Predicting nutrition and environmental factors associated with female reproductive disorders using a knowledge graph and random forests.

OBJECTIVE: Female reproductive disorders (FRDs) are common health conditions that may present with significant symptoms. Diet and environment are potential areas for FRD interventions. We utilized a knowledge graph (KG) method to predict factors associated with common FRDs (for example, endometriosis, ovarian cyst, and uterine fibroids). MATERIALS AND METHODS: We harmonized survey data from the Personalized Environment and Genes Study (PEGS) on internal and external environmental exposures and health conditions with biomedical ontology content. We merged the harmonized data and ontologies with supplemental nutrient and agricultural chemical data to create a KG. We analyzed the KG by embedding edges and applying a random forest for edge prediction to identify variables potentially associated with FRDs. We also conducted logistic regression analysis for comparison. RESULTS: Across 9765 PEGS respondents, the KG analysis resulted in 8535 significant or suggestive predicted links between FRDs and chemicals, phenotypes, and diseases. Amongst these links, 32 were exact matches when compared with the logistic regression results, including comorbidities, medications, foods, and occupational exposures. DISCUSSION: Mechanistic underpinnings of predicted links documented in the literature may support some of our findings. Our KG methods are useful for predicting possible associations in large, survey-based datasets with added information on directionality and magnitude of effect from logistic regression. These results should not be construed as causal but can support hypothesis generation. CONCLUSION: This investigation enabled the generation of hypotheses on a variety of potential links between FRDs and exposures. Future investigations should prospectively evaluate the variables hypothesized to impact FRDs.

Predicting nutrition and environmental factors associated with female reproductive disorders using a knowledge graph and random forests.

Objective: Female reproductive disorders (FRDs) are common health conditions that may present with significant symptoms. Diet and environment are potential areas for FRD interventions. We utilized a knowledge graph (KG) method to predict factors associated with common FRDs (e.g., endometriosis, ovarian cyst, and uterine fibroids). Materials and Methods: We harmonized survey data from the Personalized Environment and Genes Study on internal and external environmental exposures and health conditions with biomedical ontology content. We merged the harmonized data and ontologies with supplemental nutrient and agricultural chemical data to create a KG. We analyzed the KG by embedding edges and applying a random forest for edge prediction to identify variables potentially associated with FRDs. We also conducted logistic regression analysis for comparison. Results: Across 9765 PEGS respondents, the KG analysis resulted in 8535 significant predicted links between FRDs and chemicals, phenotypes, and diseases. Amongst these links, 32 were exact matches when compared with the logistic regression results, including comorbidities, medications, foods, and occupational exposures. Discussion: Mechanistic underpinnings of predicted links documented in the literature may support some of our findings. Our KG methods are useful for predicting possible associations in large, survey-based datasets with added information on directionality and magnitude of effect from logistic regression. These results should not be construed as causal, but can support hypothesis generation. Conclusion: This investigation enabled the generation of hypotheses on a variety of potential links between FRDs and exposures. Future investigations should prospectively evaluate the variables hypothesized to impact FRDs.

Metformin is associated with reduced COVID-19 severity in patients with prediabetes.

AIMS: Studies suggest that metformin is associated with reduced COVID-19 severity in individuals with diabetes compared to other antihyperglycemics. We assessed if metformin is associated with reduced incidence of severe COVID-19 for patients with prediabetes or polycystic ovary syndrome (PCOS), common diseases that increase the risk of severe COVID-19. METHODS: This observational, retrospective study utilized EHR data from 52 hospitals for COVID-19 patients with PCOS or prediabetes treated with metformin or levothyroxine/ondansetron (controls). After balancing via inverse probability score weighting, associations with COVID-19 severity were assessed by logistic regression. RESULTS: In the prediabetes cohort, when compared to levothyroxine, metformin was associated with a significantly lower incidence of COVID-19 with "mild-ED" or worse (OR [95% CI]: 0.636, [0.455-0.888]) and "moderate" or worse severity (0.493 [0.339-0.718]). Compared to ondansetron, metformin was associated with lower incidence of "mild-ED" or worse severity (0.039 [0.026-0.057]), "moderate" or worse (0.045 [0.03-0.069]), "severe" or worse (0.183 [0.077-0.431]), and "mortality/hospice" (0.223 [0.071-0.694]). For PCOS, metformin showed no significant differences in severity compared to levothyroxine, but was associated with a significantly lower incidence of "mild-ED" or worse (0.101 [0.061-0.166]), and "moderate" or worse (0.094 [0.049-0.18]) COVID-19 outcome compared to ondansetron. CONCLUSIONS: Metformin use is associated with less severe COVID-19 in patients with prediabetes or PCOS.

