Probabilistic disease classification of expression-dependent proteomic data from mass spectrometry of human serum.

We have developed an algorithm called Q5 for probabilistic classification of healthy versus disease whole serum samples using mass spectrometry. The algorithm employs principal components analysis (PCA) followed by linear discriminant analysis (LDA) on whole spectrum surface-enhanced laser desorption/ionization time of flight (SELDI-TOF) mass spectrometry (MS) data and is demonstrated on four real datasets from complete, complex SELDI spectra of human blood serum. Q5 is a closed-form, exact solution to the problem of classification of complete mass spectra of a complex protein mixture. Q5 employs a probabilistic classification algorithm built upon a dimension-reduced linear discriminant analysis. Our solution is computationally efficient; it is noniterative and computes the optimal linear discriminant using closed-form equations. The optimal discriminant is computed and verified for datasets of complete, complex SELDI spectra of human blood serum. Replicate experiments of different training/testing splits of each dataset are employed to verify robustness of the algorithm. The probabilistic classification method achieves excellent performance. We achieve sensitivity, specificity, and positive predictive values above 97% on three ovarian cancer datasets and one prostate cancer dataset. The Q5 method outperforms previous full-spectrum complex sample spectral classification techniques and can provide clues as to the molecular identities of differentially expressed proteins and peptides.     

Segregation of ovarian cancer stage exploiting spectral biomarkers derived from blood plasma or serum analysis: ATR‐FTIR spectroscopy coupled with variable selection methods

Ovarian cancer is a solid tumor and a leading cause of mortality. Diagnostic tools for the detection of early stage (stage I) ovarian cancer are urgently needed. For this purpose, attenuated total reflection Fourier‐transform infrared spectroscopy (ATR‐FTIR) coupled with variable selection methods, successive projection algorithm or genetic algorithm (GA) combined with linear discriminant analysis (LDA), were employed to identify spectral biomarkers in blood plasma or serum samples for accurate diagnosis of different stages of ovarian cancer, histological type and segregation based on age. Three spectral datasets (stage I vs. stage II–IV; serous vs. non‐serous carcinoma; and, ≤60 years vs. >60 years) were processed: sensitivity and specificity required for real‐world diagnosis of ovarian cancer was achieved. Toward segregating stage I vs. stage II–IV, sensitivity and specificity (plasma blood) of 100% was achieved using a GA‐LDA model with 33 wavenumbers. For serous vs. non‐serous category (plasma blood), the sensitivity and specificity levels, using 29 wavenumbers by GA‐LDA, were remarkable (up to 94%). For ≤60 years and >60 years categories (plasma blood), the sensitivity and specificity, using 42 wavenumbers by GA‐LDA, gave complete accuracy (100%). For serum samples, sensitivity and specificity results gave relatively high accuracy (up to 91.6% stage I vs. stage II–IV; up to 93.0% serous vs. non‐serous; and, up to 96.0% ≤60 years vs. >60 years) using several wavenumbers. These findings justify a prospective population‐based assessment of biomarkers signatures using ATR‐FTIR spectroscopy as a screening tool for stage of ovarian cancer. © 2015 American Institute of Chemical Engineers Biotechnol. Prog., 31:832–839, 2015     

Elemental fingerprinting of tumorous and adjacent non-tumorous tissues from patients with colorectal cancer using ICP-MS, ICP-OES and chemometric analysis

Tumorous and adjacent non-tumorous paired biopsies from 38 patients with colorectal cancer were analyzed by inductively coupled plasma mass spectrometry and inductively coupled plasma optical emission spectrometry after low-volume microwave digestion. 18 elements were investigated: Ag, Al, Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Mo, Ni, P, Pb, S, Se and Zn. Different chemometric tools were used for data evaluation: Wilcoxon signed rank test, Hieratical clustering analysis, principal component analysis (PCA) and linear discriminant analysis (LDA). With the exception of Al, tumours were observed to have significantly more elevated concentrations of essential elements as compared to non-tumours. On the contrary, elements considered potentially carcinogenic such as Cr, Ni, Mo or Co do not display significant differences. When PCA was applied, different components were obtained for tumorous and non-tumorous tissues. When LDA was applied for the elements studied (including essential and non-essential elements) about 90% of cases were correctly classified.     

