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BackgroundEvidence has accumulated from observational studies that people eating more fruits and vegetables, which are rich in beta-carotene (a violet to yellow plant pigment that acts as an antioxidant and can be converted to vitamin A by enzymes in the intestinal wall and liver) and retinol (an alcohol chemical form of vitamin A), and people having higher serum beta-carotene concentrations had lower rates of lung cancer. The Beta-Carotene and Retinol Efficacy Trial (CARET) tested the combination of 30 mg beta-carotene and 25,000 IU retinyl palmitate (vitamin A) taken daily against placebo in 18314 men and women at high risk of developing lung cancer. The CARET intervention was stopped 21 months early because of clear evidence of no benefit and substantial evidence of possible harm; there were 28% more lung cancers and 17% more deaths in the active intervention group (active = the daily combination of 30 mg beta-carotene and 25,000 IU retinyl palmitate). Promptly after the January 18, 1996, announcement that the CARET active intervention had been stopped, we published preliminary findings from CARET regarding cancer, heart disease, and total mortality.PurposeWe present for the first time results based on the pre-specified analytic method, details about risk factors for lung cancer, and analyses of subgroups and of factors that possibly influence response to the intervention.MethodsCARET was a randomized, double-blinded, placebo-controlled chemoprevention trial, initiated with a pilot phase and then expanded 10-fold at six study centers. Cigarette smoking history and status and alcohol intake were assessed through participant self-report. Serum was collected from the participants at base line and periodically after randomization and was analyzed for beta-carotene concentration. An Endpoints Review Committee evaluated endpoint reports, including pathologic review of tissue specimens. The primary analysis is a stratified logrank test for intervention arm differences in lung cancer incidence, with weighting linearly to hypothesized full effect at 24 months after randomization. Relative risks (RRs) were estimated by use of Cox regression models; tests were performed for quantitative and qualitative interactions between the intervention and smoking status or alcohol intake. O'Brien-Fleming boundaries were used for stopping criteria at interim analyses. Statistical significance was set at the .05 alpha value, and all P values were derived from two-sided statistical tests.ResultsAccording to CARET's pre-specified analysis, there was an RR of 1.36 (95% confidence interval [CI] = 1.07-1.73; P = .01) for weighted lung cancer incidence for the active intervention group compared with the placebo group, and RR = 1.59 (95% CI = 1.13-2.23; P = .01) for weighted lung cancer mortality. All subgroups, except former smokers, had a point estimate of RR of 1.10 or greater for lung cancer. There are suggestions of associations of the excess lung cancer incidence with the highest quartile of alcohol intake (RR = 1.99; 95% CI = 1.28-3.09; test for heterogeneity of RR among quartiles of alcohol intake has P = .01, unadjusted for multiple comparisons) and with large-cell histology (RR = 1.89; 95% CI = 1.09-3.26; test for heterogeneity among histologic categories has P = .35), but not with base-line serum beta-carotene concentrations.ConclusionsCARET participants receiving the combination of beta-carotene and vitamin A had no chemopreventive benefit and had excess lung cancer incidence and mortality. The results are highly consistent with those found for beta-carotene in the Alpha-Tocopherol Beta-Carotene Cancer Prevention Study in 29133 male smokers in Finland.

CARET is a chemoprevention trial of beta-carotene and vitamin A with lung cancer as the primary outcome. Participants at high risk for lung cancer are drawn from two populations: asbestos-exposed workers and heavy smokers. The intervention is a daily combination of 30 mg beta-carotene and 25,000 IU vitamin A as retinyl palmitate. Nearly 18,000 participants will be followed for a mean 6 years, yielding over 100,000 person-years of follow-up. We project that this sample size will have 80% power to detect a 23% decrease in the incidence of lung cancer cases. The purpose of this paper is to present the values of the key sample size parameters of CARET; our schemes for monitoring CARET for sample size adequacy, incidence of side effects, and efficacy of the study vitamins; an overview of the data collected; and plans for the primary, secondary, and ancillary analyses to be performed at the end of the trial. These approaches to the design, monitoring, and analysis of CARET are applicable for many other prevention trials.

Genomic and genetic information is rapidly becoming a major element in public health research and emerging public health practice. This symposium reviews the methods, findings, and significance of genome-wide association studies from epidemiological and statistical points of view. We examine infectious and inflammatory components of gene-environment interaction in the respiratory system. We note the need for nutrient and dietary data and many other kinds of environmental exposure data in population-based genomic studies. Then we explore the sufficiency of a well-informed family history for public health and family counseling purposes. Finally, in an era of direct-to-consumer genomic test promotion, we review the evidence on the critical question, will genetic risk profiles motivate individuals and families to choose more healthful behaviors? This symposium builds on the foundation of the symposium on Public Health Genetics in Volume 21 (2000) of the Annual Review of Public Health. [ABSTRACT FROM AUTHOR]

Despite numerous published studies, debate continues regarding the risk of developing lung cancer among men exposed occupationally to asbestos, particularly those without radiographic or functional evidence of asbestosis. The beta-Carotene and Retinol Efficacy Trial (CARET), a study of vitamin supplementation for chemoprevention of lung cancer, has followed 4,060 heavily exposed US men for 9-17 years. Lung cancer incidence for 1989-2002 was analyzed using a stratified proportional hazards model. The study confirmed excessive rates of lung cancer among men with radiographic asbestosis. Comparison of study arms revealed a strong, unanticipated synergy between radiographic profusion category and the active intervention. In the large subgroup of men with normal lung parenchyma on chest radiograph at baseline, there was evidence of exposure-related lung cancer risk: Men with more than 40 years' exposure in high-risk trades had a risk approximately fivefold higher than men with 5-10 years, after adjustment for covariates. The effect in these men was independent of study intervention arm, but pleural plaques on the baseline radiograph and abnormal baseline flow rate were strong independent predictors of subsequent lung cancer. Residual confounding by subclinical asbestosis, exposure to unmeasured lung carcinogens, or differences in smoking are unlikely to explain these observations better than a carcinogenic effect of asbestos per se. [ABSTRACT FROM AUTHOR]