Supervised learning with word embeddings derived from PubMed captures latent knowledge about protein kinases and cancer.

Inhibiting protein kinases (PKs) that cause cancers has been an important topic in cancer therapy for years. So far, almost 8% of >530 PKs have been targeted by FDA-approved medications, and around 150 protein kinase inhibitors (PKIs) have been tested in clinical trials. We present an approach based on natural language processing and machine learning to investigate the relations between PKs and cancers, predicting PKs whose inhibition would be efficacious to treat a certain cancer. Our approach represents PKs and cancers as semantically meaningful 100-dimensional vectors based on word and concept neighborhoods in PubMed abstracts. We use information about phase I-IV trials in ClinicalTrials.gov to construct a training set for random forest classification. Our results with historical data show that associations between PKs and specific cancers can be predicted years in advance with good accuracy. Our tool can be used to predict the relevance of inhibiting PKs for specific cancers and to support the design of well-focused clinical trials to discover novel PKIs for cancer therapy.

FZD6 triggers Wnt-signalling driven by WNT10BIVS1 expression and highlights new targets in T-cell acute lymphoblastic leukemia.

Wnt/Fzd signaling has been implicated in hematopoietic stem cell maintenance and in acute leukemia establishment. In our previous work, we described a recurrent rearrangement involving the WNT10B locus (WNT10BR ), characterized by the expression of WNT10BIVS1 transcript variant, in acute myeloid leukemia. To determine the occurrence of WNT10BR in T-cell acute lymphoblastic leukemia (T-ALL), we retrospectively analyzed an Italian cohort of patients (n = 20) and detected a high incidence (13/20) of WNT10BIVS1 expression. To address genes involved in WNT10B molecular response, we have designed a Wnt-targeted RNA sequencing panel. Identifying Wnt agonists and antagonists, it results that the expression of FZD6, LRP5, and PROM1 genes stands out in WNT10BIVS1 positive patients compared to negative ones. Using MOLT4 and MUTZ-2 as leukemic cell models, which are characterized by the expression of WNT10BIVS1 , we have observed that WNT10B drives major Wnt activation to the FZD6 receptor complex through receipt of ligand. Additionally, short hairpin RNAs (shRNAs)-mediated gene silencing and small molecule-mediated inhibition of WNTs secretion have been observed to interfere with the WNT10B/FZD6 interaction. We have therefore identified that WNT10BIVS1 knockdown, or pharmacological interference by the LGK974 porcupine (PORCN) inhibitor, reduces WNT10B/FZD6 protein complex formation and significantly impairs intracellular effectors and leukemic expansion. These results describe the molecular circuit induced by WNT10B and suggest WNT10B/FZD6 as a new target in the T-ALL treatment strategy.

Network modeling of patients' biomolecular profiles for clinical phenotype/outcome prediction.

Methods for phenotype and outcome prediction are largely based on inductive supervised models that use selected biomarkers to make predictions, without explicitly considering the functional relationships between individuals. We introduce a novel network-based approach named Patient-Net (P-Net) in which biomolecular profiles of patients are modeled in a graph-structured space that represents gene expression relationships between patients. Then a kernel-based semi-supervised transductive algorithm is applied to the graph to explore the overall topology of the graph and to predict the phenotype/clinical outcome of patients. Experimental tests involving several publicly available datasets of patients afflicted with pancreatic, breast, colon and colorectal cancer show that our proposed method is competitive with state-of-the-art supervised and semi-supervised predictive systems. Importantly, P-Net also provides interpretable models that can be easily visualized to gain clues about the relationships between patients, and to formulate hypotheses about their stratification.

Cancer module genes ranking using kernelized score functions.

