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Moradi E, Prakash M, Hall A, Solomon A, Strange B, Tohka J. Machine learning prediction of future amyloid beta positivity in amyloid-negative individuals. Alzheimers Res Ther 2024; 16:46. [PMID: 38414035 PMCID: PMC10900722 DOI: 10.1186/s13195-024-01415-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 02/11/2024] [Indexed: 02/29/2024]
Abstract
BACKGROUND The pathophysiology of Alzheimer's disease (AD) involves β -amyloid (A β ) accumulation. Early identification of individuals with abnormal β -amyloid levels is crucial, but A β quantification with positron emission tomography (PET) and cerebrospinal fluid (CSF) is invasive and expensive. METHODS We propose a machine learning framework using standard non-invasive (MRI, demographics, APOE, neuropsychology) measures to predict future A β -positivity in A β -negative individuals. We separately study A β -positivity defined by PET and CSF. RESULTS Cross-validated AUC for 4-year A β conversion prediction was 0.78 for the CSF-based and 0.68 for the PET-based A β definitions. Although not trained for the clinical status-change prediction, the CSF-based model excelled in predicting future mild cognitive impairment (MCI)/dementia conversion in cognitively normal/MCI individuals (AUCs, respectively, 0.76 and 0.89 with a separate dataset). CONCLUSION Standard measures have potential in detecting future A β -positivity and assessing conversion risk, even in cognitively normal individuals. The CSF-based definition led to better predictions than the PET-based definition.
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Affiliation(s)
- Elaheh Moradi
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, 70150, Finland.
| | - Mithilesh Prakash
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, 70150, Finland
| | - Anette Hall
- Institute of Clinical Medicine/Neurology, University of Eastern Finland, Kuopio, Finland
- Division of Clinical Geriatrics, Center for Alzheimer Research, Karolinska Institute, Stockholm, Sweden
| | - Alina Solomon
- Institute of Clinical Medicine/Neurology, University of Eastern Finland, Kuopio, Finland
- Division of Clinical Geriatrics, Center for Alzheimer Research, Karolinska Institute, Stockholm, Sweden
- Ageing Epidemiology Research Unit, School of Public Health, Imperial College London, London, UK
| | - Bryan Strange
- Laboratory for Clinical Neuroscience, Center for Biomedical Technology, Universidad Politécnica de Madrid, IdISSC, Madrid, Spain
- Reina Sofia Centre for Alzheimer's Research, Madrid, Spain
| | - Jussi Tohka
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, 70150, Finland
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HDEC: A Heterogeneous Dynamic Ensemble Classifier for Binary Datasets. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2020; 2020:8826914. [PMID: 33488690 PMCID: PMC7803144 DOI: 10.1155/2020/8826914] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 11/22/2020] [Accepted: 11/27/2020] [Indexed: 11/18/2022]
Abstract
In recent years, ensemble classification methods have been widely investigated in both industry and literature in the field of machine learning and artificial intelligence. The main advantage of this approach is to benefit from a set of classifiers instead of using a single classifier with the aim of improving the prediction performance, such as accuracy. Selecting the base classifiers and the method for combining them are the most challenging issues in the ensemble classifiers. In this paper, we propose a heterogeneous dynamic ensemble classifier (HDEC) which uses multiple classification algorithms. The main advantage of using heterogeneous algorithms is increasing the diversity among the base classifiers as it is a key point for an ensemble system to be successful. In this method, we first train many classifiers with the original data. Then, they are separated based on their strength in recognizing either positive or negative instances. For doing this, we consider the true positive rate and true negative rate, respectively. In the next step, the classifiers are categorized into two groups according to their efficiency in the mentioned measures. Finally, the outputs of the two groups are compared with each other to generate the final prediction. For evaluating the proposed approach, it has been applied to 12 datasets from the UCI and LIBSVM repositories and calculated two popular prediction performance metrics, including accuracy and geometric mean. The experimental results show the superiority of the proposed approach in comparison to other state-of-the-art methods.
