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At least 19 recordsLinked to original sources
PubMed2006

Combination analysis of hypermethylated Wnt-antagonist family genes as a novel epigenetic biomarker panel for bladder cancer detection.

PURPOSE: Aberrant promoter hypermethylation of Wnt-antagonist genes contributes to the pathogenesis of several cancers. We hypothesized that combined methylation analysis of Wnt-antagonist genes could improve their use as a panel of biomarkers for diagnosing and staging of bladder cancers. EXPERIMENTAL DESIGN: Samples (54 total) of bladder tumor and corresponding normal bladder mucosa were analyzed for the methylation and expression levels of six Wnt-antagonist genes (sFRP-1, sFRP-2, sFRP-4, and sFRP-5, Wif-1, and Dkk-3). To increase the sensitivity/specificity of bladder tumor detection, the methylation score (M score), a new method for multigene methylation analysis, was developed. The M score of each sample was calculated as the sum of the corresponding log hazard ratio coefficients derived from multivariate logistic regression analysis of the methylation status for each Wnt-antagonist gene. Receiver operator characteristic (ROC) curve analysis was used to determine the optimal sensitivity/specificity of the M score. Urine DNA from 24 matched patients with bladder tumor and 20 cancer-free volunteers was also used to investigate the methylation status of Wnt-antagonist genes. RESULTS: The methylation levels of Wnt-antagonists were significantly higher and mRNA levels were significantly lower in bladder tumor than in bladder mucosa. Each methylation level was inversely correlated with the corresponding mRNA level. In multivariate regression analysis, the methylation levels of sFRP-2 and Dkk-3 were significant independent predictors of bladder tumor (P < 0.05 and P < 0.01, respectively), whereas with sFRP-1, sFRP-5, and Wif-1 there was a trend towards significance as independent predictors. The M score of Wnt-antagonist genes was significantly higher in bladder tumor than in bladder mucosa (P < 0.05). Overall, the M score had a sensitivity of 77.2% and a specificity of 66.7% as a diagnostic biomarker (areas under the curve, 0.763). The M score could distinguish superficial from invasive bladder tumors with a sensitivity of 72.2% and a specificity of 61.1% as a staging biomarker (areas under the curve, 0.671). In patients with bladder tumor, 80.6% of the methylation-specific PCR results had identical methylation in samples of tumor- and urine-derived DNA. Most urine DNA in normal controls showed no aberrant methylation of the Wnt-antagonist genes. CONCLUSIONS: Hypermethylation of Wnt-antagonist genes plays an important role in the pathogenesis of bladder tumor and can be detected using cellular DNA extracted from urine samples. This is the first report demonstrating that M score analysis of Wnt-antagonist genes could serve as an excellent epigenetic biomarker panel for bladder tumors.

Adaptor Proteins, Signal Transducing↗
PubMed2006

Measurement of a plasma stroke biomarker panel and cardiac troponin T in marathon runners before and after the 2005 Boston marathon.

We report changes in cardiac troponin-T (TnT) and a new plasma stroke biomarker panel (D-dimer, B-natriuretic peptide [BNP], matrix metalloproteinase-9 [MMP-9], S-100 b, Biosite Diagnostics, San Diego, CA) in 30 nonprofessional marathon runners before and immediately after the 2005 Boston Marathon. Following competition, there was a statistically significant increase in MMP-9 (P < .001) and D dimer (P < .001). Nonsignificant changes in S-100 b and BNP were observed. Premarathon and postmarathon values for a multimarker stroke index increased from 0.97 (normal) to 3.5 (low risk or more; P < .001). Two subjects had index values more than the high-risk cutoff value. Mean TnT premarathon and postmarathon levels increased (from <0.01 to 0.03 ng/mL; P < .0001). After the marathon, with a cutoff value of 0.05 ng/mL, 7 runners (23%) had values above the manufacturer's recommended cutoff for myocardial damage. Although biochemical evidence of myocardial damage following strenuous exercise may reflect myocardial stunning or subclinical ischemia, the changes in the stroke index and values for individual stroke markers may reflect a systemic inflammatory response to exertional rhabdomyolysis which is common, but the possibility of subclinical central nervous system damage cannot be excluded.

Biomarkers↗
PubMed2026

Validation and refinement of a biomarker panel for frailty assessment and prediction of muscle weakness in older adults.

