AI-enhanced integration of genetic and medical imaging data for risk assessment of Type 2 diabetes.

Type 2 diabetes (T2D) presents a formidable global health challenge, highlighted by its escalating prevalence, underscoring the critical need for precision health strategies and early detection initiatives. Leveraging artificial intelligence, particularly eXtreme Gradient Boosting (XGBoost), we devise robust risk assessment models for T2D. Drawing upon comprehensive genetic and medical imaging datasets from 68,911 individuals in the Taiwan Biobank, our models integrate Polygenic Risk Scores (PRS), Multi-image Risk Scores (MRS), and demographic variables, such as age, sex, and T2D family history. Here, we show that our model achieves an Area Under the Receiver Operating Curve (AUC) of 0.94, effectively identifying high-risk T2D subgroups. A streamlined model featuring eight key variables also maintains a high AUC of 0.939. This high accuracy for T2D risk assessment promises to catalyze early detection and preventive strategies. Moreover, we introduce an accessible online risk assessment tool for T2D, facilitating broader applicability and dissemination of our findings.

Fatty liver classification via risk controlled neural networks trained on grouped ultrasound image data.

Ultrasound imaging is a widely used technique for fatty liver diagnosis as it is practically affordable and can be quickly deployed by using suitable devices. When it is applied to a patient, multiple images of the targeted tissues are produced. We propose a machine learning model for fatty liver diagnosis from multiple ultrasound images. The machine learning model extracts features of the ultrasound images by using a pre-trained image encoder. It further produces a summary embedding on these features by using a graph neural network. The summary embedding is used as input for a classifier on fatty liver diagnosis. We train the machine learning model on a ultrasound image dataset collected by Taiwan Biobank. We also carry out risk control on the machine learning model using conformal prediction. Under the risk control procedure, the classifier can improve the results with high probabilistic guarantees.

Exploring Factors Underlying Poorly-Controlled Asthma in Adults by Integrating Phenotypes and Genotypes Associated with Obesity and Asthma: A Case-Control Study.

Background: Uncontrolled asthma in adults leads to poor clinical outcome, while the clinical heterogeneity of phenotypes interferes the applicable genetic determinants. This study aimed to identify phenotypes and genetic impact on poorly-controlled asthma to optimize individualized treatment strategies. Methods: This propensity score-matched case-control study included 340 and 1020 asthmatics with poorly-controlled asthma and well-controlled asthma, respectively. Data were obtained from the 2008-2015 Taiwan Biobank Database and linked to the National Health Insurance Research Database. All asthmatics were aged ≥30 years, without cancer history, and each completed a questionnaire, physical examination, and genome-wide single nucleotide polymorphisms (SNPs). Multivariate adjusted odds ratios (ORs) for genetic risk scores were calculated using conditional logistic regression, stratified by age and sex. A model integrating obesity- and asthma-associated phenotypes and genotypes was applied for poorly-controlled asthma risk prediction. Results: General obesity with body mass index (BMI) ≥27 kg/m2 (OR:1.49, 95% confidence interval (CI) 1.09-2.03), central obesity with waist-to-height ratio (WHtR) ≥0.5 (OR:1.62, 95% CI 1.22-2.15), and parental history of asthma (OR:1.65, and 1.68; for BMI model and WHtR model, respectively) were significantly associated with poorly-controlled asthma in adults, and the combination effect of both obesity phenotypes was 1.66 (95% CI 1.17-2.35). A total of 16 obesity-associated SNPs and 9 asthma-associated SNPs were converted into genetic scores, and the aforementioned phenotypes were incorporated into the risk prediction model for poorly-controlled asthma, with an area under curve 0.72 in the receiver operating characteristic curve. The potential biological functions of genes are involved in immunity pathways. Conclusion: The prediction model integrating obesity-asthma phenotypes and genotypes for poorly-controlled asthma can facilitate the prediction of high-risk asthma and provide potential targets for novel treatment.

A Deep Generative Model for Reordering Adjacency Matrices.