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Li J, Wang J, Li J. Improving Allergen Prediction in Main Crops Using a Weighted Integrative Method. Interdiscip Sci 2017; 9:545-549. [PMID: 27734271 DOI: 10.1007/s12539-016-0192-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2016] [Revised: 09/29/2016] [Accepted: 10/03/2016] [Indexed: 10/20/2022]
Abstract
As a public health problem, food allergy is frequently caused by food allergy proteins, which trigger a type-I hypersensitivity reaction in the immune system of atopic individuals. The food allergens in our daily lives are mainly from crops including rice, wheat, soybean and maize. However, allergens in these main crops are far from fully uncovered. Although some bioinformatics tools or methods predicting the potential allergenicity of proteins have been proposed, each method has their limitation. In this paper, we built a novel algorithm PREALW, which integrated PREAL, FAO/WHO criteria and motif-based method by a weighted average score, to benefit the advantages of different methods. Our results illustrated PREALW has better performance significantly in the crops' allergen prediction. This integrative allergen prediction algorithm could be useful for critical food safety matters. The PREALW could be accessed at http://lilab.life.sjtu.edu.cn:8080/prealw .
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Affiliation(s)
- Jing Li
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, People's Republic of China
| | - Jing Wang
- Center for Quantitative Sciences, Vanderbilt University School of Medicine, Nashville, TN, 37203, USA
| | - Jing Li
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, People's Republic of China.
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Diffusion Tensor Imaging Adds Diagnostic Accuracy in Magnetic Resonance Neurography. Invest Radiol 2015; 50:498-504. [DOI: 10.1097/rli.0000000000000156] [Citation(s) in RCA: 58] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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Rath MG, Uhlmann L, Heil J, Domschke C, Roth Z, Sinn HP, Marme F, Scharf A, Schneeweiss A, Kieser M, Sohn C, Rom J. Predictors of Residual Tumor in Breast-Conserving Therapy. Ann Surg Oncol 2015. [DOI: 10.1245/s10434-015-4736-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Coolen-Maturi T, Elkhafifi FF, Coolen FP. Three-group ROC analysis: A nonparametric predictive approach. Comput Stat Data Anal 2014. [DOI: 10.1016/j.csda.2014.04.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Luo X, Stock C, Burwinkel B, Brenner H. Identification and evaluation of plasma microRNAs for early detection of colorectal cancer. PLoS One 2013; 8:e62880. [PMID: 23690963 PMCID: PMC3653912 DOI: 10.1371/journal.pone.0062880] [Citation(s) in RCA: 130] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2012] [Accepted: 03/26/2013] [Indexed: 12/16/2022] Open
Abstract
Background Colorectal cancer (CRC) is one of the most commonly diagnosed cancers. Circulating microRNAs (miRNAs) have been suggested as potentially promising markers for early detection of CRC. We aimed to identify and evaluate a panel of miRNAs that might be suitable for CRC early detection. Methods MiRNAs were profiled by TaqMan MicroRNA Array and screened for differential expression in 5 pools of plasma samples of CRC patients (N = 50) and 5 pools of neoplasm-free controls (N = 50). Additional miRNAs were selected from a literature review. Identified candidates were evaluated in independent validation samples with respect to discrimination of CRC patients (N = 80) or advanced adenoma patients (N = 50) and neoplasm-free controls (N = 194). Diagnostic performance of the panel of miRNAs was assessed by multiple logistic regression, using bootstrap analysis to correct for over-optimism. Results Five miRNAs identified to be differentially expressed from TaqMan MicroRNA Array (miR-29a, -106b, -133a, -342-3p, -532-3p), and seven miRNAs reported to be differentially expressed in the literature (miR-18a, -20a, -21, -92a, -143, -145, -181b) were selected for validation. Nine of the twelve miRNAs (miR-18a, -20a, -21, -29a, -92a, -106b, -133a, -143, -145) were found to be differentially expressed in CRC patients and controls in the validation samples. The optimism-corrected area under the curve was 0.745 (95% confidence interval: 0.708–0.846). None of the selected miRNAs showed significant differential expression between advanced adenoma patients and neoplasm-free controls. Conclusion The identified panel of miRNAs could be of potential use in the development of a multi-marker blood based test for early detection of CRC. Impact: The study underscores the high potential of plasma miRNAs for the improvement of current offers of non-invasive CRC screening.