Frailty is a complex geriatric syndrome characterized by age-related declines in physiological function and cognitive reserve. To promote early prevention and intervention, minimally invasive and objective biomarkers that can detect frailty progression are required. We aimed to identify biomarkers associated with frailty progression and to elucidate their relevance to the Japanese version of the Cardiovascular Health Study (J-CHS) criteria, consist of five components (unintentional weight loss, self-reported exhaustion, muscle weakness, slow walking speed, and low physical activity). A total of 168 individuals (61 robust, 25 pre-frail, and 82 frail) enrolled in the NCGG (National Center for Geriatrics and Gerontology) Biobank were analyzed. Clinical information, blood-test data, aging-related factors, and gene-expression data were integrated for the analysis. First, linear regression identified one clinical factor, five aging-related factors, and 251 gene-expression factors associated with frailty. Subsequent logistic regression analyses examining each J-CHS components highlighted six candidate biomarkers. Cross-validation further suggested that three of these biomarkers-SMI, apelin, and GDF15-may represent potential biomarkers. Finally, retrospective and prospective analyses further demonstrated that those biomarkers were predictive of future muscle weakness, yielding a concordance index of 0.70. In conclusion, we validated and refined a biomarker panel consisting of SMI, apelin, and GDF15 that is associated with frailty, particularly muscle weakness (a major J-CHS component). These biomarkers may be useful for frailty assessment. Longitudinal analyses further suggested that they may be associated with the future development of muscle weakness in initially robust older adults, although validation in larger prospective cohorts is warranted.

Journal Article↗
PubMed2026

Development and validation of a plasma miRNA-CEA biomarker panel for early detection of lung cancer.

Lung cancer remains a leading cause of cancer-related mortality worldwide, underscoring the critical need for early detection to improve patient outcomes. This study aimed to develop and validate a plasma microRNA biomarker panel for the early detection of non-small cell lung cancer in a Japanese cohort. We enrolled 525 participants, comprising 261 LC cases and 264 non-LC controls, divided into optimization and validation cohorts. A 12-miRNA panel was optimized and further combined with CEA to enhance diagnostic performance. The miRNA-alone model demonstrated robust performance in both the optimization (AUC = 77.0%) and validation cohorts (AUC = 77.9%). Integration with CEA significantly improved accuracy, achieving AUCs of 86.2% in optimization and 84.9% in validation, with particularly high performance in late-stage cancers (AUC = 94.4%) and squamous cell carcinoma (AUC = 90.7%). Sensitivity and specificity thresholds were evaluated, enabling model customization for diverse clinical scenarios. These findings highlight the potential of the miRNA-CEA panel as a minimally invasive tool for early LC detection, especially in non-smoking populations.

Humans↗
PubMed2026

Integrated salivary proteomic and metabolomic analyses reveal molecular characterization and novel biomarker panels of chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD) is a respiratory disorder characterized by chronic inflammation, oxidative stress, and metabolic dysregulation. The lack of convenient and easily-accessible non-invasive diagnostic approaches remains a major clinical challenge. This study applied an integrated saliva-based proteomic and untargeted metabolomic strategy to identify potential biomarkers for COPD classification. Comprehensive multi-omics analyses identified 225 differentially abundant proteins and 60 differentially abundant metabolites between patients with COPD and healthy controls, including 24 biologically relevant endogenous metabolites. Functional enrichment analyses revealed pronounced dysregulation of mitochondrial energy metabolism, redox homeostasis, lipid remodeling, and inflammatory-related pathways in COPD. By integrating salivary proteomic and metabolomic biomarkers, a stepwise feature selection combined with LASSO logistic regression was used to construct diagnostic models, yielding an optimized biomarker panel consisting of 11 proteins and 2 endogenous metabolites. This integrated model achieved excellent diagnostic performance, with an area under the ROC curve of 0.96. Collectively, these findings demonstrate that integrated salivary proteomic and metabolomic profiling provides a robust, non-invasive approach for COPD classification and offers a promising foundation for the development of biosensor-based diagnostic platforms and early disease detection. SIGNIFICANCE: Chronic obstructive pulmonary disease (COPD) remains a major global health burden. Current diagnostic approaches rely largely on spirometry and clinical assessment, which are limited in sensitivity for early-stage disease and unsuitable for large-scale screening. This study employs an integrated saliva-based proteomic and metabolomic strategy to identify non-invasive biomarkers for COPD classification. Our findings reveal coordinated dysregulation of mitochondrial energy metabolism, redox homeostasis, and lipid remodeling in COPD, highlighting the interconnected roles of metabolic reprogramming, oxidative stress, and inflammation in disease pathophysiology. Notably, a robust diagnostic panel comprising 11 proteins and 2 endogenous metabolites was established, achieving excellent classification performance (AUC of 0.96). To our knowledge, the integrated application of salivary proteomics and metabolomics for COPD diagnosis remains largely unexplored, underscoring the significance and translational potential of our findings.