Depending on the node ordering, an adjacency matrix can highlight distinct characteristics of a graph. Deriving a "proper" node ordering is thus a critical step in visualizing a graph as an adjacency matrix. Users often try multiple matrix reorderings using different methods until they find one that meets the analysis goal. However, this trial-and-error approach is laborious and disorganized, which is especially challenging for novices. This paper presents a technique that enables users to effortlessly find a matrix reordering they want. Specifically, we design a generative model that learns a latent space of diverse matrix reorderings of the given graph. We also construct an intuitive user interface from the learned latent space by creating a map of various matrix reorderings. We demonstrate our approach through quantitative and qualitative evaluations of the generated reorderings and learned latent spaces. The results show that our model is capable of learning a latent space of diverse matrix reorderings. Most existing research in this area generally focused on developing algorithms that can compute "better" matrix reorderings for particular circumstances. This paper introduces a fundamentally new approach to matrix visualization of a graph, where a machine learning model learns to generate diverse matrix reorderings of a graph.

Relationship among genetic variants, obesity traits and asthma in the Taiwan Biobank.

BACKGROUND AND OBJECTIVE: Obesity and asthma impose a heavy health and economic burden on millions of people around the world. The complex interaction between genetic traits and phenotypes caused the mechanism between obesity and asthma is still vague. This study investigates the relationship among obesity-related polygenic risk score (PRS), obesity phenotypes and the risk of having asthma. METHODS: This is a matched case-control study, with 4 controls (8288 non-asthmatic) for each case (2072 asthmatic). Data were obtained from the 2008-2015 Taiwan Biobank Database and linked to the 2000-2016 National Health Insurance Research Database. All participants were ≥30 years old with no history of cancer and had a complete questionnaire, as well as physical examination, genome-wide single nucleotide polymorphisms and clinical diagnosis data. Environmental exposure, PM2.5, was also considered. Multivariate adjusted ORs and 95% CIs were calculated using conditional logistic regression stratified by age and sex. Mediation analysis was also assessed, using a generalised linear model. RESULTS: We found that the obese phenotype was associated with significantly increased odds of asthma by approximately 26%. Four obesity-related PRS, including body mass index (OR=1.07 (1.01-1.13)), waist circumference (OR=1.10 (1.04-1.17)), central obesity as defined by waist-to-height ratio (OR=1.09 (1.03-1.15)) and general-central obesity (OR=1.06 (1.00-1.12)), were associated with increased odds of asthma. Additional independent risk factors for asthma included lower educational level, family history of asthma, certain chronic diseases and increased PM2.5 exposure. Obesity-related PRS is an indirect risk factor for asthma, the link being fully mediated by the trait of obesity. CONCLUSIONS: Obese phenotypes and obesity-related PRS are independent risk factors for having asthma in adults in the Taiwan Biobank. Overall, genetic risk for obesity increases the risk of asthma by affecting the obese phenotype.

Subtyping of major SARS-CoV-2 variants reveals different transmission dynamics based on 10 million genomes.

SARS-CoV-2 continues to evolve, causing waves of the pandemic. Up to May 2022, 10 million genome sequences have accumulated, which are classified into five major variants of concern. With the growing number of sequenced genomes, analysis of the big dataset has become increasingly challenging. Here we developed systematic approaches based on sets of correlated single nucleotide variations (SNVs) for comprehensive subtyping and pattern recognition of transmission dynamics. The approach outperformed single-SNV and spike-centric scans. Moreover, the derived subtypes elucidate the relationship of signature SNVs and transmission dynamics. We found that different subtypes of the same variant, including Delta and Omicron exhibited distinct temporal trajectories. For example, some Delta and Omicron subtypes did not spread rapidly, while others did. We identified sets of characteristic SNVs that appeared to enhance transmission or decrease efficacy of antibodies for some subtypes. We also identified a set of SNVs that appeared to suppress transmission or increase viral sensitivity to antibodies. For the Omicron variant, the dominant type in the world, we identified the subtypes with enhanced and suppressed transmission in an analysis of eight million genomes as of March 2022 and further confirmed the findings in a later analysis of ten million genomes as of May 2022. While the "enhancer" SNVs exhibited an enriched presence on the spike protein, the "suppressor" SNVs are mainly elsewhere. Disruption of the SNV correlation largely destroyed the enhancer-suppressor phenomena. These results suggest the importance of fine subtyping of variants, and point to potential complex interactions among SNVs.