BACKGROUND: Co-expression based Cancer Modules (CMs) are sets of genes that act in concert to carry out specific functions in different cancer types, and are constructed by exploiting gene expression profiles related to specific clinical conditions or expression signatures associated to specific processes altered in cancer. Unfortunately, genes involved in cancer are not always detectable using only expression signatures or co-expressed sets of genes, and in principle other types of functional interactions should be exploited to obtain a comprehensive picture of the molecular mechanisms underlying the onset and progression of cancer. RESULTS: We propose a novel semi-supervised method to rank genes with respect to CMs using networks constructed from different sources of functional information, not limited to gene expression data. It exploits on the one hand local learning strategies through score functions that extend the guilt-by-association approach, and on the other hand global learning strategies through graph kernels embedded in the score functions, able to take into account the overall topology of the network. The proposed kernelized score functions compare favorably with other state-of-the-art semi-supervised machine learning methods for gene ranking in biological networks and scales well with the number of genes, thus allowing fast processing of very large gene networks. CONCLUSIONS: The modular nature of kernelized score functions provides an algorithmic scheme from which different gene ranking algorithms can be derived, and the results show that using integrated functional networks we can successfully predict CMs defined mainly through expression signatures obtained from gene expression data profiling. A preliminary analysis of top ranked "false positive" genes shows that our approach could be in perspective applied to discover novel genes involved in the onset and progression of tumors related to specific CMs.

Regeneration-associated WNT signaling is activated in long-term reconstituting AC133bright acute myeloid leukemia cells.

Acute myeloid leukemia (AML) is a genetically heterogeneous clonal disorder characterized by two molecularly distinct self-renewing leukemic stem cell (LSC) populations most closely related to normal progenitors and organized as a hierarchy. A requirement for WNT/ß-catenin signaling in the pathogenesis of AML has recently been suggested by a mouse model. However, its relationship to a specific molecular function promoting retention of self-renewing leukemia-initiating cells (LICs) in human remains elusive. To identify transcriptional programs involved in the maintenance of a self-renewing state in LICs, we performed the expression profiling in normal (n = 10) and leukemic (n = 33) human long-term reconstituting AC133(+) cells, which represent an expanded cell population in most AML patients. This study reveals the ligand-dependent WNT pathway activation in AC133(bright) AML cells and shows a diffuse expression and release of WNT10B, a hematopoietic stem cell regenerative-associated molecule. The establishment of a primary AC133(+) AML cell culture (A46) demonstrated that leukemia cells synthesize and secrete WNT ligands, increasing the levels of dephosphorylated ß-catenin in vivo. We tested the LSC functional activity in AC133(+) cells and found significant levels of engraftment upon transplantation of A46 cells into irradiated Rag2(-/-)γc(-/-) mice. Owing to the link between hematopoietic regeneration and developmental signaling, we transplanted A46 cells into developing zebrafish. This system revealed the formation of ectopic structures by activating dorsal organizer markers that act downstream of the WNT pathway. In conclusion, our findings suggest that AC133(bright) LSCs are promoted by misappropriating homeostatic WNT programs that control hematopoietic regeneration.

A mathematical model for the validation of gene selection methods.

Gene selection methods aim at determining biologically relevant subsets of genes in DNA microarray experiments. However, their assessment and validation represent a major difficulty since the subset of biologically relevant genes is usually unknown. To solve this problem a novel procedure for generating biologically plausible synthetic gene expression data is proposed. It is based on a proper mathematical model representing gene expression signatures and expression profiles through Boolean threshold functions. The results show that the proposed procedure can be successfully adopted to analyze the quality of statistical and machine learning-based gene selection algorithms.

Fuzzy ensemble clustering based on random projections for DNA microarray data analysis.

