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Background Identification of transcription factors (TFs) responsible for modulation of differentially expressed genes is a key step in deducing gene regulatory pathways. Most current methods identify TFs by searching for presence of DNA binding motifs in the promoter regions of co-regulated genes. However, this strategy may not always be useful as presence of a motif does not necessarily imply a regulatory role. Conversely, motif presence may not be required for a TF to regulate a set of genes. Therefore, it is imperative to include functional (biochemical and molecular) associations, such as those found in the biomedical literature, into algorithms for identification of putative regulatory TFs that might be explicitly or implicitly linked to the genes under investigation. Results In this study, we present a Latent Semantic Indexing (LSI) based text mining approach for identification and ranking of putative regulatory TFs from microarray derived differentially expressed genes (DEGs). Two LSI models were built using different term weighting schemes to devise pair-wise similarities between 21,027 mouse genes annotated in the Entrez Gene repository. Amongst these genes, 433 were designated TFs in the TRANSFAC database. The LSI derived TF-to-gene similarities were used to calculate TF literature enrichment p-values and rank the TFs for a given set of genes. We evaluated our approach using five different publicly available microarray datasets focusing on TFs Rel , Stat6 , Ddit3 , Stat5 and Nfic . In addition, for each of the datasets, we constructed gold standard TFs known to be functionally relevant to the study in question. Receiver Operating Characteristics (ROC) curves showed that the log-entropy LSI model outperformed the tf -normal LSI model and a benchmark co-occurrence based method for four out of five datasets, as well as motif searching approaches, in identifying putative TFs. Conclusions Our results suggest that our LSI based text mining approach can complement existing approaches used in systems biology research to decipher gene regulatory networks by providing putative lists of ranked TFs that might be explicitly or implicitly associated with sets of DEGs derived from microarray experiments. In addition, unlike motif searching approaches, LSI based approaches can reveal TFs that may indirectly regulate genes.

High-throughput genomic technologies enable researchers to identify genes that are co-regulated with respect to specific experimental conditions. Numerous statistical approaches have been developed to identify differentially expressed genes. Because each approach can produce distinct gene sets, it is difficult for biologists to determine which statistical approach yields biologically relevant gene sets and is appropriate for their study. To address this issue, we implemented Latent Semantic Indexing (LSI) to determine the functional coherence of gene sets. An LSI model was built using over 1 million Medline abstracts for over 20,000 mouse and human genes annotated in Entrez Gene. The gene-to-gene LSI-derived similarities were used to calculate a literature cohesion p-value (LPv) for a given gene set using a Fisher's exact test. We tested this method against genes in more than 6,000 functional pathways annotated in Gene Ontology (GO) and found that approximately 75% of gene sets in GO biological process category and 90% of the gene sets in GO molecular function and cellular component categories were functionally cohesive (LPv<0.05). These results indicate that the LPv methodology is both robust and accurate. Application of this method to previously published microarray datasets demonstrated that LPv can be helpful in selecting the appropriate feature extraction methods. To enable real-time calculation of LPv for mouse or human gene sets, we developed a web tool called Gene-set Cohesion Analysis Tool (GCAT). GCAT can complement other gene set enrichment approaches by determining the overall functional cohesion of data sets, taking into account both explicit and implicit gene interactions reported in the biomedical literature. GCAT is freely available at http://binf1.memphis.edu/gcat.

Text fields in electronic medical records (EMR) contain information on important factors that influence health outcomes, however, they are underutilized in clinical decision making due to their unstructured nature. We analyzed 6497 inpatient surgical cases with 719,308 free text notes from Le Bonheur Children’s Hospital EMR. We used a text mining approach on preoperative notes to obtain a text-based risk score to predict death within 30 days of surgery. In addition, we evaluated the performance of a hybrid model that included the text-based risk score along with structured data pertaining to clinical risk factors. The C-statistic of a logistic regression model with five-fold cross-validation significantly improved from 0.76 to 0.92 when text-based risk scores were included in addition to structured data. We conclude that preoperative free text notes in EMR include significant information that can predict adverse surgery outcomes.

One of the most fundamental questions in PDE is that of global existence. That is, given an initial datum, does a solution exist for all time? In this thesis, we explore this problem in the context of active scalar equations. We consider the motion of a scalar quantity, such as density, being transported by an incompressible fluid. The motion of the density, in turn, affects the motion of the fluid, leading to a coupling between the fluid and the scalar quantity. The nonlinear interaction between the scalar and the fluid makes the question of global existence quite subtle. We explore the problem of global existence from both the positive and negative direction. In the first part of the thesis, we construct an instance of singularity formation in the incompressible porous medium equation in a setting that is not driven by the boundary. In the second part of the thesis, we study solutions to the surface quasi-geostrophic equation with one-homogeneous initial data. We derive a one-dimensional system and prove that positive solutions to the one-dimensional system exist globally in time.