Linear Discrimination,Ordination, and the Visualization of Selection Gradients in Modern Morphometrics

Linear discriminant analysis (LDA) is a multivariate classification technique frequently applied to morphometric data in various biomedical disciplines. Canonical variate analysis (CVA), the generalization of LDA for multiple groups, is often used in the exploratory style of an ordination technique (a low-dimensional representation of the data). In the rare case when all groups have the same covariance matrix, maximum likelihood classification can be based on these linear functions. Both LDA and CVA require full-rank covariance matrices, which is usually not the case in modern morphometrics. When the number of variables is close to the number of individuals, groups appear separated in a CVA plot even if they are samples from the same population. Hence, reliable classification and assessment of group separation require many more organisms than variables. A simple alternative to CVA is the projection of the data onto the principal components of the group averages (between-group PCA). In contrast to CVA, these axes are orthogonal and can be computed even when the data are not of full rank, such as for Procrustes shape coordinates arising in samples of any size, and when covariance matrices are heterogeneous. In evolutionary quantitative genetics, the selection gradient is identical to the coefficient vector of a linear discriminant function between the populations before vs. after selection. When the measured variables are Procrustes shape coordinates, discriminant functions and selection gradients are vectors in shape space and can be visualized as shape deformations. Except for applications in quantitative genetics and in classification, however, discriminant functions typically offer no interpretation as biological factors.     

3-Hydroxy fatty acids in saliva as diagnostic markers in chronic periodontitis

Saturated straight- and branched-chain 3-hydroxy fatty acids (3-OH FAs) of 10-18 carbon chain lengths were determined in saliva from 27 individuals with chronic periodontitis and 18 healthy individuals by using gas chromatography-tandem mass spectrometry. Of the 14 different 3-OH FAs detected, 3-OH-C(i17:0) was the most abundant in the periodontitis samples while 3-OH-C(14:0) was the most abundant in the healthy individuals. Considering the relative percentages of 3-OH-C(12:0), 3-OH-C(14:0), 3-OH-C(i17:0), and 3-OH-C(17:0), 95.6% of all cases were correctly classified as healthy individuals or periodontitis patients by means of discriminant analysis. The sensitivity, specificity, positive predictive value and negative predictive value of 3-OH FA analysis in diagnosing peridontitis were, respectively, 0.92, 1.00, 1.00, and 0.90. The results indicate that 3-OH FA analysis of saliva samples is a useful diagnostic method in chronic periodontitis.     

Multi-method analysis of MRI images in early diagnostics of Alzheimer's disease

The role of structural brain magnetic resonance imaging (MRI) is becoming more and more emphasized in the early diagnostics of Alzheimer's disease (AD). This study aimed to assess the improvement in classification accuracy that can be achieved by combining features from different structural MRI analysis techniques. Automatically estimated MR features used are hippocampal volume, tensor-based morphometry, cortical thickness and a novel technique based on manifold learning. Baseline MRIs acquired from all 834 subjects (231 healthy controls (HC), 238 stable mild cognitive impairment (S-MCI), 167 MCI to AD progressors (P-MCI), 198 AD) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were used for evaluation. We compared the classification accuracy achieved with linear discriminant analysis (LDA) and support vector machines (SVM). The best results achieved with individual features are 90% sensitivity and 84% specificity (HC/AD classification), 64%/66% (S-MCI/P-MCI) and 82%/76% (HC/P-MCI) with the LDA classifier. The combination of all features improved these results to 93% sensitivity and 85% specificity (HC/AD), 67%/69% (S-MCI/P-MCI) and 86%/82% (HC/P-MCI). Compared with previously published results in the ADNI database using individual MR-based features, the presented results show that a comprehensive analysis of MRI images combining multiple features improves classification accuracy and predictive power in detecting early AD. The most stable and reliable classification was achieved when combining all available features.     