Analysis of genomic distributions of SARS-CoV-2 reveals a dominant strain type with strong allelic associations.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causal agent of COVID 19, continues to evolve since its first emergence in December 2019. Using the complete sequences of 1,932 SARS-CoV-2 genomes, various clustering analyses consistently identified six types of the strains. Independent of the dendrogram construction, 13 signature variations in the form of single nucleotide variations (SNVs) in protein coding regions and one SNV in the 5' untranslated region (UTR) were identified and provided a direct interpretation for the six types (types I to VI). The six types of the strains and their underlying signature SNVs were validated in two subsequent analyses of 6,228 and 38,248 SARS-CoV-2 genomes which became available later. To date, type VI, characterized by the four signature SNVs C241T (5'UTR), C3037T (nsp3 F924F), C14408T (nsp12 P4715L), and A23403G (Spike D614G), with strong allelic associations, has become the dominant type. Since C241T is in the 5' UTR with uncertain significance and the characteristics can be captured by the other three strongly associated SNVs, we focus on the other three. The increasing frequency of the type VI haplotype 3037T-14408T-23403G in the majority of the submitted samples in various countries suggests a possible fitness gain conferred by the type VI signature SNVs. The fact that strains missing one or two of these signature SNVs fail to persist implies possible interactions among these SNVs. Later SNVs such as G28881A, G28882A, and G28883C have emerged with strong allelic associations, forming new subtypes. This study suggests that SNVs may become an important consideration in SARS-CoV-2 classification and surveillance.

Covariate-adjusted heatmaps for visualizing biological data via correlation decomposition.

Motivation: Heatmap is a popular visualization technique in biology and related fields. In this study, we extend heatmaps within the framework of matrix visualization (MV) by incorporating a covariate adjustment process through the estimation of conditional correlations. MV can explore the embedded information structure of high-dimensional large-scale datasets effectively without dimension reduction. The benefit of the proposed covariate-adjusted heatmap is in the exploration of conditional association structures among the subjects or variables that cannot be done with conventional MV. Results: For adjustment of a discrete covariate, the conditional correlation is estimated by the within and between analysis. This procedure decomposes a correlation matrix into the within- and between-component matrices. The contribution of the covariate effects can then be assessed through the relative structure of the between-component to the original correlation matrix while the within-component acts as a residual. When a covariate is of continuous nature, the conditional correlation is equivalent to the partial correlation under the assumption of a joint normal distribution. A test is then employed to identify the variable pairs which possess the most significant differences at varying levels of correlation before and after a covariate adjustment. In addition, a z-score significance map is constructed to visualize these results. A simulation and three biological datasets are employed to illustrate the power and versatility of our proposed method. Availability and implementation: GAP is available to readers and is free to non-commercial applications. The installation instructions, the user's manual, and the detailed tutorials can be found at http://gap.stat.sinica.edu.tw/Software/GAP. Supplementary information: Supplementary Data are available at Bioinformatics online.

Using an Event-History with Risk-Free Model to Study the Genetics of Alcoholism.

Case-control genetic association studies typically ignore possible later disease onset in currently healthy subjects and assume that subjects with diseases equally contribute to the likelihood for inference, regardless of their onset age. Therefore, we used an event-history with risk-free model to simultaneously characterize alcoholism susceptibility and onset age in 65 independent non-Hispanic Caucasian males in the Collaborative Study on the Genetics of Alcoholism. Following data quality control, we analysed 22 single nucleotide polymorphisms (SNPs) on 12 candidate genes. The single-SNP analysis showed that the dominant minor allele of rs2134655 on DRD3 increases alcoholism susceptibility; the dominant minor allele of rs1439047 on NTRK2 delays the alcoholism onset age, but the additive minor allele of rs172677 on GRIN2B and the dominant minor allele of rs63319 on ALDH1A1 advance the alcoholism onset age; and the dominant minor allele of rs1079597 on DRD2 shortens the onset age range. Similarly, multiple-SNPs analysis revealed joint effects of rs2134655, rs172677 and rs1079597, with an adjustment for habitual smoking. This study provides a more comprehensive understanding of the genetics of alcoholism than previous case-control studies.

Gene Expression Profiling and Pathway Network Analysis Predicts a Novel Antitumor Function for a Botanical-Derived Drug, PG2.

PG2 is a botanical drug that is mostly composed of Astragalus polysaccharides (APS). Its role in hematopoiesis and relieving cancer-related fatigue has recently been clinically investigated in cancer patients. However, systematic analyses of its functions are still limited. The aim of this study was to use microarray-based expression profiling to evaluate the quality and consistency of PG2 from three different product batches and to study biological mechanisms of PG2. An integrative molecular analysis approach has been designed to examine significant PG2-induced signatures in HL-60 leukemia cells. A quantitative analysis of gene expression signatures was conducted for PG2 by hierarchical clustering of correlation coefficients. The results showed that PG2 product batches were consistent and of high quality. These batches were also functionally equivalent to each other with regard to how they modulated the immune and hematopoietic systems. Within the PG2 signature, there were five genes associated with doxorubicin: IL-8, MDM4, BCL2, PRODH2, and BIRC5. Moreover, the combination of PG2 and doxorubicin had a synergistic effect on induced cell death in HL-60 cells. Together with the bioinformatics-based approach, gene expression profiling provided a quantitative measurement for the quality and consistency of herbal medicines and revealed new roles (e.g., immune modulation) for PG2 in cancer treatment.

