Machine learning methodology applied to characterize subgroups of gastric cancer patients using an integrated large biomarker dataset

Williams, Coralie; Połom, Karol; Adamczyk, B; Afshar, Mohammad; D’Ignazio, Alessia; Kamali-Moghaddam, M.; Karlsson, Niclas G.; Guergova-Kuras, Mariana; Lisacek, Frédérique; Marrelli, Daniele; Mereiter, Stefan; Morley, David; Parmentier, Frédéric; Reis, Celso A.; Roviello, Franco; Shen, Qiujin; Tognetti, Y.

doi:10.1016/j.ejso.2018.10.285

Cited by 5 publications

(6 citation statements)

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“…The abundance levels of 14 different N-glycans were recently used to distinguish GC tissues from adjacent ones using machine learning integrated with mass spectrometry-based N-glycomics [151]. Experimental glycomics data were already combined with proteomic data and clinical and pathological information using a machine learning methodology (KEM ® , Knowledge Extraction and Management, Ariana Pharma, Cambridge, MA, USA) to characterize the subgroups of GC patients, and a high potentiality of this integrated large biomarker dataset emerged for non-invasive GC diagnosis and prognosis [152].…”

Section: Glycosylation Of Circulating Proteins For Gc Diagnosismentioning

confidence: 99%

Circulating Proteins as Diagnostic Markers in Gastric Cancer

Repetto,

Vettori,

Steffan

et al. 2023

IJMS

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Gastric cancer (GC) is a highly malignant disease affecting humans worldwide and has a poor prognosis. Most GC cases are detected at advanced stages due to the cancer lacking early detectable symptoms. Therefore, there is great interest in improving early diagnosis by implementing targeted prevention strategies. Markers are necessary for early detection and to guide clinicians to the best personalized treatment. The current semi-invasive endoscopic methods to detect GC are invasive, costly, and time-consuming. Recent advances in proteomics technologies have enabled the screening of many samples and the detection of novel biomarkers and disease-related signature signaling networks. These biomarkers include circulating proteins from different fluids (e.g., plasma, serum, urine, and saliva) and extracellular vesicles. We review relevant published studies on circulating protein biomarkers in GC and detail their application as potential biomarkers for GC diagnosis. Identifying highly sensitive and highly specific diagnostic markers for GC may improve patient survival rates and contribute to advancing precision/personalized medicine.

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Section: Glycosylation Of Circulating Proteins For Gc Diagnosismentioning

confidence: 99%

Circulating Proteins as Diagnostic Markers in Gastric Cancer

Repetto,

Vettori,

Steffan

et al. 2023

IJMS

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“…37,38 FCA was introduced by Agrawal and Srikant. 39,40 FCA has been successfully applied in different domains, including: drug discovery, 22,41 studies for the identification of patient selection biomarkers for therapeutic responses, 42,43 genomic characterization of complex diseases, 44 and market basket analysis. 45 FCA association rules may reflect a causal relationship between two variables.…”

Section: Association Rules and The Kem R Platformmentioning

confidence: 99%

“…Both subjects had SIGMAR1 wild type, high mean concentration of blarcamesine (ANAVEX2-73) in plasma and baseline MMSE ≥ 20 (Fig. Summary of mean change in MMSE and mean change in ADCS-ADL scores at week 57 since baseline, Cohen's d effect size calculation compared to standard of care [40][41][42][43] depending on genomic variant status of SIGMAR1 p.Gln2Pro and COMT p.Leu146fs, and/or baseline MMSE score. Mean ± SD Delta scores baseline to week 57 are presented.…”

Section: Confirmation At 148 Weeks: Unadjusted Valuesmentioning

confidence: 99%

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A precision medicine framework using artificial intelligence for the identification and confirmation of genomic biomarkers of response to an Alzheimer's disease therapy: Analysis of the blarcamesine (ANAVEX2‐73) Phase 2a clinical study

Hampel

Williams²,

Etcheto³

et al. 2020

A&D Transl Res & Clin Interv

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Introduction:The search for drugs to treat Alzheimer's disease (AD) has failed to yield effective therapies. Here we report the first genome-wide search for biomarkers associated with therapeutic response in AD. Blarcamesine (ANAVEX2-73), a selective sigma-1 receptor (SIGMAR1) agonist, was studied in a 57-week Phase 2a trial (NCT02244541).The study was extended for a further 208 weeks (NCT02756858) after meeting its primary safety endpoint. Methods: Safety, clinical features, pharmacokinetic, and efficacy, measured by changes in the Mini-Mental State Examination (MMSE) and the Alzheimer's Disease Cooperative Study-Activities of Daily Living scale (ADCS-ADL), were recorded. Whole exome and transcriptome sequences were obtained for 21 patients. The relationship between all available patient data and efficacy outcome measures was analyzed with unsupervised formal concept analysis (FCA), integrated in the Knowledge Extraction and Management (KEM) environment.Results: Biomarkers with a significant impact on clinical outcomes were identified at week 57: mean plasma concentration of blarcamesine (slope MMSE:P < .041), genomic variants SIGMAR1 p.Gln2Pro (ΔMMSE:P < .039; ΔADCS-ADL:P < .063) and COMT p.Leu146fs (ΔMMSE:P < .039; ΔADCS-ADL:P < .063), and baseline MMSE score (slope MMSE:P < .015). Their combined impact on drug response was confirmed at week 148 with linear mixed effect models.

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“…The accuracy of predicting early cancer recurrence was measured using artificial neural network (ANN), estimated using ANN with missing data [6]. In 2019, Williams et al [7] suggested knowledge extraction and management (KEM). KEM can identify all related relationships between variables, even when there is only weak correlation, compared to statistical approaches.…”

Section: Related Workmentioning

confidence: 99%

Chronic Disease Prediction Using Character-Recurrent Neural Network in The Presence of Missing Information

2019

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The aim of this study was to predict chronic diseases in individual patients using a character-recurrent neural network (Char-RNN), which is a deep learning model that treats data in each class as a word when a large portion of its input values is missing. An advantage of Char-RNN is that it does not require any additional imputation method because it implicitly infers missing values considering the relationship with nearby data points. We applied Char-RNN to classify cases in the Korea National Health and Nutrition Examination Survey (KNHANES) VI as normal status and five chronic diseases: hypertension, stroke, angina pectoris, myocardial infarction, and diabetes mellitus. We also employed a multilayer perceptron network for the same task for comparison. The results show higher accuracy for Char-RNN than for the conventional multilayer perceptron model. Char-RNN showed remarkable performance in finding patients with hypertension and stroke. The present study utilized the KNHANES VI data to demonstrate a practical approach to predicting and managing chronic diseases with partially observed information.

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Machine learning methodology applied to characterize subgroups of gastric cancer patients using an integrated large biomarker dataset

Cited by 5 publications

References 0 publications

Circulating Proteins as Diagnostic Markers in Gastric Cancer

Circulating Proteins as Diagnostic Markers in Gastric Cancer

A precision medicine framework using artificial intelligence for the identification and confirmation of genomic biomarkers of response to an Alzheimer's disease therapy: Analysis of the blarcamesine (ANAVEX2‐73) Phase 2a clinical study

Chronic Disease Prediction Using Character-Recurrent Neural Network in The Presence of Missing Information

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