Classification algorithms for microcalcifications in mammograms (Review)

Sakka, E.; Prentza, Andriana; Koutsouris, Dimitrios

doi:10.3892/or.15.4.1049

Cited by 24 publications

(17 citation statements)

References 12 publications

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“…We performed FFT analysis on time course microarrays in the asexual intraerythrocytic developmental cycle (IDC) of P. falciparum [ 23 ]. These expression profiles were obtained at intervals of 1 h for 48 h except on the 23rd and 29th h. The missing data were filled using the k nearest neighbor (KNN) method [ 24 ]. Before FFT analysis was performed, the expression value of each gene was centered by subtracting the mean value.…”

Section: Methodsmentioning

confidence: 99%

Computational prediction of protein interactions related to the invasion of erythrocytes by malarial parasites

Liu

Huang

et al. 2014

BMC Bioinformatics

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BackgroundThe invasion of red blood cells (RBCs) by malarial parasites is an essential step in the life cycle of Plasmodium falciparum. Human-parasite surface protein interactions play a critical role in this process. Although several interactions between human and parasite proteins have been discovered, the mechanism related to invasion remains poorly understood because numerous human-parasite protein interactions have not yet been identified. High-throughput screening experiments are not feasible for malarial parasites due to difficulty in expressing the parasite proteins. Here, we performed computational prediction of the PPIs involved in malaria parasite invasion to elucidate the mechanism by which invasion occurs.ResultsIn this study, an expectation maximization algorithm was used to estimate the probabilities of domain-domain interactions (DDIs). Estimates of DDI probabilities were then used to infer PPI probabilities. We found that our prediction performance was better than that based on the information of D. melanogaster alone when information related to the six species was used. Prediction performance was assessed using protein interaction data from S. cerevisiae, indicating that the predicted results were reliable. We then used the estimates of DDI probabilities to infer interactions between 490 parasite and 3,787 human membrane proteins. A small-scale dataset was used to illustrate the usability of our method in predicting interactions between human and parasite proteins. The positive predictive value (PPV) was lower than that observed in S. cerevisiae. We integrated gene expression data to improve prediction accuracy and to reduce false positives. We identified 80 membrane proteins highly expressed in the schizont stage by fast Fourier transform method. Approximately 221 erythrocyte membrane proteins were identified using published mass spectral datasets. A network consisting of 205 interactions was predicted. Results of network analysis suggest that SNARE proteins of parasites and APP of humans may function in the invasion of RBCs by parasites.ConclusionsWe predicted a small-scale PPI network that may be involved in parasite invasion of RBCs by integrating DDI information and expression profiles. Experimental studies should be conducted to validate the predicted interactions. The predicted PPIs help elucidate the mechanism of parasite invasion and provide directions for future experimental investigations.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-014-0393-z) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

Computational prediction of protein interactions related to the invasion of erythrocytes by malarial parasites

Liu

Huang

et al. 2014

BMC Bioinformatics

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“…As the GUI is also dynamically designed, this creates a great potential for the system to interface with any other AI/DSS module, exchange data and dynamically present the results to the users. In that sense, the presented system could be considered as a universal tool to be used for presenting and interfacing with other clinical decision support or computer aided diagnosis systems, like the ones presented in [9][10][11].…”

Section: Discussionmentioning

confidence: 99%

CxCaDSS: A Web-Based Clinical Decision Support System for Cervical Cancer

et al. 2015

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Abstract-Data from countries with well-organized screening programs and cancer registries indicate that the vast majority of participants who developed cervical cancer could be explained as underestimation of cases that had at least one abnormal Pap test. Nowadays, there are ancillary molecular biology techniques available that provide important information related to cervical cancer and the HPV natural history, including DNA micro-arrays identifying HPV subtypes, mRNA techniques such as nucleic acid based amplification or flow cytometry identifying E6/E7 oncogenes, and immunocytochemistry techniques such as overexpression of p16. However, each one of these techniques has its own performance, advantages and limitations, thus a combinatorial approach via artificial intelligence methods could exploit the benefits of each method and produce more accurate results. In this paper we present a novel web-based clinical decision support system and its integration with underlying artificial neural networks, for the combination of the results of classic and ancillary techniques in order to increase the accuracy of diagnosis and thus identify women at true risk of developing cervical cancer. The presented system follows the MVC approach enabling it to easily adapt to any underlying data and structure to support clinical decisions for other domains as well.

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“…Sakka et al review and evaluate some of the classification algorithms on microcalcifications in mammograms used in various CAD systems to improve breast cancer prognosis (13). The algorithms are separated into categories according to the method in use.…”

Section: Design and Assessment Of Classification Toolsmentioning

confidence: 99%

Overview: Computational analysis and decision support systems in oncology

Makedon

Karkaletsis

2006

Oncol Rep

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Computational analysis tools and decision support systems have increased their penetration in the support of clinical processes and management of medical data and knowledge. Applications range from adjunct tools for diagnosis and disease investigation to the treatment and monitoring of therapeutic procedures. As all medical fields, the field of oncology is affected. This special issue includes studies presenting research and applications of computational intelligence in oncology, covering four main areas: i) decision support systems (DSS) and artificial intelligence (AI) applications in oncology; ii) design and assessment of classification tools in oncology; iii) intelligent accessing, retrieving, and storing of medical images; and iv) intelligent telemedicine and telehealth applications in oncology.

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Classification algorithms for microcalcifications in mammograms (Review)

Cited by 24 publications

References 12 publications

Computational prediction of protein interactions related to the invasion of erythrocytes by malarial parasites

Computational prediction of protein interactions related to the invasion of erythrocytes by malarial parasites

CxCaDSS: A Web-Based Clinical Decision Support System for Cervical Cancer

Overview: Computational analysis and decision support systems in oncology

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