Humans↗
PubMed2024

Comprehensive Proteomic Analysis Reveals Distinct Features and a Diagnostic Biomarker Panel for Early Pregnancy Loss in Histological Subtypes.

Early pregnancy loss (EPL) is a common event in human reproduction and is classified into histological subtypes such as hydropic abortion (HA) and hydatidiform moles, including complete hydatidiform moles (CHMs) and partial hydatidiform moles (PHMs). However, accurate diagnosis and improved patient management remain challenging due to high rates of misdiagnosis and diverse prognostic risks. Therefore, diagnostic biomarkers for EPL are urgently needed. Our study aimed to identify biomarkers for EPL through comprehensive proteomic analysis. Ten CHMs, six PHMs, ten HAs, and 10 normal control products of conception were used to obtain a proteomic portrait. Parallel reaction monitoring-targeted proteomic and regression analyses were used to verify and select the diagnostic signatures. Finally, 14 proteins were selected and a panel of diagnostic classifiers (DLK1, SPTB/COL21A1, and SAR1A) was built to represent the CHM, PHM, and normal control groups (area under the receiver operating characteristic curve = 0.900, 0.804/0.885, and 0.991, respectively). This high diagnostic power was further validated in another independent cohort (n&#xa0;=&#xa0;148) by immunohistochemistry (n&#xa0;=&#xa0;120) and Western blot analyses (n&#xa0;=&#xa0;28). The protein SPTB was selected for further biological behavior experiments in&#xa0;vitro. Our data suggest that SPTB maintains trophoblast cell proliferation, angiogenesis, cell motility, and the cytoskeleton network. This study provides a comprehensive proteomic portrait and identifies potential diagnostic biomarkers. These findings enhance our understanding of EPL pathogenesis and offer novel targets for diagnosis and therapeutic interventions.

Humans↗
PubMed2006

Multiple biomarker panels for early detection of ovarian cancer.

Ovarian cancer is the eighth most common cause of cancer mortality in women. It is diagnosed in more than 20,000 women in the USA each year and approximately 15,000 women die of the disease annually. The majority of patients are diagnosed with advanced-stage ovarian cancer, as this deadly disease causes minimal and nonspecific symptoms until late in the course of the disease. No standardized screening test exists to reliably detect ovarian cancer. Cancer antigen (CA)-125 is a protein antigen found at abnormally high levels in the blood of many women with ovarian cancer. Most healthy women have CA-125 levels of below 35 units/microl of blood serum. However, a number of noncancerous conditions can cause elevated CA 125 levels, and many women with early-stage ovarian cancer have normal CA-125 levels. Owing to these limitations, this test is not recommended for routine screening in women who are not at high risk or who do not have specific symptoms of the disease. Currently, many researchers are focusing on simultaneous examination of multiple markers to increase sensitivity of the screening test for early detection of ovarian cancer. Analysis of the current literature shows that combining several biomarkers dramatically improves sensitivity of CA-125 in ovarian cancer patients. This article provides a comprehensive overview of existing studies in the area of multimarker panel development for the early detection and monitoring of ovarian cancer. Our literature review demonstrates that a multimarker approach for the generation of a prototype assay for early detection of ovarian cancer has a great potential to lead to the development of a screening test for this disease.

Biomarkers, Tumor↗
PubMed2006

Novel prognostic immunohistochemical biomarker panel for estrogen receptor-positive breast cancer.