Background: Evidence has accumulated from observational studies that people eating more fruits and vegetables, which are rich in beta-carotene (a violet to yellow plant pigment that acts as an antioxidant and can be converted to vitamin A by enzymes in the intestinal wall and liver) and retinol (an alcohol chemical form of vitamin A), and people having higher serum beta-carotene concentrations had lower rates of lung cancer. The Beta-Carotene and Retinol Efficacy Trial (CARET) tested the combination of 30 mg beta-carotene and 25,000 IU retinyl palmitate (vitamin A) taken daily against placebo in 18314 men and women at high risk of developing lung cancer. The CARET intervention was stopped 21 months early because of clear evidence of no benefit and substantial evidence of possible harm; there were 28% more lung cancers and 17% more deaths in the active intervention group (active = the daily combination of 30 mg beta-carotene and 25,000 IU retinyl palmitate). Promptly after the January 18, 1996, announcement that the CARET active intervention had been stopped, we published preliminary findings from CARET regarding cancer, heart disease, and total mortality.Purpose: We present for the first time results based on the pre-specified analytic method, details about risk factors for lung cancer, and analyses of subgroups and of factors that possibly influence response to the intervention.Methods: CARET was a randomized, double-blinded, placebo-controlled chemoprevention trial, initiated with a pilot phase and then expanded 10-fold at six study centers. Cigarette smoking history and status and alcohol intake were assessed through participant self-report. Serum was collected from the participants at base line and periodically after randomization and was analyzed for beta-carotene concentration. An Endpoints Review Committee evaluated endpoint reports, including pathologic review of tissue specimens. The primary analysis is a stratified logrank test for intervention arm differences in lung cancer incidence, with weighting linearly to hypothesized full effect at 24 months after randomization. Relative risks (RRs) were estimated by use of Cox regression models; tests were performed for quantitative and qualitative interactions between the intervention and smoking status or alcohol intake. O'Brien-Fleming boundaries were used for stopping criteria at interim analyses. Statistical significance was set at the .05 alpha value, and all P values were derived from two-sided statistical tests.Results: According to CARET's pre-specified analysis, there was an RR of 1.36 (95% confidence interval [CI] = 1.07-1.73; P = .01) for weighted lung cancer incidence for the active intervention group compared with the placebo group, and RR = 1.59 (95% CI = 1.13-2.23; P = .01) for weighted lung cancer mortality. All subgroups, except former smokers, had a point estimate of RR of 1.10 or greater for lung cancer. There are suggestions of associations of the excess lung cancer incidence with the highest quartile of alcohol intake (RR = 1.99; 95% CI = 1.28-3.09; test for heterogeneity of RR among quartiles of alcohol intake has P = .01, unadjusted for multiple comparisons) and with large-cell histology (RR = 1.89; 95% CI = 1.09-3.26; test for heterogeneity among histologic categories has P = .35), but not with base-line serum beta-carotene concentrations.Conclusions: CARET participants receiving the combination of beta-carotene and vitamin A had no chemopreventive benefit and had excess lung cancer incidence and mortality. The results are highly consistent with those found for beta-carotene in the Alpha-Tocopherol Beta-Carotene Cancer Prevention Study in 29133 male smokers in Finland. [ABSTRACT FROM AUTHOR]

Just getting patients to stop smoking would drastically reduce the incidence of lung cancer. The next line of defense is early recognition of the high-risk patient, plus an aggressive approach to diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Reasons to fortify flour with vitamin B12 are considered, including the high prevalence of depletion and deficiency of this vitamin that occurs in persons of all ages in resource-poor countries and in the elderly in wealthier countries, and the adverse functional consequences of poor vitamin B12 status. From a global perspective, the main cause of inadequate intake and status is a low intake of animal-source foods; even lacto-ovo vegetarians have lower serum vitamin B12 concentrations than omnivores, and for various reasons many populations have limited consumption of animal-source foods. Infants are vitamin B12-depleted from early infancy if their mothers' vitamin B12 status and intake are poor during pregnancy and lactation. Even in the United States, more than 20% of the elderly have serum vitamin B12 concentrations that indicate depletion, and an additional 6% have deficiency, primarily due to gastric atrophy, which impairs the absorption of the vitamin from food but usually not from supplements or fortified foods. Although the evidence is limited, it shows that fortified flour, consumed as bread, can improve vitamin B12 status. Where vitamin B12 fortification is implemented, the recommendation is to add 20 microg/kg flour, assuming consumption of 75 to 100 g flour per day, to provide 75% to 100% of the Estimated Average Requirement; the amount of the vitamin that can be added is limited by its cost. The effectiveness of this level of addition for improving vitamin B12 status in programs needs to be determined and monitored. In addition, further research should evaluate the bioavailability of the vitamin from fortified flour by elderly people with food cobalamin malabsorption and gastric atrophy. [ABSTRACT FROM AUTHOR]

Despite implementing hundreds of strategies, cancer drug development suffers from a 95% failure rate over 30 years, with only 30% of approved cancer drugs extending patient survival beyond 2.5 months. Adding more criteria without eliminating nonessential ones is impractical and may fall into the "survivorship bias" trap. Machine learning (ML) models may enhance efficiency by saving time and cost. Yet, they may not improve success rate without identifying the root causes of failure. We propose a "STAR-guided ML system" (structure-tissue/cell selectivity-activity relationship) to enhance success rate and efficiency by addressing three overlooked interdependent factors: potency/specificity to the on/off-targets determining efficacy in tumors at clinical doses, on/off-target-driven tissue/cell selectivity influencing adverse effects in the normal organs at clinical doses, and optimal clinical doses balancing efficacy/safety as determined by potency/specificity and tissue/cell selectivity. STAR-guided ML models can directly predict clinical dose/efficacy/safety from five features to design/select the best drugs, enhancing success and efficiency of cancer drug development.