Study on gastric cancer blood plasma based on surface-enhanced Raman spectroscopy combined with multivariate analysis

A surface-enhanced Raman spectroscopy (SERS) method combined with multivariate analysis was developed for non-invasive gastric cancer detection. SERS measurements were performed on two groups of blood plasma samples: one group from 32 gastric patients and the other group from 33 healthy volunteers. Tentative assignments of the Raman bands in the measured SERS spectra suggest interesting cancer-specific biomolecular changes, including an increase in the relative amounts of nucleic acid, collagen, phospholipids and phenylalanine and a decrease in the percentage of amino acids and saccharide in the blood plasma of gastric cancer patients as compared with those of healthy subjects. Principal components analysis (PCA) and linear discriminant analysis (LDA) were employed to develop effective diagnostic algorithms for classification of SERS spectra between normal and cancer plasma with high sensitivity (79.5%) and specificity (91%). A receiver operating characteristic (ROC) curve was employed to assess the accuracy of diagnostic algorithms based on PCA-LDA. The results from this exploratory study demonstrate that SERS plasma analysis combined with PCA-LDA has tremendous potential for the non-invasive detection of gastric cancers.     

Ambient mass spectrometry employing a DART ion source for metabolomic fingerprinting/profiling: a powerful tool for beer origin recognition

A metabolomic fingerprinting/profiling generated by ambient mass spectrometry (MS) employing a direct analysis in real time (DART) ion source coupled to high-resolution time-of-flight mass spectrometry (TOFMS) was employed as a tool for beer origin recognition. In a first step, the DART–TOFMS instrumental conditions were optimized to obtain the broadest possible representation of ionizable compounds occurring in beer samples (direct measurement, no sample preparation). In the next step, metabolomic profiles (mass spectra) of a large set of different beer brands (Trappist and non-Trappist specialty beers) were acquired. In the final phase, the experimental data were analyzed using partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and artificial neural networks with multilayer perceptrons (ANN-MLP) with the aim of distinguishing (i) the beers labeled as Rochefort 8; (ii) a group consisting of Rochefort 6, 8, 10 beers; and (iii) Trappist beers. The best prediction ability was obtained for the model that distinguished the group of Rochefort 6, 8, 10 beers from the rest of beers. In this case, all chemometric tools employed provided ≥95% correct classification. The current study showed that DART–TOFMS metabolomic fingerprinting/profiling is a powerful analytical strategy enabling quality monitoring/authenticity assessment to be conducted in real time.     

Evaluation of proteomics-identified CCL18 and CXCL1 as circulating tumor markers for differential diagnosis between ovarian carcinomas and benign pelvic masses

A lack of sensitive and specific tumor markers for early diagnosis and treatment is a major cause for the high mortality rate of ovarian cancer. The purpose of this study was to identify potential proteomics-based biomarkers useful for the differential diagnosis between ovarian cancer and benign pelvic masses. Serum samples from 41 patients with ovarian cancer, 32 patients with benign pelvic masses, and 41 healthy female blood donors were examined, and proteomic profiling of the samples was assessed by surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectroscopy (MS). A confirmatory study was also conducted with serum specimens from 58 patients with ovarian carcinoma, 37 patients with benign pelvic masses, and 48 healthy women. A classification tree was established using Biomarker Pattern Software. Six differentially expressed proteins (APP, CA 125, CCL18, CXCL1, IL-8, and ITIH4) were separated by high-performance liquid chromatography and identified by matrix-assisted laser desorption/ionization (MALDI)-MS/MS and database searches. Two of the proteins overexpressed in ovarian cancer patients, chemokine CC2 motif ligand 18 (CCL18) and chemokine CXC motif ligand 1 (CXCL1), were automatically selected in a multivariate predictive model. These two protein biomarkers were then validated and evaluated by enzyme-linked immunosorbent assay (ELISA) in 535 serum specimens (130 ovarian cancer, 64 benign ovarian masses, 36 lung cancer, 60 gastric cancer, 55 nasopharyngeal carcinoma, 48 hepatocellular carcinoma, and 142 healthy women). The combined use of CCL18 and CXCL1 as biomarkers for ovarian cancer had a sensitivity of 92% and a specificity of 97%. The multivariate ELISA analysis of the two putative markers in combination with CA 125 resulted in a sensitivity of 99% for healthy women and 94% for benign pelvic masses, and a specificity of 92% for both groups; these values were significantly higher than those obtained with CA 125 alone (p and lt;0.05). We conclude that serum CCL18 and CXCL1 are potentially useful as novel circulating tumor markers for the differential diagnosis between ovarian cancer and benign ovarian masses.     