Symptom patterns and phenotypic subgrouping of women with polycystic ovary syndrome: association between endocrine characteristics and metabolic aberrations.

STUDY QUESTION: What are the potential endocrine characteristics related to risk and severity of metabolic disturbances in women with polycystic ovary syndrome (PCOS)? SUMMARY ANSWER: Women with PCOS could be subtyped into four subgroups according to heterogeneous endocrine characteristics and the major predictive endocrine factors for metabolic aberrations among different subgroups were free androgen index (FAI) and luteinizing hormone (LH) levels. WHAT IS KNOWN ALREADY: Women diagnosed with PCOS present with highly heterogeneous phenotypes, including endocrine and metabolic aberrations. Different strategies have been proposed to predict the metabolic outcomes but whether the endocrine factors can solely predict the metabolic aberrations is still inconclusive. STUDY DESIGN, SIZE, DURATION: A cross-sectional study including 460 patients recruited from a reproductive endocrinology outpatient clinic of a tertiary medical center. PARTICIPANTS/MATERIALS, SETTING, METHODS: Patients with PCOS diagnosed according to the 2003 Rotterdam criteria were studied. Clinical history recorded by questionnaires, anthropometric measurements, biochemistry tests after an overnight fast, and pelvic ultrasonography were collected from all patients. MAIN RESULTS AND THE ROLE OF CHANCE: Applying a matrix visualization and clustering approach (generalized association plots), the patients were divided into four distinct clusters according to the correlation with four endocrine parameters. Each cluster exhibited specific endocrine characteristics and the prevalence of metabolic syndrome (MS) was significantly different among the clusters (P < 0.0001). The high-risk subgroups for MS included one cluster with higher mean (SD) FAI (39.6 (14.7) in cluster 4), and another one with lower mean (SD) FAI (10 (6.4) in cluster 2). A common endocrine characteristic of these two metabolically unhealthy clusters was relatively lower LH level. Contrarily, higher LH level (≧15 mIU/ml) during early follicular phase was found to be the best indicator of the metabolically healthy cluster (cluster 1). While high FAI level did correlate with more severe metabolic aberrations, high LH level showed better predictive value than low FAI level to become a metabolically healthy cluster. LIMITATIONS, REASONS FOR CAUTION: The results should be applied to other populations with caution due to racial or environmental differences. Another limitation is a lack of normal non-PCOS control in our study. WIDER IMPLICATIONS OF THE FINDINGS: Stratifying women with PCOS into meaningful subtypes could provide a better understanding of related risk factors and potentially enable the design and delivery of more effective screening and treatment intervention. STUDY FUNDING/COMPETING INTERESTS: This study was supported by grant NSC 100-2314-B002-027-MY3 from the National Science Council of Taiwan. TRIAL REGISTRATION NUMBER: Nil.

Using an in situ proximity ligation assay to systematically profile endogenous protein-protein interactions in a pathway network.

Signal transduction pathways in the cell require protein-protein interactions (PPIs) to respond to environmental cues. Diverse experimental techniques for detecting PPIs have been developed. However, the huge amount of PPI data accumulated from various sources poses a challenge with respect to data reliability. Herein, we collected ∼ 700 primary antibodies and employed a highly sensitive and specific technique, an in situ proximity ligation assay, to investigate 1204 endogenous PPIs in HeLa cells, and 557 PPIs of them tested positive. To overview the tested PPIs, we mapped them into 13 PPI public databases, which showed 72% of them were annotated in the Human Protein Reference Database (HPRD) and 8 PPIs were new PPIs not in the PubMed database. Moreover, TP53, CTNNB1, AKT1, CDKN1A, and CASP3 were the top 5 proteins prioritized by topology analyses of the 557 PPI network. Integration of the PPI-pathway interaction revealed that 90 PPIs were cross-talk PPIs linking 17 signaling pathways based on Reactome annotations. The top 2 connected cross-talk PPIs are MAPK3-DAPK1 and FAS-PRKCA interactions, which link 9 and 8 pathways, respectively. In summary, we established an open resource for biological modules and signaling pathway profiles, providing a foundation for comprehensive analysis of the human interactome.