The quick Sequential Organ Failure Assessment (qSOFA) system identifies an individual's risk to progress to poor sepsis-related outcomes using minimal variables. We used Support Vector Machine, Learning Using Concave and Convex Kernels, and Random Forest to predict an increase in qSOFA score using electronic health record (EHR) data, electrocardiograms (ECG), and arterial line signals. We structured physiological signals data in a tensor format and used Canonical Polyadic/Parallel Factors (CP) decomposition for feature reduction. Random Forests trained on ECG data show improved performance after tensor decomposition for predictions in a 6-h time frame (AUROC 0.67 ± 0.06 compared to 0.57 ± 0.08, p = 0.01 ). Adding arterial line features can also improve performance (AUROC 0.69 ± 0.07, p < 0.01 ), and benefit from tensor decomposition (AUROC 0.71 ± 0.07, p = 0.01 ). Adding EHR data features to a tensor-reduced signal model further improves performance (AUROC 0.77 ± 0.06, p < 0.01 ). Despite reduction in performance going from an EHR data-informed model to a tensor-reduced waveform data model, the signals-informed model offers distinct advantages. The first is that predictions can be made on a continuous basis in real-time, and second is that these predictions are not limited by the availability of EHR data. Additionally, structuring the waveform features as a tensor conserves structural and temporal information that would otherwise be lost if the data were presented as flat vectors., (© 2024. The Author(s).)

Non-clear cell renal cell carcinomas (non-ccRCCs) encompass diverse malignant and benign tumors. Refinement of differential diagnosis biomarkers, markers for early prognosis of aggressive disease, and therapeutic targets to complement immunotherapy are current clinical needs. Multi-omics analyses of 48 non-ccRCCs compared with 103 ccRCCs reveal proteogenomic, phosphorylation, glycosylation, and metabolic aberrations in RCC subtypes. RCCs with high genome instability display overexpression of IGF2BP3 and PYCR1. Integration of single-cell and bulk transcriptome data predicts diverse cell-of-origin and clarifies RCC subtype-specific proteogenomic signatures. Expression of biomarkers MAPRE3, ADGRF5, and GPNMB differentiates renal oncocytoma from chromophobe RCC, and PIGR and SOSTDC1 distinguish papillary RCC from MTSCC. This study expands our knowledge of proteogenomic signatures, biomarkers, and potential therapeutic targets in non-ccRCC., Competing Interests: Declaration of interests A.I.N., F.Y., and D.A.P. receive royalties from the University of Michigan for the sale of MSFragger software licences to commercial entities. All licence transactions are managed by the University of Michigan Innovation Partnerships office and all proceeds are subject to university technology transfer policy. Related to this work a provisional patent has been filed by University of Michigan, where A.M.C., A.I.N., S.M.D., R. Mannan, R. Mehra, Y.Z., S.C., A.D., X.W., G.X.L., and Y.H. are named as inventors., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Few studies examining the patient outcomes of concurrent neurological manifestations during acute COVID-19 leveraged multinational cohorts of adults and children or distinguished between central and peripheral nervous system (CNS vs. PNS) involvement. Using a federated multinational network in which local clinicians and informatics experts curated the electronic health records data, we evaluated the risk of prolonged hospitalization and mortality in hospitalized COVID-19 patients from 21 healthcare systems across 7 countries. For adults, we used a federated learning approach whereby we ran Cox proportional hazard models locally at each healthcare system and performed a meta-analysis on the aggregated results to estimate the overall risk of adverse outcomes across our geographically diverse populations. For children, we reported descriptive statistics separately due to their low frequency of neurological involvement and poor outcomes. Among the 106,229 hospitalized COVID-19 patients (104,031 patients ≥18 years; 2,198 patients <18 years, January 2020-October 2021), 15,101 (14%) had at least one CNS diagnosis, while 2,788 (3%) had at least one PNS diagnosis. After controlling for demographics and pre-existing conditions, adults with CNS involvement had longer hospital stay (11 versus 6 days) and greater risk of (Hazard Ratio = 1.78) and faster time to death (12 versus 24 days) than patients with no neurological condition (NNC) during acute COVID-19 hospitalization. Adults with PNS involvement also had longer hospital stay but lower risk of mortality than the NNC group. Although children had a low frequency of neurological involvement during COVID-19 hospitalization, a substantially higher proportion of children with CNS involvement died compared to those with NNC (6% vs 1%). Overall, patients with concurrent CNS manifestation during acute COVID-19 hospitalization faced greater risks for adverse clinical outcomes than patients without any neurological diagnosis. Our global informatics framework using a federated approach (versus a centralized data collection approach) has utility for clinical discovery beyond COVID-19., Competing Interests: All authors report no competing interests or conflicts of interest. JGK reports a consulting relationship with the i2b2-tranSMART Foundation through Invocate, Inc. RB reports being a shareholder of Biomeris s.r.l. and Engenome s.r.l. DAH reports entitled to royalties from the University of Michigan for licensing of the EMERSE "synonyms". AM’s work is being funded by the Federal Ministry of Education and Research (BMBF) in Germany in the framework of the MIRACUM Consortium. AM reports being a shareholder of Biomeris s.r.l. BM reports being co-founder and equity owner from DESKI. DLM has received research support from the National Institutes of Health, Department of Veteran Affairs, and the University of Pittsburgh/Pittsburgh Health Data Alliance outside of this work. PA reports consulting for CCHMC and BCH. NG is a co-founder and equity owner of Datavisyn. ZX has served as a Consultant for Genentech/Roche. The institution of ZX has received research support from the National Institute of Health, the National Multiple Sclerosis Society, Food and Drug Administration, the Pittsburgh Foundation, the PNC Charitable Trust, the Ethel Vincent Trust, and Genentech / Roche., (Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.)