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Affiliation(s)
- Xiaoya Luo
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Gastroenterology, Beijing Friendship Hospital Affiliated with the Capital Medical University, Beijing, China
| | - Christian Stock
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Barbara Burwinkel
- Divison of Molecular Biology of Breast Cancer, Department of Gynecology and Obstetrics, University Heidelberg, Heidelberg, Germany
- Molecular Epidemiology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- * E-mail:
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Chang YCI. Maximizing an ROC-type measure via linear combination of markers when the gold reference is continuous. Stat Med 2012; 32:1893-903. [PMID: 22972679 DOI: 10.1002/sim.5616] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2012] [Revised: 08/19/2012] [Accepted: 08/28/2012] [Indexed: 11/08/2022]
Abstract
Effectively combining many classification instruments or diagnostic measurements together to improve the classification accuracy of individuals is a common idea in disease diagnosis or classification. These ensemble-type diagnostic methods can be constructed with respect to different kinds of performance criterions. Among them, the receiver operating characteristic (ROC) curve is the most popular criterion, which, together with some indexes derived from it, is commonly used to evaluate and summarize the performance of a classification instrument, such as a biomarker or a classifier. However, the usefulness of ROC curve and its related indexes relies on the existence of a binary label for each individual subject. In many disease diagnosis situations, such a binary variable may not exist, but only the continuous measurement of the true disease status is available. This true disease status is often referred to as the 'gold standard'. The modified area under ROC curve (AUC)-type measure defined by Obuchowski is a method proposed to accommodate such a situation. However, there is still no method for finding the optimal combination of diagnostic measurements, with respect to such an index, to have better diagnostic power than that of each individual measurement. In this paper, we propose an algorithm for finding the optimal combination with respect to such an extended AUC-type measure such that the combined measurement can have more diagnostic power. We illustrate the performance of our algorithm by using some synthesized data and a diabetes data set.
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Woloszynski T, Podsiadlo P, Stachowiak GW, Kurzynski M, Lohmander LS, Englund M. Prediction of progression of radiographic knee osteoarthritis using tibial trabecular bone texture. ACTA ACUST UNITED AC 2012; 64:688-95. [PMID: 21989629 DOI: 10.1002/art.33410] [Citation(s) in RCA: 47] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
OBJECTIVE To develop a system for predicting the progression of radiographic knee osteoarthritis (OA) using tibial trabecular bone texture. METHODS We studied 203 knees with (n = 68) or without (n = 135) radiographic tibiofemoral OA in 105 subjects (90 men and 15 women with a mean age of 54 years) in whom 2 sets of knee radiographs were obtained 4 years apart. We determined medial and lateral compartment tibial trabecular bone texture using an automated region selection method. Three texture parameters were calculated: roughness, degree of anisotropy, and direction of anisotropy based on a signature dissimilarity measure method. We evaluated tibiofemoral OA progression using a radiographic semiquantitative outcome: an increase in the medial joint space narrowing (JSN) grade. We examined the predictive ability of trabecular bone texture in knees with and those without preexisting radiographic OA, with adjustment for age, sex, and body mass index, using logistic regression (generalized estimating equations) and receiver operating characteristic curves. RESULTS The prediction of increased medial JSN in knees with or without preexisting radiographic OA was the most accurate for medial trabecular bone texture; the area under the curve (AUC) was 0.77 and 0.75, respectively. For lateral trabecular bone texture, the AUC was 0.71 in knees with preexisting OA and 0.72 in knees without preexisting OA. CONCLUSION We have developed a system, based on analyzing tibial trabecular bone texture, which yields good prediction of loss of tibiofemoral joint space. The predictive ability of the system needs to be further validated.
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Affiliation(s)
- T Woloszynski
- School of Mechanical and Chemical Engineering, University of Western Australia, Crawley, Perth, Western Australia, Australia.
