A biomarker can be either predictive or prognostic. A predictive marker predicts benefit from a specific treatment; it helps to select a particular treatment over another. A prognostic marker predicts the natural history of disease (survival), independent of treatment. It can indicate a need for further treatment, but does not help to determine which treatment. The ROC Plotter is the first online transcriptome-level validation tool for predictive biomarkers in cancer research.
The Kaplan Meier plotter is capable to assess the correlation between the expression of 30k genes (mRNA, miRNA, protein) and survival in 25k+ samples from 21 tumor types including breast, ovarian, lung, & gastric cancer. Sources for the databases include GEO, EGA, and TCGA. Primary purpose of the tool is a meta-analysis based discovery and validation of survival biomarkers.
Genes showing higher expression in either tumor or metastatic tissues can help in better understanding tumor formation and can serve as biomarkers of progression or as potential therapy targets. Our goal was to establish an integrated database using available transcriptome-level datasets and to create a web platform which enables the mining of this database by comparing normal, tumor and metastatic data across all genes in real time. Our tool can termed TNMplot.com can be used perform the analysis of the database in real time.
The muTarget platform is designed to connect mutation status to gene expression changes in solid tumours. It has two major functions: 1) With a “Genotype” run one can identify gene(s) showing altered expression in samples harbouring a mutated input gene. This option is useful in case one searches new drug targets in a cohort of patients with a given mutation. 2) With a “Target” run one can identify mutations resulting in expression change in the input gene. This option is useful in case one has a drug target gene, and patient cohorts with enriched expression is the question.
Scientists from nearly all disciplines face the problem of simultaneously evaluating many hypotheses. Conducting multiple comparisons increases the likelihood that a non-negligible proportion of associations will be false positives, clouding real discoveries. Drawing valid conclusions require taking into account the number of performed statistical tests and adjusting the statistical confidence measures. To facilitate multiple-testing corrections, we developed a fully automated solution not requiring programming skills or the use of a command line. Our registration free online tool is available at http://www.multipletesting.com and compiles the five most frequently used adjustment tools, including the Bonferroni, the Holm, the Hochberg corrections, allows to calculate False Discovery Rates (FDR) and q-values.
Scientometrics.org is a scientific project aiming to objectively compare the scientific output of Hungarian researchers and disciplines to each other. The analysis is performed by comparing each researcher to a common reference database containing Hungarian researchers of the same age and active in the same scientific discipline.

Quantum chemical programs

István Mayer’s personal professional website which contains free available quantum chemical programs, advanced teaching textbooks and list of publications.

http://occam.ttk.mta.hu

MassKinetics

Theory and Windows-based program to calculate mass spectra. For research and for teaching mass spectrometry and reaction kinetics.

http://proteomics.ttk.mta.hu/masskinetics/

GlycoMiner

Computer software to determine the N-glycosylation of proteins from glycopeptide LC-MS/MS measurements.

glycominer

X-ray Photoelectron Spectroscopy

XPS MultiQuant is a quantitative evaluation program for X-ray Photoelectron Spectroscopy, serving as a practical and universal tool for the surface spectroscopist. It applies the “classic” methods of quantitative calculations using the integrated intensity of the measured XPS lines.

XMQhome

Computer code for method and model comparison (ranking and grouping, as well).

VBA program’s homepage

Sum of (abolute) ranking differences (SRD) and its validation:

Compare Ranks with Random Numbers (CRRN ) without ties.

Authors: Dr. Klára Kollár-Hunek and Dr. Károly Héberger

email: kollarne[at]mail.bme.hu and heberger.karoly[at]ttk.mta.hu

Downloadable program, samples for input and output files:

CRRN_DNA_V8_restN.xls program file

Basic input file sample: Basic_DataSample.xls

Output of basic input file: Basic_DataSample_DNA_V6_CRRN.xls

Special input file sample: DataSample_Freq2.xls

Output of special input file: DataSample_Freq2_DNA_V6_CRRN.xls

For MATLAB code to perform sum of ranking differences (SRD)visit:

http://www.isu.edu/chem/people/faculty/kalijohn/

(see at the end of the page: 2013_12_16_SRD.zip)

“SRD with ties: SRDrep_V5_E10.xlsm program file

1.input sample (xls file with several sheets) ; Output of the 1.input file

2.input sample (xlsx file with one sheet) ; Output of the 2.input file

Warning: The SRD-with-ties program is for Excel-2010, and needs Solver among the VBA Tools (References)

Coming soon: SRD-with-ties program which doesn’t need the Solver.

If you have problems with using Solver in Excel, then wait for the new program version,

where we build in a new approximation of the SRD-with-ties probability distribution.

Here you see, how you can check after opening the downloaded SRDrep_V5_E10.xlsm program

whether you have the Solver among the VBA Tools (References):

For MATLAB code to perform sum of ranking differences (SRD)visit:

http://www.isu.edu/chem/people/faculty/kalijohn/

(see at the end of the page: 2013_12_16_SRD.zip)”

Generalization of the Pair-correlation Method (GPCM) – a VBA code for variable (feature) selection,

PCM works in MS Excel 97-2003 version: PCM.xla

Input file: PCM_McReynolds.xls

Output file (example for Williams t-test): PCM_McReynoldsResults.xls

Program usage, options and explanations can be found in the references (below).

The program is downloadable freely, provided proper references are cited as shown below:

PCM distinction between two variables X1 and X2 (using one dependent variable (supervisor) Y) :

Róbert Rajkó, and Károly Héberger, Conditional Fisher’s exact test as a selection criterion for pair-correlation method. Type I and Type II errors

Chemometrics and Intelligent Laboratory Systems, 57 (2001) 1-14.

Generalization of PCM (GPCM) for more independent (X) variables:

Károly Héberger and Róbert Rajkó: Generalization of Pair-Correlation Method (PCM) for Nonparametric Variable Selection

Journal of Chemometrics, 16 (2002) 436-443.

GPCM application:

Károly Héberger and Róbert Rajkó: Variable Selection using Pair-Correlation Method. Environmental Applications.

SAR and QSAR in Environmental Research, 13 (2002) 541-554.

Application of GPCM for idenfication of product strengths and weaknesses in sensory and consumer sciences:

Attila Gere, László Sipos, Károly Héberger, Generalized Pairwise Correlation and method comparison: Impact assessment for JAR attributes on overall liking. Food Quality and Preference, 43 (2015) 88-96.

PCM works in MS Excel 97-2003 version: PCM.xla

PCM2 works in MS Excel 2010 version: PCM2.xlam

Input file: PCM_McReynolds.xls

Output file (example for Williams t-test):PCM_McReynoldsResults.xls