Despite the successes of immunotherapy in cancer treatment over recent decades, less than <10%-20% cancer cases have demonstrated durable responses from immune checkpoint blockade. To enhance the efficacy of immunotherapies, combination therapies suppressing multiple immune evasion mechanisms are increasingly contemplated. To better understand immune cell surveillance and diverse immune evasion responses in tumor tissues, we comprehensively characterized the immune landscape of more than 1,000 tumors across ten different cancers using CPTAC pan-cancer proteogenomic data. We identified seven distinct immune subtypes based on integrative learning of cell type compositions and pathway activities. We then thoroughly categorized unique genomic, epigenetic, transcriptomic, and proteomic changes associated with each subtype. Further leveraging the deep phosphoproteomic data, we studied kinase activities in different immune subtypes, which revealed potential subtype-specific therapeutic targets. Insights from this work will facilitate the development of future immunotherapy strategies and enhance precision targeting with existing agents., Competing Interests: Declaration of interests R. Sebra is currently a paid consultant and equity holder at GeneDx. L.C.C. is a founder and member of the board of directors of Agios Pharmaceuticals; is a founder and receives research support from Petra Pharmaceuticals; has equity in and consults for Cell Signaling Technologies, Volastra, Larkspur, and 1 Base Pharmaceuticals; and consults for Loxo-Lilly. J.L.J. has received consulting fees from Scorpion Therapeutics and Volastra Therapeutics. T.M.Y. is a co-founder and stockholder of DeStroke., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Since 2010, the Human Proteome Project (HPP), the flagship initiative of the Human Proteome Organization (HUPO), has pursued two goals: (1) to credibly identify the protein parts list and (2) to make proteomics an integral part of multiomics studies of human health and disease. The HPP relies on international collaboration, data sharing, standardized reanalysis of MS data sets by PeptideAtlas and MassIVE-KB using HPP Guidelines for quality assurance, integration and curation of MS and non-MS protein data by neXtProt, plus extensive use of antibody profiling carried out by the Human Protein Atlas. According to the neXtProt release 2023-04-18, protein expression has now been credibly detected (PE1) for 18,397 of the 19,778 neXtProt predicted proteins coded in the human genome (93%). Of these PE1 proteins, 17,453 were detected with mass spectrometry (MS) in accordance with HPP Guidelines and 944 by a variety of non-MS methods. The number of neXtProt PE2, PE3, and PE4 missing proteins now stands at 1381. Achieving the unambiguous identification of 93% of predicted proteins encoded from across all chromosomes represents remarkable experimental progress on the Human Proteome parts list. Meanwhile, there are several categories of predicted proteins that have proved resistant to detection regardless of protein-based methods used. Additionally there are some PE1-4 proteins that probably should be reclassified to PE5, specifically 21 LINC entries and ∼30 HERV entries; these are being addressed in the present year. Applying proteomics in a wide array of biological and clinical studies ensures integration with other omics platforms as reported by the Biology and Disease-driven HPP teams and the antibody and pathology resource pillars. Current progress has positioned the HPP to transition to its Grand Challenge Project focused on determining the primary function(s) of every protein itself and in networks and pathways within the context of human health and disease.

Background: Omics characterization of pancreatic adenocarcinoma tissue is complicated by the highly heterogeneous and mixed populations of cells. We evaluate the feasibility and potential benefit of using a coring method to enrich specific regions from bulk tissue and then perform proteogenomic analyses., Methods: We used the Biopsy Trifecta Extraction (BioTExt) technique to isolate cores of epithelial-enriched and stroma-enriched tissue from pancreatic tumor and adjacent tissue blocks. Histology was assessed at multiple depths throughout each core. DNA sequencing, RNA sequencing, and proteomics were performed on the cored and bulk tissue samples. Supervised and unsupervised analyses were performed based on integrated molecular and histology data., Results: Tissue cores had mixed cell composition at varying depths throughout. Average cell type percentages assessed by histology throughout the core were better associated with KRAS variant allele frequencies than standard histology assessment of the cut surface. Clustering based on serial histology data separated the cores into three groups with enrichment of neoplastic epithelium, stroma, and acinar cells, respectively. Using this classification, tumor overexpressed proteins identified in bulk tissue analysis were assigned into epithelial- or stroma-specific categories, which revealed novel epithelial-specific tumor overexpressed proteins., Conclusions: Our study demonstrates the feasibility of multi-omics data generation from tissue cores, the necessity of interval H&E stains in serial histology sections, and the utility of coring to improve analysis over bulk tissue data., (© 2024. The Author(s).)

Electronic health records (EHRs) contain a wealth of information that can be used to further precision health. One particular data element in EHRs that is not only under-utilized but oftentimes unaccounted for is missing data. However, missingness can provide valuable information about comorbidities and best practices for monitoring patients, which could save lives and reduce burden on the healthcare system. We characterize patterns of missing data in laboratory measurements collected at the University of Pennsylvania Hospital System from long-term COVID-19 patients and focus on the changes in these patterns between 2020 and 2021. We investigate how these patterns are associated with comorbidities such as acute respiratory distress syndrome (ARDS), and 90-day mortality in ARDS patients. This work displays how knowledge and experience can change the way clinicians and hospitals manage a novel disease. It can also provide insight into best practices when it comes to patient monitoring to improve outcomes., (©2023 AMIA - All rights reserved.)

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancer types, partly because it is frequently identified at an advanced stage, when surgery is no longer feasible. Therefore, early detection using minimally invasive methods such as blood tests may improve outcomes. However, studies to discover molecular signatures for the early detection of PDAC using blood tests have only been marginally successful. In the current study, a quantitative glycoproteomic approach via data-independent acquisition mass spectrometry was utilized to detect glycoproteins in 29 patient-matched PDAC tissues and sera. A total of 892 N-linked glycopeptides originating from 141 glycoproteins had PDAC-associated changes beyond normal variation. We further evaluated the specificity of these serum-detectable glycoproteins by comparing their abundance in 53 independent PDAC patient sera and 65 cancer-free controls. The PDAC tissue-associated glycoproteins we have identified represent an inventory of serum-detectable PDAC-associated glycoproteins as candidate biomarkers that can be potentially used for the detection of PDAC using blood tests., Competing Interests: Conflict of interest The authors declare no competing interests., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