Differential network biology reveals a positive correlation between a novel protein-protein interaction and cancer cells migration.

This paper introduces a differential network biology for discovering tumor migration. We applied statistical methods to prioritize PPI candidates and an in situ proximity ligation assay to verify 67 endogenous PPIs among 21 interlinked pathways in two hepatocellular carcinoma (HCC) cells, Huh7 (minimally migratory cells) and Mahlavu (highly migratory cells). Differential network biology analysis was applied to determine the novel interaction, CRKL-FLT1, has a high centrality ranking, and the expression of this interaction is strongly correlated with the migratory ability of HCC and other cancer cell lines. Knockdown of CRKL and FLT1 in HCC cells leads to a decrease in cell migration. This study demonstrated that functional exploration of a disease network with differential network in interlinked pathways via PPIs can be used to discover tumor migration.

Increased gene expression of FOXP1 in patients with autism spectrum disorders.

BACKGROUND: Comparative gene expression profiling analysis is useful in discovering differentially expressed genes associated with various diseases, including mental disorders. Autism spectrum disorders (ASD) are a group of complex childhood-onset neurodevelopmental and genetic disorders characterized by deficits in language development and verbal communication, impaired reciprocal social interaction, and the presence of repetitive behaviors or restricted interests. The study aimed to identify novel genes associated with the pathogenesis of ASD. METHODS: We conducted comparative total gene expression profiling analysis of lymphoblastoid cell lines (LCL) between 16 male patients with ASD and 16 male control subjects to screen differentially expressed genes associated with ASD. We verified one of the differentially expressed genes, FOXP1, using real-time quantitative PCR (RT-qPCR) in a sample of 83 male patients and 83 male controls that included the initial 16 male patients and male controls, respectively. RESULTS: A total of 252 differentially expressed probe sets representing 202 genes were detected between the two groups, including 89 up- and 113 downregulated genes in the ASD group. RT-qPCR verified significant elevation of the FOXP1 gene transcript of LCL in a sample of 83 male patients (10.46 ± 11.34) compared with 83 male controls (5.17 ± 8.20, P = 0.001). CONCLUSIONS: Comparative gene expression profiling analysis of LCL is useful in discovering novel genetic markers associated with ASD. Elevated gene expression of FOXP1 might contribute to the pathogenesis of ASD. CLINICAL TRIAL REGISTRATION: Identifier: NCT00494754.

The DAO gene is associated with schizophrenia and interacts with other genes in the Taiwan Han Chinese population.

BACKGROUND: Schizophrenia is a highly heritable disease with a polygenic mode of inheritance. Many studies have contributed to our understanding of the genetic underpinnings of schizophrenia, but little is known about how interactions among genes affect the risk of schizophrenia. This study aimed to assess the associations and interactions among genes that confer vulnerability to schizophrenia and to examine the moderating effect of neuropsychological impairment. METHODS: We analyzed 99 SNPs from 10 candidate genes in 1,512 subject samples. The permutation-based single-locus, multi-locus association tests, and a gene-based multifactorial dimension reduction procedure were used to examine genetic associations and interactions to schizophrenia. RESULTS: We found that no single SNP was significantly associated with schizophrenia. However, a risk haplotype, namely A-T-C of the SNP triplet rsDAO7-rsDAO8-rsDAO13 of the DAO gene, was strongly associated with schizophrenia. Interaction analyses identified multiple between-gene and within-gene interactions. Between-gene interactions including DAO*DISC1 , DAO*NRG1 and DAO*RASD2 and a within-gene interaction for CACNG2 were found among schizophrenia subjects with severe sustained attention deficits, suggesting a modifying effect of impaired neuropsychological functioning. Other interactions such as the within-gene interaction of DAO and the between-gene interaction of DAO and PTK2B were consistently identified regardless of stratification by neuropsychological dysfunction. Importantly, except for the within-gene interaction of CACNG2, all of the identified risk haplotypes and interactions involved SNPs from DAO. CONCLUSIONS: These results suggest that DAO, which is involved in the N-methyl-d-aspartate receptor regulation, signaling and glutamate metabolism, is the master gene of the genetic associations and interactions underlying schizophrenia. Besides, the interaction between DAO and RASD2 has provided an insight in integrating the glutamate and dopamine hypotheses of schizophrenia.