Improvements in the search for potential biomarkers by proteomics: application of principal component and discriminant analyses for two-dimensional maps evaluation

In this study, we evaluated if the application of multivariate analysis on the data obtained from two-dimensional protein maps could mean an improvement in the search for protein markers. First, we performed a classical proteomic study of the differential expression of serum N-glycoproteins in colorectal cancer patients. Then, applying principal component analysis (PCA) we assessed the utility of the 2-D protein pattern and certain subsets of spots as a tool to distinguish control and case samples, and tested the accuracy of the classification model by linear discriminant analysis (LDA). On the other hand we looked for altered spots by univariate statistics and then analysed them as a cluster by PCA and LDA. We found that those proteins combined presented a theoretical sensitivity and specificity of 100%. Finally, the spots with known protein identity were analysed by multivariate methods, finding a subgroup that behaved as the most obvious candidates for further validation trials.     

Multivariate analysis of sorghum volatiles for the fast screening of sugarcane aphid infestation

The sugarcane aphid, Melanaphis sacchari, has been a severe pest throughout the sorghum field in Texas, which can worse the sorghum yield economically. For this purpose of early detection, the mechanism of herbivore-induced plant volatiles (HIPVs) needs to be utilized in the detection method. In this study, the HayeSep Q adsorbent combined gas chromatography mass spectrometry (GC/MS) was tested to analyze the volatile organic compounds (VOCs) that sorghum can emit when they are in good shape as well as they are infested by the sugarcane aphids, and multivariate techniques were performed for the fast screening of the infestation. Several VOCs identified from Student’s t-test with p < 0.05 were finally chosen as variables for multivariate analysis, and both unsupervised learning of principal component analysis (PCA) and clustering analysis (CA) and supervised learning of linear discriminant analysis (LDA) were done, showing good performance on discrimination between healthy and infested sorghum.     

Rapid and Accurate Detection of Urinary Pathogens by Mobile IMS-Based Electronic Nose: A Proof-of-Principle Study

Urinary tract infection (UTI) is a common disease with significant morbidity and economic burden, accounting for a significant part of the workload in clinical microbiology laboratories. Current clinical chemisty point-of-care diagnostics rely on imperfect dipstick analysis which only provides indirect and insensitive evidence of urinary bacterial pathogens. An electronic nose (eNose) is a handheld device mimicking mammalian olfaction that potentially offers affordable and rapid analysis of samples without preparation at athmospheric pressure. In this study we demonstrate the applicability of ion mobility spectrometry (IMS) –based eNose to discriminate the most common UTI pathogens from gaseous headspace of culture plates rapidly and without sample preparation. We gathered a total of 101 culture samples containing four most common UTI bacteries: E. coli, S. saprophyticus, E. faecalis, Klebsiella spp and sterile culture plates. The samples were analyzed using ChemPro 100i device, consisting of IMS cell and six semiconductor sensors. Data analysis was conducted by linear discriminant analysis (LDA) and logistic regression (LR). The results were validated by leave-one-out and 5-fold cross validation analysis. In discrimination of sterile and bacterial samples sensitivity of 95% and specificity of 97% were achieved. The bacterial species were identified with sensitivity of 95% and specificity of 96% using eNose as compared to urine bacterial cultures. In conclusion: These findings strongly demonstrate the ability of our eNose to discriminate bacterial cultures and provides a proof of principle to use this method in urinanalysis of UTI.     

Serum proteome profiling for diagnostics of ovarian cancer using ClinProt magnetic technique and MALDI-TOF mass spectrometry

Using ClinProt magnetic beads with reverse-phase (MB-HIC 8 and HB-HIC 18), weak cation exchange (MB-WCX) and metal affinity (MB-IMAC Cu) surfaces fractions of peptides and proteins were isolated from human sera for their profiling by MALDI-TOF mass spectrometry. Proteome profiling of sera from basically healthy women (47 subjects, average age 49) and from women with verified ovarian cancer (stages 1-IV, 47 patients, average age 51) by means of MB-WCX beads allowed to generate the best diagnostic models based on Genetic Algorithm and Supervised Neural Network classifiers; these models demonstrated 100% sensitivity and specificity during analysis of the test set. Introduction of additional sera from patients with colorectal cancer (19) and ulcerous colitis (5) to the statistical model confirmed 100% ovarian cancer recognition. Statistical analysis of mass-spectrometry peak areas included to the diagnostic classifiers showed 3 peaks characteristic for ovarian cancer and 4 peak areas exhibiting changes associated with both ovarian and colorectal cancer.     