We introduce a pioneering approach that integrates pathology imaging with transcriptomics and proteomics to identify predictive histology features associated with critical clinical outcomes in cancer. We utilize 2,755 H&E-stained histopathological slides from 657 patients across 6 cancer types from CPTAC. Our models effectively recapitulate distinctions readily made by human pathologists: tumor vs. normal (AUROC = 0.995) and tissue-of-origin (AUROC = 0.979). We further investigate predictive power on tasks not normally performed from H&E alone, including TP53 prediction and pathologic stage. Importantly, we describe predictive morphologies not previously utilized in a clinical setting. The incorporation of transcriptomics and proteomics identifies pathway-level signatures and cellular processes driving predictive histology features. Model generalizability and interpretability is confirmed using TCGA. We propose a classification system for these tasks, and suggest potential clinical applications for this integrated human and machine learning approach. A publicly available web-based platform implements these models., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Multisystem inflammatory syndrome in children (MIS-C) is a severe complication of SARS-CoV-2 infection. It remains unclear how MIS-C phenotypes vary across SARS-CoV-2 variants. We aimed to investigate clinical characteristics and outcomes of MIS-C across SARS-CoV-2 eras., Methods: We performed a multicentre observational retrospective study including seven paediatric hospitals in four countries (France, Spain, U.K., and U.S.). All consecutive confirmed patients with MIS-C hospitalised between February 1st, 2020, and May 31st, 2022, were included. Electronic Health Records (EHR) data were used to calculate pooled risk differences (RD) and effect sizes (ES) at site level, using Alpha as reference. Meta-analysis was used to pool data across sites., Findings: Of 598 patients with MIS-C (61% male, 39% female; mean age 9.7 years [SD 4.5]), 383 (64%) were admitted in the Alpha era, 111 (19%) in the Delta era, and 104 (17%) in the Omicron era. Compared with patients admitted in the Alpha era, those admitted in the Delta era were younger (ES -1.18 years [95% CI -2.05, -0.32]), had fewer respiratory symptoms (RD -0.15 [95% CI -0.33, -0.04]), less frequent non-cardiogenic shock or systemic inflammatory response syndrome (SIRS) (RD -0.35 [95% CI -0.64, -0.07]), lower lymphocyte count (ES -0.16 × 10 9 /uL [95% CI -0.30, -0.01]), lower C-reactive protein (ES -28.5 mg/L [95% CI -46.3, -10.7]), and lower troponin (ES -0.14 ng/mL [95% CI -0.26, -0.03]). Patients admitted in the Omicron versus Alpha eras were younger (ES -1.6 years [95% CI -2.5, -0.8]), had less frequent SIRS (RD -0.18 [95% CI -0.30, -0.05]), lower lymphocyte count (ES -0.39 × 10 9 /uL [95% CI -0.52, -0.25]), lower troponin (ES -0.16 ng/mL [95% CI -0.30, -0.01]) and less frequently received anticoagulation therapy (RD -0.19 [95% CI -0.37, -0.04]). Length of hospitalization was shorter in the Delta versus Alpha eras (-1.3 days [95% CI -2.3, -0.4])., Interpretation: Our study suggested that MIS-C clinical phenotypes varied across SARS-CoV-2 eras, with patients in Delta and Omicron eras being younger and less sick. EHR data can be effectively leveraged to identify rare complications of pandemic diseases and their variation over time., Funding: None., Competing Interests: The authors have no conflicts of interests to declare related to the content of this manuscript. JNW has research grant funding from National Heart, Lung, and Blood Institute (NHLBI), the Department of Defense, the Centres for Disease Control (CDC), and Pfizer; has been a consultant for Pfizer; chaired the Independent Events Adjudication Committees for Novartis, Pfizer, and Bristol-Myer-Squibb; and received honoraria from Daiichi Sankyo for service on the Steering Committee of the ENNOBLE-ATE Trial and from UpToDate. GSO has research grant funding from the National Institute of Environmental Health Sciences (NIEHS), National Institutes of Health (NIH), and from the National Cancer Institute (NCI). DAH has research grant funding from the National Center for Advancing Translational Sciences (NCATS). TGG has research grant funding from the Institute of Health Carlos III, the European Regional Development Fund (ERDF), the National Institute of Environmental Health Sciences (NIEHS), National Institutes of Health (NIH), and National Cancer Institute (NCI). KFK has research grant funding from the National Institute of Child Health and Human Development (NIHCD). SEM has research grant funding from the National Center for Advancing Translational Sciences (NCATS). AD has research grant funding from Pfizer. DMT has research grant funding from NIH. AMS has research grant funding from the National Heart, Lung, and Blood Institute (NHLBI) and from the National Center for Advancing Translational Sciences (NCATS). GV has internal research funding from the Centre Hospitalier Universitaire de Bordeaux. ZX has research grant funding from the National Institute of Neurological Disorders and Stroke (NINDS). None of these funding sources had any role in supporting the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the paper for publication. All the other authors have no conflicts of interests to declare., (© 2023 The Authors.)

Background: Characterizing Post-Acute Sequelae of COVID (SARS-CoV-2 Infection), or PASC has been challenging due to the multitude of sub-phenotypes, temporal attributes, and definitions. Scalable characterization of PASC sub-phenotypes can enhance screening capacities, disease management, and treatment planning., Methods: We conducted a retrospective multi-centre observational cohort study, leveraging longitudinal electronic health record (EHR) data of 30,422 patients from three healthcare systems in the Consortium for the Clinical Characterization of COVID-19 by EHR (4CE). From the total cohort, we applied a deductive approach on 12,424 individuals with follow-up data and developed a distributed representation learning process for providing augmented definitions for PASC sub-phenotypes., Findings: Our framework characterized seven PASC sub-phenotypes. We estimated that on average 15.7% of the hospitalized COVID-19 patients were likely to suffer from at least one PASC symptom and almost 5.98%, on average, had multiple symptoms. Joint pain and dyspnea had the highest prevalence, with an average prevalence of 5.45% and 4.53%, respectively., Interpretation: We provided a scalable framework to every participating healthcare system for estimating PASC sub-phenotypes prevalence and temporal attributes, thus developing a unified model that characterizes augmented sub-phenotypes across the different systems., Funding: Authors are supported by National Institute of Allergy and Infectious Diseases, National Institute on Aging, National Center for Advancing Translational Sciences, National Medical Research Council, National Institute of Neurological Disorders and Stroke, European Union, National Institutes of Health, National Center for Advancing Translational Sciences., Competing Interests: Riccardo Bellazzi is shareholder of Biomeris s. r.l. Gilbert Omenn holds patents for U.S. Application No. 16/169,048 Filed: 24-October- 2018 and License 2023–0632 with Radial Therapeutics, Inc.; Invention Disclosure No. 2022-382., (© 2023 The Author(s).)

We characterized a prospective endometrial carcinoma (EC) cohort containing 138 tumors and 20 enriched normal tissues using 10 different omics platforms. Targeted quantitation of two peptides can predict antigen processing and presentation machinery activity, and may inform patient selection for immunotherapy. Association analysis between MYC activity and metformin treatment in both patients and cell lines suggests a potential role for metformin treatment in non-diabetic patients with elevated MYC activity. PIK3R1 in-frame indels are associated with elevated AKT phosphorylation and increased sensitivity to AKT inhibitors. CTNNB1 hotspot mutations are concentrated near phosphorylation sites mediating pS45-induced degradation of β-catenin, which may render Wnt-FZD antagonists ineffective. Deep learning accurately predicts EC subtypes and mutations from histopathology images, which may be useful for rapid diagnosis. Overall, this study identified molecular and imaging markers that can be further investigated to guide patient stratification for more precise treatment of EC., Competing Interests: Declaration of interests The authors declare no competing interests., (Published by Elsevier Inc.)