PURPOSE: Patients with breast cancer experience progression and respond to treatment in diverse ways, but prognostic and predictive tools for the oncologist are limited. We have used gene expression data to guide the production of hundreds of novel antibody reagents to discover novel diagnostic tools for stratifying carcinoma patients. PATIENTS AND METHODS: One hundred forty novel and 23 commercial antisera, selected on their ability to differentially stain tumor samples, were used to stain paraffin blocks from a retrospective breast cancer cohort. Cox proportional hazards and regression tree analysis identified minimal panels of reagents able to predict risk of recurrence. We tested the prognostic association of these prospectively defined algorithms in two independent cohorts. RESULTS: In both validation cohorts, the Kaplan-Meier estimates of recurrence confirmed that both the Cox model using five reagents (p53, NDRG1, CEACAM5, SLC7A5, and HTF9C) and the regression tree model using six reagents (p53, PR, Ki67, NAT1, SLC7A5, and HTF9C) distinguished estrogen receptor (ER)-positive patients with poor outcomes. The Cox model was superior and distinguished patients with poor outcomes from patients with good or moderate outcomes with a hazard ratio of 2.21 (P = .0008) in validation cohort 1 and 1.88 (P = .004) in cohort 2. In multivariable analysis, the calculated risk of recurrence was independent of stage, grade, and lymph node status. A model proposed for ER-negative patients failed validation in the independent cohorts. CONCLUSION: A panel of five antibodies can significantly improve on traditional prognosticators in predicting outcome for ER-positive breast cancer patients.

Algorithms↗
PubMed2025

Proteomic-based biomarker discovery reveals panels of diagnostic biomarkers for early identification of heart failure subtypes.

BACKGROUND: Limited access to echocardiography can delay the diagnosis of suspected heart failure (HF), which in turn postpones the initiation of optimal guideline-directed medical therapy. Although natriuretic peptides like B-type natriuretic peptide (BNP) are valuable biomarkers for diagnosing and managing HF, the utility of combining BNP with other blood-based biomarkers to predict subtypes of new-onset HF remains underexplored. OBJECTIVES: This study sought to investigate and evaluate the diagnostic significance of adding blood-based biomarkers to BNP for identifying heart failure with preserved ejection fraction (HFpEF) or reduced ejection fraction (HFrEF), with the goal of enhancing diagnostic assays beyond BNP measurements. METHODS: We identified candidate blood protein biomarkers using untargeted proteomics workflows from a cohort of individuals recruited to the STOP-HF trial who were at risk of HF and subsequently developed either HFpEF or HFrEF over time ("HF progressors"; n&#x2009;=&#x2009;40). Candidate biomarkers were verified in an independent cohort (n&#x2009;=&#x2009;52) from a community-based rapid access HF diagnostic clinic. The biological processes associated with these proteins were assessed, and the diagnostic values of biomarker panels were evaluated using a machine learning approach. RESULTS: Within HF progressors, we identified 3 proteins associated with HFpEF development: vascular cell adhesion protein 1 (VCAM1), insulin-like growth factor 2 (IGF2), and inter-alpha-trypsin inhibitor heavy chain 3 (ITIH3). Additionally, 4 proteins were linked to HFrEF development: C-reactive protein (CRP), interleukin-6 receptor subunit beta (IL6RB), phosphatidylinositol-glycan-specific phospholipase D (PHLD), and noelin (NOE1). These findings were verified in an independent cohort to distinguish HF subtypes from controls. Moreover, a random forest algorithm demonstrated that combining these candidate biomarkers with BNP measurement significantly improved the prediction of HF subtypes. CONCLUSIONS: We identified candidate proteins linked to HFpEF and HFrEF in a longitudinal HF progressor cohort and validated them in a community-based cohort. Adding these proteins to BNP led to a significant improvement in HF subtype prediction. Study results have clinical implications for blood-based screening of HF subtypes using panels of biomarkers, particularly in resource-limited settings.

Humans↗
PubMed1993

A panel of biomarkers of carcinogenesis of the upper aerodigestive tract as potential intermediate endpoints in chemoprevention trials.