Discriminant analysis and its application in DNA sequence motif recognition

Identification of functional motifs in a DNA sequence is fundamentally a statistical pattern recognition problem. Discriminant analysis is widely used for solving such problems. This paper will review two basic parametric methods: LDA (linear discriminant analysis) and QDA (quadratic discriminant analysis). Their usage in recognition of splice sites and exons in the human genome will be demonstrated.     

Liquid chromatography–mass spectrometry-based metabolomics for authenticity assessment of fruit juices

High performance liquid chromatography?Cmass spectrometry (HPLC?CMS) technique, employing a hybrid triple quadrupole/linear ion trap (QqQ/LIT) mass analyzer, was used for comprehensive metabolomic fingerprinting of several fruit juices types, prepared from expensive (orange) or relatively low-priced (apple, grapefruit) fruits. Following the automated data mining and pre-treatment step, the suitability of the multivariate HPLC?CMS metabolomic data for authentication, i.e., classification of fruit juice and adulteration detection, was assessed with the use of advanced chemometric tools (principal component analysis, PCA, and linear discrimination analysis, LDA). The LDA classification model, constructed and validated employing a highly variable samples set, was able to reliably detect 15% addition of apple or grapefruit juice to orange juice. In the final stage of this study, high performance liquid chromatography?Cquadrupole?Cquadrupole-time-of-flight mass spectrometry (HPLC?CQqTOFMS) measurements were performed in order to obtain data for identification of pre-selected marker compounds using elemental formula calculation and online databases search.     

Tumor antigen NB/70K and CA 125 levels in the blood of preoperative ovarian cancer patients and controls: a preliminary report of the use of the NB12123 and CA 125 radioimmunoassays alone and in combination

The NB12123 and CA125 radioimmunoassays, murine monoclonal antibody assays for measuring circulating levels of human ovarian tumor associated antigens NB/70K and CA 125, respectively, have been previously described. In the present study, preoperative serum samples were obtained from patients undergoing laparotomy for benign neoplastic ovarian tumors (N = 16), cancer of the cervix (N = 22), cancer of the uterus (N = 20), and cancer of the ovary (N = 47). Controls (N = 50) were obtained from healthy blood bank donors. No correlation was observed between the levels of NB/70K and CA 125 in these samples (r2 = .079, linear regression analysis). In general, increasing levels of both antigens were present with increasing tumor burden and higher histological grade. In addition, both markers were most elevated in the serum of ovarian cancer patients with serous and unclassified adenocarcinomas. Using 40 AU and 35 unit cut-offs for the NB/70K and CA 125 assay, respectively, overall specificity for healthy controls and patients with benign diseases approaches 100%. The combined sensitivity of the assays for ovarian cancer patient sera in this study indicates that the assays may be helpful in establishing a pre operative diagnosis of ovarian cancer. Complementarity of the NB/70K and CA 125 assays has been demonstrated, indicating that one or both assays may be used to monitor as many as 85% of ovarian cancer patients.     

Superiority of Classification Tree versus Cluster,Fuzzy and Discriminant Models in a Heartbeat Classification System

This study presents a 2-stage heartbeat classifier of supraventricular (SVB) and ventricular (VB) beats. Stage 1 makes computationally-efficient classification of SVB-beats, using simple correlation threshold criterion for finding close match with a predominant normal (reference) beat template. The non-matched beats are next subjected to measurement of 20 basic features, tracking the beat and reference template morphology and RR-variability for subsequent refined classification in SVB or VB-class by Stage 2. Four linear classifiers are compared: cluster, fuzzy, linear discriminant analysis (LDA) and classification tree (CT), all subjected to iterative training for selection of the optimal feature space among extended 210-sized set, embodying interactive second-order effects between 20 independent features. The optimization process minimizes at equal weight the false positives in SVB-class and false negatives in VB-class. The training with European ST-T, AHA, MIT-BIH Supraventricular Arrhythmia databases found the best performance settings of all classification models: Cluster (30 features), Fuzzy (72 features), LDA (142 coefficients), CT (221 decision nodes) with top-3 best scored features: normalized current RR-interval, higher/lower frequency content ratio, beat-to-template correlation. Unbiased test-validation with MIT-BIH Arrhythmia database rates the classifiers in descending order of their specificity for SVB-class: CT (99.9%), LDA (99.6%), Cluster (99.5%), Fuzzy (99.4%); sensitivity for ventricular ectopic beats as part from VB-class (commonly reported in published beat-classification studies): CT (96.7%), Fuzzy (94.4%), LDA (94.2%), Cluster (92.4%); positive predictivity: CT (99.2%), Cluster (93.6%), LDA (93.0%), Fuzzy (92.4%). CT has superior accuracy by 0.3–6.8% points, with the advantage for easy model complexity configuration by pruning the tree consisted of easy interpretable ‘if-then’ rules.     