Physical and psychological symptoms lasting months following an acute COVID-19 infection are now recognized as post-acute sequelae of COVID-19 (PASC). Accurate tools for identifying such patients could enhance screening capabilities for the recruitment for clinical trials, improve the reliability of disease estimates, and allow for more accurate downstream cohort analysis. In this retrospective cohort study, we analyzed the EHR of hospitalized COVID-19 patients across three healthcare systems to develop a pipeline for better identifying patients with persistent PASC symptoms (dyspnea, fatigue, or joint pain) after their SARS-CoV-2 infection. We implemented distributed representation learning powered by the Machine Learning for modeling Health Outcomes (MLHO) to identify novel EHR features that could suggest PASC symptoms outside of typical diagnosis codes. MLHO applies an entropy-based feature selection and boosting algorithms for representation mining. These improved definitions were then used for estimating PASC among hospitalized patients. 30,422 hospitalized patients were diagnosed with COVID-19 across three healthcare systems between March 13, 2020 and February 28, 2021. The mean age of the population was 62.3 years (SD, 21.0 years) and 15,124 (49.7%) were female. We implemented the distributed representation learning technique to augment PASC definitions. These definitions were found to have positive predictive values of 0.73, 0.74, and 0.91 for dyspnea, fatigue, and joint pain, respectively. We estimated that 25 percent (CI 95%: 6-48), 11 percent (CI 95%: 6-15), and 13 percent (CI 95%: 8-17) of hospitalized COVID-19 patients will have dyspnea, fatigue, and joint pain, respectively, 3 months or longer after a COVID-19 diagnosis. We present a validated framework for screening and identifying patients with PASC in the EHR and then use the tool to estimate its prevalence among hospitalized COVID-19 patients., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Strasser et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The 2022 Metrics of the Human Proteome from the HUPO Human Proteome Project (HPP) show that protein expression has now been credibly detected (neXtProt PE1 level) for 18 407 (93.2%) of the 19 750 predicted proteins coded in the human genome, a net gain of 50 since 2021 from data sets generated around the world and reanalyzed by the HPP. Conversely, the number of neXtProt PE2, PE3, and PE4 missing proteins has been reduced by 78 from 1421 to 1343. This represents continuing experimental progress on the human proteome parts list across all the chromosomes, as well as significant reclassifications. Meanwhile, applying proteomics in a vast array of biological and clinical studies continues to yield significant findings and growing integration with other omics platforms. We present highlights from the Chromosome-Centric HPP, Biology and Disease-driven HPP, and HPP Resource Pillars, compare features of mass spectrometry and Olink and Somalogic platforms, note the emergence of translation products from ribosome profiling of small open reading frames, and discuss the launch of the initial HPP Grand Challenge Project, "A Function for Each Protein".

Background: In electronic health records, patterns of missing laboratory test results could capture patients' course of disease as well as ​​reflect clinician's concerns or worries for possible conditions. These patterns are often understudied and overlooked. This study aims to identify informative patterns of missingness among laboratory data collected across 15 healthcare system sites in three countries for COVID-19 inpatients., Methods: We collected and analyzed demographic, diagnosis, and laboratory data for 69,939 patients with positive COVID-19 PCR tests across three countries from 1 January 2020 through 30 September 2021. We analyzed missing laboratory measurements across sites, missingness stratification by demographic variables, temporal trends of missingness, correlations between labs based on missingness indicators over time, and clustering of groups of labs based on their missingness/ordering pattern., Results: With these analyses, we identified mapping issues faced in seven out of 15 sites. We also identified nuances in data collection and variable definition for the various sites. Temporal trend analyses may support the use of laboratory test result missingness patterns in identifying severe COVID-19 patients. Lastly, using missingness patterns, we determined relationships between various labs that reflect clinical behaviors., Conclusion: In this work, we use computational approaches to relate missingness patterns to hospital treatment capacity and highlight the heterogeneity of looking at COVID-19 over time and at multiple sites, where there might be different phases, policies, etc. Changes in missingness could suggest a change in a patient's condition, and patterns of missingness among laboratory measurements could potentially identify clinical outcomes. This allows sites to consider missing data as informative to analyses and help researchers identify which sites are better poised to study particular questions., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

De novo protein design generally consists of two steps, including structure and sequence design. Many protein design studies have focused on sequence design with scaffolds adapted from native structures in the PDB, which renders novel areas of protein structure and function space unexplored. We developed FoldDesign to create novel protein folds from specific secondary structure (SS) assignments through sequence-independent replica-exchange Monte Carlo (REMC) simulations. The method was tested on 354 non-redundant topologies, where FoldDesign consistently created stable structural folds, while recapitulating on average 87.7% of the SS elements. Meanwhile, the FoldDesign scaffolds had well-formed structures with buried residues and solvent-exposed areas closely matching their native counterparts. Despite the high fidelity to the input SS restraints and local structural characteristics of native proteins, a large portion of the designed scaffolds possessed global folds completely different from natural proteins in the PDB, highlighting the ability of FoldDesign to explore novel areas of protein fold space. Detailed data analyses revealed that the major contributions to the successful structure design lay in the optimal energy force field, which contains a balanced set of SS packing terms, and REMC simulations, which were coupled with multiple auxiliary movements to efficiently search the conformational space. Additionally, the ability to recognize and assemble uncommon super-SS geometries, rather than the unique arrangement of common SS motifs, was the key to generating novel folds. These results demonstrate a strong potential to explore both structural and functional spaces through computational design simulations that natural proteins have not reached through evolution.