OBJECTIVE: Two major problems related the unsupervised analysis of gene expression data are represented by the accuracy and reliability of the discovered clusters, and by the biological fact that the boundaries between classes of patients or classes of functionally related genes are sometimes not clearly defined. The main goal of this work consists in the exploration of new strategies and in the development of new clustering methods to improve the accuracy and robustness of clustering results, taking into account the uncertainty underlying the assignment of examples to clusters in the context of gene expression data analysis. METHODOLOGY: We propose a fuzzy ensemble clustering approach both to improve the accuracy of clustering results and to take into account the inherent fuzziness of biological and bio-medical gene expression data. We applied random projections that obey the Johnson-Lindenstrauss lemma to obtain several instances of lower dimensional gene expression data from the original high-dimensional ones, approximately preserving the information and the metric structure of the original data. Then we adopt a double fuzzy approach to obtain a consensus ensemble clustering, by first applying a fuzzy k-means algorithm to the different instances of the projected low-dimensional data and then by using a fuzzy t-norm to combine the multiple clusterings. Several variants of the fuzzy ensemble clustering algorithms are proposed, according to different techniques to combine the base clusterings and to obtain the final consensus clustering. RESULTS AND CONCLUSION: We applied our proposed fuzzy ensemble methods to the gene expression analysis of leukemia, lymphoma, adenocarcinoma and melanoma patients, and we compared the results with other state of the art ensemble methods. Results show that in some cases, taking into account the natural fuzziness of the data, we can improve the discovery of classes of patients defined at bio-molecular level. The reduction of the dimension of the data, achieved through random projections techniques, is well-suited to the characteristics of high-dimensional gene expression data, thus resulting in improved performance with respect to single fuzzy k-means and with respect to ensemble methods based on resampling techniques. Moreover, we show that the analysis of the accuracy and diversity of the base fuzzy clusterings can be useful to explain the advantages and the limitations of the proposed fuzzy ensemble approach.

Randomized maps for assessing the reliability of patients clusters in DNA microarray data analyses.

OBJECTIVE: Clustering algorithms may be applied to the analysis of DNA microarray data to identify novel subgroups that may lead to new taxonomies of diseases defined at bio-molecular level. A major problem related to the identification of biologically meaningful clusters is the assessment of their reliability, since clustering algorithms may find clusters even if no structure is present. METHODOLOGY: Recently, methods based on random "perturbations" of the data, such as bootstrapping, noise injections techniques and random subspace methods have been applied to the problem of cluster validity estimation. In this framework, we propose stability measures that exploits the high dimensionality of DNA microarray data and the redundancy of information stored in microarray chips. To this end we randomly project the original gene expression data into lower dimensional subspaces, approximately preserving the distance between the examples according to the Johnson-Lindenstrauss (JL) theory. The stability of the clusters discovered in the original high dimensional space is estimated by comparing them with the clusters discovered in randomly projected lower dimensional subspaces. The proposed cluster-stability measures may be applied to validate and to quantitatively assess the reliability of the clusters obtained by a large class of clustering algorithms. RESULTS AND CONCLUSION: We tested the effectiveness of our approach with high dimensional synthetic data, whose distribution is a priori known, showing that the stability measures based on randomized maps correctly predict the number of clusters and the reliability of each individual cluster. Then we showed how to apply the proposed measures to the analysis of DNA microarray data, whose underlying distribution is unknown. We evaluated the validity of clusters discovered by hierarchical clustering algorithms in diffuse large B-cell lymphoma (DLBCL) and malignant melanoma patients, showing that the proposed reliability measures can support bio-medical researchers in the identification of stable clusters of patients and in the discovery of new subtypes of diseases characterized at bio-molecular level.

Gene expression data analysis of human lymphoma using support vector machines and output coding ensembles.

The large amount of data generated by DNA microarrays was originally analysed using unsupervised methods, such as clustering or self-organizing maps. Recently supervised methods such as decision trees, dot-product support vector machines (SVM) and multi-layer perceptrons (MLP) have been applied in order to classify normal and tumoural tissues. We propose methods based on non-linear SVM with polynomial and Gaussian kernels, and output coding (OC) ensembles of learning machines to separate normal from malignant tissues, to classify different types of lymphoma and to analyse the role of sets of coordinately expressed genes in carcinogenic processes of lymphoid tissues. Using gene expression data from "Lymphochip", a specialised DNA microarray developed at Stanford University School of Medicine, we show that SVM can correctly separate normal from tumoural tissues, and OC ensembles can be successfully used to classify different types of lymphoma. Moreover, we identify a group of coordinately expressed genes related to the separation of two distinct subgroups inside diffuse large B-cell lymphoma (DLBCL), validating a previous Alizadeh's hypothesis about the existence of two distinct diseases inside DLBCL.