BACKGROUND: Patients with squamous cell carcinoma of the head and neck have a 10-30% risk of developing a second primary tumor. The concept of "field cancerization" assumes that the whole upper aerodigestive tract is affected and prone to malignant transformation. This study was undertaken to investigate the value of a panel of monoclonal antibodies to identify biomarkers in oral mucosa associated with cancer risk. Such biomarkers may be suitable candidates to serve as intermediate endpoints in cancer chemoprevention trials. METHODS: As a model, the expression of antigens was assessed in cytologic preparations obtained from macroscopically normal oral mucosa of patients with tongue carcinoma and of controls. The panel consisted of antibodies against cytokeratin 8, 10, 13, and 19 and the monoclonal antibodies designated K931, K984, E48, Ki-67, and UM-A9. RESULTS: Oral mucosa of cancer patients had a more than threefold increased expression of cytokeratin 19 as compared with controls (36.0 versus 11.3%; P < 0.01). CONCLUSION: Cytokeratin 19 is a potential intermediate endpoint in head and neck cancer chemopreventive trials.

Adult↗
PubMed2005

Evaluation and management of the acutely dyspneic patient: the role of biomarkers.

The etiology of dyspnea can often be difficult to rapidly and accurately determine and can delay timely and appropriate therapies. The current literature reveals important diagnostic, prognostic, and therapeutic implications of several currently used biomarkers: sensitive d -dimer, myoglobin, creatine kinase-MB, cardiac troponins, and b-type natriuretic peptide. These biomarkers were found to have a high sensitivity and negative predictive value for rapidly ruling out potential serious etiologies of dyspnea, namely, pulmonary embolism (PE), acute myocardial infarction (AMI), and congestive heart failure (CHF). In the setting of a low to moderate pretest probability of PE, a negative sensitive d -dimer can rule out a PE with 97% accuracy. After 10 hours from the onset of symptoms, normal levels of myoglobin, creatine kinase-MB, and cardiac troponin I can rule out an AMI with greater than 96% accuracy. A b-type natriuretic peptide level less than 80 pg/mL can confidently rule out decompensated CHF with greater than 99% accuracy. However, no literature was found analyzing the use of these biomarkers in combination. A dyspnea biomarker panel could rapidly and accurately assist a clinician to rule out PE, AMI, and CHF. If a PE, AMI, or CHF is determined to be the cause of dyspnea, a biomarker panel could help risk stratify and help determine initial therapies. Subsequent clinical research is needed to corroborate this postulation.

Antifibrinolytic Agents↗
PubMed2026

Bridging genotype, phenotype, and clinical insight: the role of multi-omics in cardiovascular disease.

INTRODUCTION: It is increasingly evident that the multifactorial nature of cardiovascular disease requires the combination of different omics approaches for improving our mechanistic understanding, identifying novel drug targets, and developing accurate diagnostic, predictive, and prognostic biomarker panels. AREAS COVERED: We review the current state and the potential of multi-omics in cardiovascular disease, with a specific focus on plasma-, spatial-, and single-cell approaches. We discuss lipidomics as a genotype&#x2011;to&#x2011;phenotype bridge, the utility of remote longitudinal monitoring via microsampling/dried blood spots, and emerging clinical&#x2011;trial integrations of multi-omics approaches. We outline critical gaps in standardization and how to overcome these, pre&#x2011;analytical challenges and constraints that are often neglected, and data&#x2011;integration methods spanning from canonical correlation analysis to modern machine learning approaches. EXPERT OPINION: Multi&#x2011;omics can shape cardiovascular care by identifying drug targets in diseased tissue and by yielding small, usable biomarker panels.

Humans↗
PubMed2003

Identification of biomarkers for ovarian cancer using strong anion-exchange ProteinChips: potential use in diagnosis and prognosis.

One hundred eighty-four serum samples from patients with ovarian cancer (n = 109), patients with benign tumors (n = 19), and healthy donors (n = 56) were analyzed on strong anion-exchange surfaces using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry technology. Univariate and multivariate statistical analyses applied to protein-profiling data obtained from 140 training serum samples identified three biomarker protein panels. The first panel of five candidate protein biomarkers, termed the screening biomarker panel, effectively diagnosed benign and malignant ovarian neoplasia [95.7% sensitivity, 82.6% specificity, 89.2% accuracy, and receiver operating characteristic (ROC) area under the curve of 0.94]. The other two panels, consisting of five and four candidate protein biomarkers each, effectively distinguished between benign and malignant ovarian neoplasia and were therefore referred to as validation biomarker panel I (81.5% sensitivity, 94.9% specificity, 88.2% accuracy, and ROC = 0.94) and validation biomarker panel II (72.8% sensitivity, 94.9% specificity, 83.9% accuracy, and ROC = 0.90). The three ovarian cancer biomarker protein panels correctly diagnosed 41 of the 44 blinded test samples: 21 of 22 malignant ovarian neoplasias [10 of 11 early-stage ovarian cancer (I/II) and 11 of 11 advanced-stage ovarian cancer (III/IV)], 6 of 6 low malignant potential, 5 of the 6 benign tumors, and 9 of 10 normal patient samples. In conclusion, we have discovered three ovarian cancer biomarker protein panels that, when used together, effectively distinguished serum samples from healthy controls and patients with either benign or malignant ovarian neoplasia.

