings New Challenges and Opportunities for Trajectory Inference

Bioinformatics of Corals: Investigating Heterogeneous Omics Data from Coral Holobionts for Insight into Reef Health and Resilience

Extracellular Vesicle–Based Multianalyte Liquid Biopsy as a Diagnostic for Cancer

Spatial Transcriptomics Brings New Challenges and Opportunities for Trajectory Inference

Privacy-Enhancing Technologies in Biomedical Data Science

The Value Proposition of Coordinated Population Cohorts Across Africa

Disease Trajectories from Healthcare Data: Methodologies, Key Results, and Future Perspectives

Employing Informatics Strategies in Alzheimer's Disease Research: A Review from Genetics, Multiomics, and Biomarkers to Clinical Outcomes