Clear cell renal cell carcinomas (ccRCCs) represent ∼75% of RCC cases and account for most RCC-associated deaths. Inter- and intratumoral heterogeneity (ITH) results in varying prognosis and treatment outcomes. To obtain the most comprehensive profile of ccRCC, we perform integrative histopathologic, proteogenomic, and metabolomic analyses on 305 ccRCC tumor segments and 166 paired adjacent normal tissues from 213 cases. Combining histologic and molecular profiles reveals ITH in 90% of ccRCCs, with 50% demonstrating immune signature heterogeneity. High tumor grade, along with BAP1 mutation, genome instability, increased hypermethylation, and a specific protein glycosylation signature define a high-risk disease subset, where UCHL1 expression displays prognostic value. Single-nuclei RNA sequencing of the adverse sarcomatoid and rhabdoid phenotypes uncover gene signatures and potential insights into tumor evolution. In vitro cell line studies confirm the potential of inhibiting identified phosphoproteome targets. This study molecularly stratifies aggressive histopathologic subtypes that may inform more effective treatment strategies., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

COVID-19 often manifests with different outcomes in different patients, highlighting the complexity of the host-pathogen interactions involved in manifestations of the disease at the molecular and cellular levels. In this paper, we propose a set of postulates and a framework for systematically understanding complex molecular host-pathogen interaction networks. Specifically, we first propose four host-pathogen interaction (HPI) postulates as the basis for understanding molecular and cellular host-pathogen interactions and their relations to disease outcomes. These four postulates cover the evolutionary dispositions involved in HPIs, the dynamic nature of HPI outcomes, roles that HPI components may occupy leading to such outcomes, and HPI checkpoints that are critical for specific disease outcomes. Based on these postulates, an HPI Postulate and Ontology (HPIPO) framework is proposed to apply interoperable ontologies to systematically model and represent various granular details and knowledge within the scope of the HPI postulates, in a way that will support AI-ready data standardization, sharing, integration, and analysis. As a demonstration, the HPI postulates and the HPIPO framework were applied to study COVID-19 with the Coronavirus Infectious Disease Ontology (CIDO), leading to a novel approach to rational design of drug/vaccine cocktails aimed at interrupting processes occurring at critical host-coronavirus interaction checkpoints. Furthermore, the host-coronavirus protein-protein interactions (PPIs) relevant to COVID-19 were predicted and evaluated based on prior knowledge of curated PPIs and domain-domain interactions, and how such studies can be further explored with the HPI postulates and the HPIPO framework is discussed., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Yu, Li, Huffman, Beverley, Hur, Merrell, Huang, Wang, Liu, Ong, Cheng, Zeng, Zhang, Li, Liu, Wang, Zhang, Ye, Handelman, Sexton, Eaton, Higgins, Omenn, Athey, Smith, Chen and He.)

Importance: The COVID-19 pandemic has been associated with an increase in mental health diagnoses among adolescents, though the extent of the increase, particularly for severe cases requiring hospitalization, has not been well characterized. Large-scale federated informatics approaches provide the ability to efficiently and securely query health care data sets to assess and monitor hospitalization patterns for mental health conditions among adolescents., Objective: To estimate changes in the proportion of hospitalizations associated with mental health conditions among adolescents following onset of the COVID-19 pandemic., Design, Setting, and Participants: This retrospective, multisite cohort study of adolescents 11 to 17 years of age who were hospitalized with at least 1 mental health condition diagnosis between February 1, 2019, and April 30, 2021, used patient-level data from electronic health records of 8 children's hospitals in the US and France., Main Outcomes and Measures: Change in the monthly proportion of mental health condition-associated hospitalizations between the prepandemic (February 1, 2019, to March 31, 2020) and pandemic (April 1, 2020, to April 30, 2021) periods using interrupted time series analysis., Results: There were 9696 adolescents hospitalized with a mental health condition during the prepandemic period (5966 [61.5%] female) and 11 101 during the pandemic period (7603 [68.5%] female). The mean (SD) age in the prepandemic cohort was 14.6 (1.9) years and in the pandemic cohort, 14.7 (1.8) years. The most prevalent diagnoses during the pandemic were anxiety (6066 [57.4%]), depression (5065 [48.0%]), and suicidality or self-injury (4673 [44.2%]). There was an increase in the proportions of monthly hospitalizations during the pandemic for anxiety (0.55%; 95% CI, 0.26%-0.84%), depression (0.50%; 95% CI, 0.19%-0.79%), and suicidality or self-injury (0.38%; 95% CI, 0.08%-0.68%). There was an estimated 0.60% increase (95% CI, 0.31%-0.89%) overall in the monthly proportion of mental health-associated hospitalizations following onset of the pandemic compared with the prepandemic period., Conclusions and Relevance: In this cohort study, onset of the COVID-19 pandemic was associated with increased hospitalizations with mental health diagnoses among adolescents. These findings support the need for greater resources within children's hospitals to care for adolescents with mental health conditions during the pandemic and beyond.

