Module: miRNA sequencing data
This suplementary software allows you to use our neural network to predict diagnosis of invasive epithelial ovarian cancer (vs. controls or borderline) depending on the levels of miRNAs.
In order to use this software please set up the values in the panel on your left hand side. The calculations will be performed automatically as the values change.
Using next generation sequencing technology to profile all serum miRNA transcripts in 179 patient samples, we identified 192 miRNA species that could be reliably detected in human sera. We used these data to evaluate 11 independent machine learning algorithms and to select the modeling approach that could best discriminate among women with and without invasive epithelial ovarian cancer. A neural network machine learning approach presented here based on a subset of 14 miRNAs had the best performance characteristics with an area under the curve (AUC) of 0.90 (95% CI: 0.81-0.99). This model outperformed CA-125, and unlike other modeling approaches, was insensitive to clinical heterogeneity or batch effects.
This network requires the values of miRNA log-transformed expression levels in transcripts per million. Please note that log-transformed data should be provided as input (input = log10(tpm), where tpm is transcripts per million). Please refer to the full text of article for the interpretation of results.
The probability of cancer was estimated as:
Advanced details and features:
This tab allows you to evaluate several instances at once. Please upload CSV file and await the table with results. More details about CSV file format can be found in the other tab.
Comma-separated values (CSV) file stores tabular data (numbers and text) in plain text. Each line of the file is a data record. Each record consists of one or more fields, separated by commas. The use of the comma as a field separator is the source of the name for this file format. The first row should contain column "Id" and the miRNA names complient with the names on your left hand side. Column "Id" is important and should contain identifiers of patients, so you can recoginze and assing results to them. Please remember that this format may be dependent on regional settings. You can use Microsft Excel, but we strongly encourage you to check in notepad whether comma is used as column delimiter and not as the decimal mark. Please see the example in the Keller-like data module.
This software is a part of paper entitled 'Diagnostic potential for a serum miRNA neural network for detection of ovarian cancer' and authored by Kevin M. Elias, Wojciech Fendler, Konrad Stawiski, Stephen J. Fiascone, Allison F. Vitonis, Ross S. Berkowitz, Gyorgy Frendl, Panagiotis A. Konstantinopoulos, Christopher P. Crum, Magdalena Kedzierska, Daniel W. Cramer, and Dipanjan Chowdhury.
Corresponding author: Dipanjan Chowdhury, PhD, Department of Radiation Oncology, Dana-Farber Cancer Institute, email@example.com.
Software author, technical issues: Konrad Stawiski, MD (contact: firstname.lastname@example.org; Department of Biostatistics and Translational Medicine, Medical University of Lodz, Poland).