Interferons (IFNs) are critical to the host innate immune response by inducing the expression of a family of early response genes, denoted as IFN-stimulated genes (ISGs). The role of tyrosine phosphorylation of STAT proteins in the transcription activation of ISGs is well-documented. Recent studies have indicated that other transcription factors (TFs) are likely to play a role in regulating ISG expression. Here, we describe a novel integrative approach that combines gene expression profiling, promoter sequence analysis, and literature mining to screen candidate regulatory factors in the IFN signal transduction pathway. Application of this method identified the nuclear factor κB (NFκB) protein, cRel, as a candidate regulatory factor for a subset of ISGs in mouse embryo fibroblasts. Chromatin immunoprecipitation (ChIP) and real-time PCR assays confirmed that cRel directly binds to the promoters of several ISGs, including Cxcl10, Isg15, Gbp2, Ifit3, and Ifi203, and regulates their expression. Thus, our studies identify cRel as an important TF for ISGs, and validate the approach of using Latent Semantic Indexing (LSI)-based methods to identify regulatory factors from microarray data.

ObjectiveWe aimed to use machine learning (ML) models to investigate the impact of clinical, social and behavioural factors on 1-year progression from pre-diabetes to type 2 diabetes mellitus (DM).DesignA retrospective cohort study.SettingA large health system including eight sites in southeast Michigan.ParticipantsAdults with haemoglobin A1c (HbA1c) between 5.7% and 6.4% for two consecutive years between 1 January 2008 and 31 December 2023, and no prior history of type 2 DM or metformin use.Primary outcome measureNew-onset type 2 DM (HbA1c ≥6.5%) in 1 year.ResultsAmong 11 809 individuals, 815 (6.9%) progressed to type 2 DM within 1 year. CatBoost demonstrated the best performance (average area under the curve 0.78). Prior-year HbA1c was the most influential covariate (SHapley Additive exPlanations 1.02). Traditional metabolic factors (high-density lipoprotein, body mass index (BMI), white blood cell, age, gender, triglycerides) also contributed. Lastly, while inclusion of social and behavioural determinants of health (SBDH) did not significantly improve the overall model performance, depression emerged as the prominent SBDH covariate. Depression was a stronger predictor of diabetes progression in individuals with higher baseline BMI and lower baseline HbA1c.ConclusionsInclusion of social and behavioural covariates provided no incremental value for prediction of diabetes progression from pre-diabetes. However, machine learning revealed that depression may play a role in progression to type 2 DM.

Identifying functional groups of genes is a challenging problem for biological applications. Text mining approaches can be used to build hierarchical clusters or trees from the information in the biological literature. In particular, the nonnegative matrix factorization (NMF) is examined as one approach to label hierarchical trees. A generic labeling algorithm as well as an evaluation technique is proposed, and the effects of different NMF parameters with regard to convergence and labeling accuracy are discussed. The primary goals of this study are to provide a qualitative assessment of the NMF and its various parameters and initialization, to provide an automated way to classify biomedical data, and to provide a method for evaluating labeled data assuming a static input tree. As a byproduct, a method for generating gold standard trees is proposed.

Vascular dementia (VaD), incorporating cognitive dysfunction with vascular disease, ranks as the second leading cause of dementia in the United States, yet no effective treatment is currently available. The challenge of defining the pathological substrates of VaD is complicated by the heterogeneous nature of cerebrovascular disease and coexistence of other pathologies, including Alzheimer’s disease (AD) types of lesion. The use of rodent models of ischemic stroke may help to elucidate the type of lesions that are responsible for cognitive impairment in humans. Endovascular middle cerebral artery (MCA) occlusion in rats is considered to be a convenient and reliable model of human cerebral ischemia. Both sensorimotor and cognitive dysfunction can be induced in the rat endovascular MCA occlusion model, yet sensorimotor deficits induced by endovascular MCA occlusion may improve with time, whereas data presented in this review suggest that in rats this model can result in a progressive course of cognitive impairment that is consistent with the clinical progression of VaD. Thus far, experimental studies using this model have demonstrated a direct interaction of cerebral ischemic damage and AD-type neuropathologies in the primary ischemic area. Further, coincident to the progressive decline of cognitive function, a delayed neurodegeneration in a remote area, distal to the primary ischemic area, the hippocampus, has been demonstrated in a rat endovascular MCA occlusion model. We argue that this model could be employed to study VaD and provide insight into some of the pathophysiological mechanisms of VaD.