Background: While acute kidney injury (AKI) is a common complication in COVID-19, data on post-AKI kidney function recovery and the clinical factors associated with poor kidney function recovery is lacking., Methods: A retrospective multi-centre observational cohort study comprising 12,891 hospitalized patients aged 18 years or older with a diagnosis of SARS-CoV-2 infection confirmed by polymerase chain reaction from 1 January 2020 to 10 September 2020, and with at least one serum creatinine value 1-365 days prior to admission. Mortality and serum creatinine values were obtained up to 10 September 2021., Findings: Advanced age (HR 2.77, 95%CI 2.53-3.04, p < 0.0001), severe COVID-19 (HR 2.91, 95%CI 2.03-4.17, p < 0.0001), severe AKI (KDIGO stage 3: HR 4.22, 95%CI 3.55-5.00, p < 0.0001), and ischemic heart disease (HR 1.26, 95%CI 1.14-1.39, p < 0.0001) were associated with worse mortality outcomes. AKI severity (KDIGO stage 3: HR 0.41, 95%CI 0.37-0.46, p < 0.0001) was associated with worse kidney function recovery, whereas remdesivir use (HR 1.34, 95%CI 1.17-1.54, p < 0.0001) was associated with better kidney function recovery. In a subset of patients without chronic kidney disease, advanced age (HR 1.38, 95%CI 1.20-1.58, p < 0.0001), male sex (HR 1.67, 95%CI 1.45-1.93, p < 0.0001), severe AKI (KDIGO stage 3: HR 11.68, 95%CI 9.80-13.91, p < 0.0001), and hypertension (HR 1.22, 95%CI 1.10-1.36, p = 0.0002) were associated with post-AKI kidney function impairment. Furthermore, patients with COVID-19-associated AKI had significant and persistent elevations of baseline serum creatinine 125% or more at 180 days (RR 1.49, 95%CI 1.32-1.67) and 365 days (RR 1.54, 95%CI 1.21-1.96) compared to COVID-19 patients with no AKI., Interpretation: COVID-19-associated AKI was associated with higher mortality, and severe COVID-19-associated AKI was associated with worse long-term post-AKI kidney function recovery., Funding: Authors are supported by various funders, with full details stated in the acknowledgement section., Competing Interests: Dr Hanauer reported having developed an electronic resource of clinical synonyms, EMERSE, that is licensed by the University of Michigan and receiving a portion of the licensing fees for this resource outside the submitted work. Dr Omenn reported being an early investor and serving on the board of Angion Biomedica Corporation, New York, which has conducted clinical trials of drug candidates for overcoming acute kidney injury following cardiopulmonary surgery or kidney transplantation. The former was terminated early based on unsatisfactory efficacy/adverse effects assessment; the latter had insufficient benefit to warrant proposing a Phase III trial. The company is moving in other directions, to be determined. No further work on kidney is anticipated. Dr Holmes disclosed participation as an NIH/NIDDK T2 Coach R01DK113189. Dr Malovini disclosed being a shareholder of Biomeris s.r.l. Dr Bellazzi reported receiving honoraria from Pfizer, and disclosed being a shareholder of University of Pavia spin-off Biomeris. Dr Klann reports consulting fees from i2b2 tranSMART foundation, for work to enhance open-source data warehouse platform. He reports no direct relationship to this work, except that the data model for analysis in this manuscript was inspired by this platform., (© 2022 Published by Elsevier Ltd.)

Background: The current COVID-19 pandemic and the previous SARS/MERS outbreaks of 2003 and 2012 have resulted in a series of major global public health crises. We argue that in the interest of developing effective and safe vaccines and drugs and to better understand coronaviruses and associated disease mechenisms it is necessary to integrate the large and exponentially growing body of heterogeneous coronavirus data. Ontologies play an important role in standard-based knowledge and data representation, integration, sharing, and analysis. Accordingly, we initiated the development of the community-based Coronavirus Infectious Disease Ontology (CIDO) in early 2020., Results: As an Open Biomedical Ontology (OBO) library ontology, CIDO is open source and interoperable with other existing OBO ontologies. CIDO is aligned with the Basic Formal Ontology and Viral Infectious Disease Ontology. CIDO has imported terms from over 30 OBO ontologies. For example, CIDO imports all SARS-CoV-2 protein terms from the Protein Ontology, COVID-19-related phenotype terms from the Human Phenotype Ontology, and over 100 COVID-19 terms for vaccines (both authorized and in clinical trial) from the Vaccine Ontology. CIDO systematically represents variants of SARS-CoV-2 viruses and over 300 amino acid substitutions therein, along with over 300 diagnostic kits and methods. CIDO also describes hundreds of host-coronavirus protein-protein interactions (PPIs) and the drugs that target proteins in these PPIs. CIDO has been used to model COVID-19 related phenomena in areas such as epidemiology. The scope of CIDO was evaluated by visual analysis supported by a summarization network method. CIDO has been used in various applications such as term standardization, inference, natural language processing (NLP) and clinical data integration. We have applied the amino acid variant knowledge present in CIDO to analyze differences between SARS-CoV-2 Delta and Omicron variants. CIDO's integrative host-coronavirus PPIs and drug-target knowledge has also been used to support drug repurposing for COVID-19 treatment., Conclusion: CIDO represents entities and relations in the domain of coronavirus diseases with a special focus on COVID-19. It supports shared knowledge representation, data and metadata standardization and integration, and has been used in a range of applications., (© 2022. The Author(s).)

Objective: For multi-center heterogeneous Real-World Data (RWD) with time-to-event outcomes and high-dimensional features, we propose the SurvMaximin algorithm to estimate Cox model feature coefficients for a target population by borrowing summary information from a set of health care centers without sharing patient-level information., Materials and Methods: For each of the centers from which we want to borrow information to improve the prediction performance for the target population, a penalized Cox model is fitted to estimate feature coefficients for the center. Using estimated feature coefficients and the covariance matrix of the target population, we then obtain a SurvMaximin estimated set of feature coefficients for the target population. The target population can be an entire cohort comprised of all centers, corresponding to federated learning, or a single center, corresponding to transfer learning., Results: Simulation studies and a real-world international electronic health records application study, with 15 participating health care centers across three countries (France, Germany, and the U.S.), show that the proposed SurvMaximin algorithm achieves comparable or higher accuracy compared with the estimator using only the information of the target site and other existing methods. The SurvMaximin estimator is robust to variations in sample sizes and estimated feature coefficients between centers, which amounts to significantly improved estimates for target sites with fewer observations., Conclusions: The SurvMaximin method is well suited for both federated and transfer learning in the high-dimensional survival analysis setting. SurvMaximin only requires a one-time summary information exchange from participating centers. Estimated regression vectors can be very heterogeneous. SurvMaximin provides robust Cox feature coefficient estimates without outcome information in the target population and is privacy-preserving., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier Inc.)

Gene Ontology (GO) has been widely used to annotate functions of genes and gene products. Here, we proposed a new method, TripletGO, to deduce GO terms of protein-coding and non-coding genes, through the integration of four complementary pipelines built on transcript expression profile, genetic sequence alignment, protein sequence alignment, and naïve probability. TripletGO was tested on a large set of 5754 genes from 8 species (human, mouse, Arabidopsis, rat, fly, budding yeast, fission yeast, and nematoda) and 2433 proteins with available expression data from the third Critical Assessment of Protein Function Annotation challenge (CAFA3). Experimental results show that TripletGO achieves function annotation accuracy significantly beyond the current state-of-the-art approaches. Detailed analyses show that the major advantage of TripletGO lies in the coupling of a new triplet network-based profiling method with the feature space mapping technique, which can accurately recognize function patterns from transcript expression profiles. Meanwhile, the combination of multiple complementary models, especially those from transcript expression and protein-level alignments, improves the coverage and accuracy of the final GO annotation results. The standalone package and an online server of TripletGO are freely available at https://zhanggroup.org/TripletGO/., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)