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Mittlböck M, Edler L, LeBlanc M, Niland J, Zwinderman K. Second Issue for Computational Statistics for Clinical Research. Comput Stat Data Anal 2012. [DOI: 10.1016/j.csda.2012.01.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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Meier-Meitinger M, Rauh C, Adamietz B, Fasching P, Schwab S, Haeberle L, Hein A, Bayer C, Bani M, Lux M, Hartmann A, Wachter D, Uder M, Schulz-Wendtland R, Beckmann M, Heusinger K. Accuracy of radiological tumour size assessment and the risk for re-excision in a cohort of primary breast cancer patients. Eur J Surg Oncol 2012; 38:44-51. [DOI: 10.1016/j.ejso.2011.10.008] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2011] [Revised: 09/19/2011] [Accepted: 10/10/2011] [Indexed: 10/15/2022] Open
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Fasching PA, Heusinger K, Haeberle L, Niklos M, Hein A, Bayer CM, Rauh C, Schulz-Wendtland R, Bani MR, Schrauder M, Kahmann L, Lux MP, Strehl JD, Hartmann A, Dimmler A, Beckmann MW, Wachter DL. Ki67, chemotherapy response, and prognosis in breast cancer patients receiving neoadjuvant treatment. BMC Cancer 2011; 11:486. [PMID: 22081974 PMCID: PMC3262864 DOI: 10.1186/1471-2407-11-486] [Citation(s) in RCA: 230] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2011] [Accepted: 11/14/2011] [Indexed: 02/07/2023] Open
Abstract
Background The pathological complete response (pCR) after neoadjuvant chemotherapy is a surrogate marker for a favorable prognosis in breast cancer patients. Factors capable of predicting a pCR, such as the proliferation marker Ki67, may therefore help improve our understanding of the drug response and its effect on the prognosis. This study investigated the predictive and prognostic value of Ki67 in patients with invasive breast cancer receiving neoadjuvant treatment for breast cancer. Methods Ki67 was stained routinely from core biopsies in 552 patients directly after the fixation and embedding process. HER2/neu, estrogen and progesterone receptors, and grading were also assessed before treatment. These data were used to construct univariate and multivariate models for predicting pCR and prognosis. The tumors were also classified by molecular phenotype to identify subgroups in which predicting pCR and prognosis with Ki67 might be feasible. Results Using a cut-off value of > 13% positively stained cancer cells, Ki67 was found to be an independent predictor for pCR (OR 3.5; 95% CI, 1.4, 10.1) and for overall survival (HR 8.1; 95% CI, 3.3 to 20.4) and distant disease-free survival (HR 3.2; 95% CI, 1.8 to 5.9). The mean Ki67 value was 50.6 ± 23.4% in patients with pCR. Patients without a pCR had an average of 26.7 ± 22.9% positively stained cancer cells. Conclusions Ki67 has predictive and prognostic value and is a feasible marker for clinical practice. It independently improved the prediction of treatment response and prognosis in a group of breast cancer patients receiving neoadjuvant treatment. As mean Ki67 values in patients with a pCR were very high, cut-off values in a high range above which the prognosis may be better than in patients with lower Ki67 values may be hypothesized. Larger studies will be needed in order to investigate these findings further.
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Affiliation(s)
- Peter A Fasching
- Department of Medicine, Division of Hematology and Oncology, David Geffen School of Medicine, University of California at Los Angeles, California, USA.
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Gamaleldin R, Iskander I, Seoud I, Aboraya H, Aravkin A, Sampson PD, Wennberg RP. Risk factors for neurotoxicity in newborns with severe neonatal hyperbilirubinemia. Pediatrics 2011; 128:e925-31. [PMID: 21911352 PMCID: PMC3182847 DOI: 10.1542/peds.2011-0206] [Citation(s) in RCA: 94] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
OBJECTIVE To evaluate the importance of total serum bilirubin (TSB) and neurotoxicity risk factors in predicting acute bilirubin encephalopathy (ABE) at admission or posttreatment bilirubin encephalopathy (BE) in infants with severe hyperbilirubinemia. METHODS We analyzed the interaction of TSB and risk factors as determinants of ABE and BE in 249 newborns admitted with a TSB level of ≥ 25 mg/dL (427 μmol/L) to Cairo University Children's Hospital during a 12-month period. RESULTS Admission TSB values ranged from 25 to 76.4 mg/dL. Forty-four newborns had moderate or severe ABE at admission; 35 of 249 infants (14%) had evidence of BE at the time of discharge or death. Rh incompatibility (odds ratio [OR]: 48.6) and sepsis (OR: 20.6) greatly increased the risk for ABE/BE, but TSB levels correlated poorly with the presence or absence of ABE or BE in these patients. The OR for ABO incompatibility with anemia (1.8) was not statistically significant. Low admission weight (OR: 0.83 per 100 g) increased the risk for BE, especially when other risk factors were present. The threshold TSB level that identified 90% of infants with ABE/BE was 25.4 mg/dL when neurotoxicity risk factors were present. In contrast, neurotoxicity was first observed at a TSB level of >31.5 mg/dL in 111 infants without risk factors. CONCLUSIONS Newborns without risk factors for neurotoxicity have a higher tolerance for hyperbilirubinemia than recognized in management guidelines. The risk for BE in hemolytic disease varies with etiology. The great variation in response to TSB indicates that biological factors other than TSB values are important in the pathogenesis of BE.
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Affiliation(s)
| | - Iman Iskander
- Department of Pediatrics, Cairo University, Cairo, Egypt; and
| | - Iman Seoud
- Department of Pediatrics, Cairo University, Cairo, Egypt; and
| | - Hanan Aboraya
- Department of Pediatrics, Cairo University, Cairo, Egypt; and
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