Biomarkers, Tumor↗
PubMed2005

Proteomic profiling of cerebrospinal fluid identifies biomarkers for amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is characterized by degeneration of motor neurons. We tested the hypothesis that proteomic analysis will identify protein biomarkers that provide insight into disease pathogenesis and are diagnostically useful. To identify ALS specific biomarkers, we compared the proteomic profile of cerebrospinal fluid (CSF) from ALS and control subjects using surface-enhanced laser desorption/ionization-time of flight mass spectrometry (SELDI-TOF-MS). We identified 30 mass ion peaks with statistically significant (p < 0.01) differences between control and ALS subjects. Initial analysis with a rule-learning algorithm yielded biomarker panels with diagnostic predictive value as subsequently assessed using an independent set of coded test subjects. Three biomarkers were identified that are either decreased (transthyretin, cystatin C) or increased (carboxy-terminal fragment of neuroendocrine protein 7B2) in ALS CSF. We validated the SELDI-TOF-MS results for transthyretin and cystatin C by immunoblot and immunohistochemistry using commercially available antibodies. These findings identify a panel of CSF protein biomarkers for ALS.

Adult↗
PubMed2006

Potential biomarkers found by protein profiling may provide insight for the macrovascular pathogenesis of diabetes mellitus.

Diabetes mellitus (DM) is an alarming threat to health of mankind, yet its pathogenesis is unclear. The purpose of this study was to find potential biomarkers to serve as indicators for the pathogenesis of DM in a time course manner. Based on our previous findings that oxidative stress occurred at week 8, aorta lysate and sera of 102 streptozotocin (STZ)-induced diabetic and 85 control male Sprague-Dawley rats were obtained at the 4th, 8th and 12th week after STZ injection. The protein profiles were studied employing surface-enhanced laser desorption/ionization time-of-flight mass spectrometry technology in attomole sensitivity range. In the aorta, a multiple biomarker panel was discovered at the 4th week. At the 8th week, 4 biomarkers were found, while at the 12th week, 3 biomarkers were identified. In the sera, a triplet of 3 peaks and 2 biomarkers were all discovered to have 100% classification accuracy rate to differentiate the DM and control groups at all time intervals. Besides, 2 biomarkers were also found to have high classification value at week 12. Comparing the aorta and sera from DM and non-DM rats, a bundle of potential biomarkers with significant changes in peak intensities and high classification values were found. Two of the serum biomarkers matched with islet amyloid polypeptide and resistin in the SWISS-PROT knowledgebase. Validation has been conducted using immunoassay kits. These potential biomarkers may provide valuable insight on the pathogenesis of DM and macrovascular complications.

Animals↗
PubMed2006

Link test--A statistical method for finding prostate cancer biomarkers.

We present a new method, link-test, to select prostate cancer biomarkers from SELDI mass spectrometry and microarray data sets. Biomarkers selected by link-test are supported by data sets from both mRNA and protein levels, and therefore results in improved robustness. Link-test determines the level of significance of the association between a microarray marker and a specific mass spectrum marker by constructing background mass spectra distributions estimated by all human protein sequences in the SWISS-PROT database. The data set consist of both microarray and mass spectrometry data from prostate cancer patients and healthy controls. A list of statistically justified prostate cancer biomarkers is reported by link-test. Cross-validation results show high prediction accuracy using the identified biomarker panel. We also employ a text-mining approach with OMIM database to validate the cancer biomarkers. The study with link-test represents one of the first cross-platform studies of cancer biomarkers.

Algorithms↗