Serum metabolomics for the diagnosis and classification of myasthenia gravis

Myasthenia gravis (MG) is a chronic autoimmune neuromuscular disease with few reliable diagnostic measures. Therefore, it is great important to explore novel tools for the diagnosis of MG. In this study, a serum metabolomic approach based on LC?CMS in combination with multivariate statistical analyses was used to identify and classify patients with various grades of MG. Serum samples from 42 MG patients and 16 healthy volunteers were analyzed by liquid chromatography Fourier transform mass spectrometry (LC-FTMS). MG patients were clearly distinguished from healthy subjects based on their global serum metabolic profiles by using orthogonal partial least squares (OPLS) analysis. Moreover, different changes in metabolic profiles were observed between early- and late-stages MG patients. Nine biomarkers, including gamma-aminobutyric acid and sphingosine 1-phosphate were identified. In addition, 92.8% sensitivity, 83.3% specificity and 90% accuracy were obtained from the OPLS discriminant analysis (OPLS-DA) class prediction model in detecting MG. The results presented here illustrate that serum metabolomics exhibits great potential in the detecting and grading of MG, and it is potentially applicable as a new diagnostic approach for MG.     

Optical diagnosis of laryngeal cancer using high wavenumber Raman spectroscopy

We report the implementation of the transnasal image-guided high wavenumber (HW) Raman spectroscopy to differentiate tumor from normal laryngeal tissue at endoscopy. A rapid-acquisition Raman spectroscopy system coupled with a miniaturized fiber-optic Raman probe was utilized to realize real-time HW Raman (2800-3020 cm(-1)) measurements in the larynx. A total of 94 HW Raman spectra (22 normal sites, 72 tumor sites) were acquired from 39 patients who underwent laryngoscopic screening. Significant differences in Raman intensities of prominent Raman bands at 2845, 2880 and 2920 cm(-1) (CH(2) stretching of lipids), and 2940 cm(-1) (CH(3) stretching of proteins) were observed between normal and cancer laryngeal tissue. The diagnostic algorithms based on principal components analysis (PCA) and linear discriminant analysis (LDA) together with the leave-one subject-out, cross-validation method on HW Raman spectra yielded a diagnostic sensitivity of 90.3% (65/72) and specificity of 90.9% (20/22) for laryngeal cancer identification. This study demonstrates that HW Raman spectroscopy has the potential for the noninvasive, real-time diagnosis and detection of laryngeal cancer at the molecular level.     

Proteomic technologies for identification of serum biomarkers of potential autoimmune demyelinating polyneuropathies

Time-of-flight MALDI mass spectrometry (MALDI-TOF MS) profiling of blood serum of patients with Guillain-Barré syndrome (GBS, 36 samples), chronic inflammatory demyelinating polyneuropathy (CIDP, 24 samples), and practically healthy donors (HD, 35 samples) was carried out in order to identify potential biomarkers of autoimmune demyelinating polyneuropathies (ADP). To simplify the peptide-protein mixture of serum prior to MALDI-TOF-MS analysis, samples were prefractionated on magnetic beads with a weak cation-exchange (MB-WCX) surface. Comparative analysis of mass spectrometric data using the classification algorithms (genetic and neural network-controlled) revealed a characteristic set of peaks and a correlating change area with a high specificity and sensitivity of the differentiated mass spectrometry profiles of the blood serum of patients with DPNP and healthy donors (for GBS, values of these characteristics reached 100 and 100%, and for CIDP, 94.1 and 100% respectively). Comparative analysis of mass spectrometric profiles of serum samples obtained from patients with GBS and CIDP allowed us to build a classification model to differentiate these diseases from each other, with a specificity of 88.9 and a sensitivity of 80%.