Analysis of protein-protein interactions in cross-talk pathways reveals CRKL protein as a novel prognostic marker in hepatocellular carcinoma.

Deciphering the network of signaling pathways in cancer via protein-protein interactions (PPIs) at the cellular level is a promising approach but remains incomplete. We used an in situ proximity ligation assay to identify and quantify 67 endogenous PPIs among 21 interlinked pathways in two hepatocellular carcinoma (HCC) cells, Huh7 (minimally migratory cells) and Mahlavu (highly migratory cells). We then applied a differential network biology analysis and determined that the novel interaction, CRKL-FLT1, has a high centrality ranking, and the expression of this interaction is strongly correlated with the migratory ability of HCC and other cancer cell lines. Knockdown of CRKL and FLT1 in HCC cells leads to a decrease in cell migration via ERK signaling and the epithelial-mesenchymal transition process. Our immunohistochemical analysis shows high expression levels of the CRKL and CRKL-FLT1 pair that strongly correlate with reduced disease-free and overall survival in HCC patient samples, and a multivariate analysis further established CRKL and the CRKL-FLT1 as novel prognosis markers. This study demonstrated that functional exploration of a disease network with interlinked pathways via PPIs can be used to discover novel biomarkers.

Morus alba and active compound oxyresveratrol exert anti-inflammatory activity via inhibition of leukocyte migration involving MEK/ERK signaling.

BACKGROUND: Morus alba has long been used in traditional Chinese medicine to treat inflammatory diseases; however, the scientific basis for such usage and the mechanism of action are not well understood. This study investigated the action of M. alba on leukocyte migration, one key step in inflammation. METHODS: Gas chromatography-mass spectrometry (GC-MS) and cluster analyses of supercritical CO2 extracts of three Morus species were performed for chemotaxonomy-aided plant authentication. Phytochemistry and CXCR4-mediated chemotaxis assays were used to characterize the chemical and biological properties of M. alba and its active compound, oxyresveratrol. fluorescence-activated cell sorting (FACS) and Western blot analyses were conducted to determine the mode of action of oxyresveratrol. RESULTS: Chemotaxonomy was used to help authenticate M. alba. Chemotaxis-based isolation identified oxyresveratrol as an active component in M. alba. Phytochemical and chemotaxis assays showed that the crude extract, ethyl acetate fraction and oxyresveratrol from M. alba suppressed cell migration of Jurkat T cells in response to SDF-1. Mechanistic study indicated that oxyresveratrol diminished CXCR4-mediated T-cell migration via inhibition of the MEK/ERK signaling cascade. CONCLUSIONS: A combination of GC-MS and cluster analysis techniques are applicable for authentication of the Morus species. Anti-inflammatory benefits of M. alba and its active compound, oxyresveratrol, may involve the inhibition of CXCR-4-mediated chemotaxis and MEK/ERK pathway in T and other immune cells.

Development of a brief self-report questionnaire for screening putative pre-psychotic states.

BACKGROUND: Several self-report instruments were developed to capture psychotic prodrome, and were claimed to have good predictive validity. The feasibility of screening is questionable considering the heterogeneity of the targeted populations and the negative ramifications of false positive identification. This study developed a questionnaire using data covering a wide range of clinical characteristics. METHODS: One hundred and eleven putative pre-psychotic participants, 129 normal comparison subjects, and 95 non-psychotic psychiatric outpatients completed a 231-item questionnaire comprising a 110-item Wisconsin psychotic prone scale, 74-item schizotypal personality questionnaire, 33-item basic symptoms, and 14-item cognitive symptoms. Items showing the best discriminating power, estimated using chi-square statistics with Bonferroni correction, were extracted to create a brief version. A two-stage cut-off approach emphasizing specific items was applied to maximize sensitivity and specificity. The concurrent validity of the proposed approach was estimated using a ten-fold cross-validation procedure. RESULTS: A 15-item self-report questionnaire was developed. Respondents checking at least eight items, or those checking three to seven items including any of the three referring to feeling stress in crowds, aloofness, and perceptual disturbance, would be considered putatively pre-psychotic with the largest sensitivity+specificity (0.784+0.705=1.489). This cut-off selection was the best estimate by calculating 1000 permutations in the cross-validation procedure. CONCLUSIONS: This investigation proposes a different orientation for applying questionnaires to screen putative pre-psychotic states, with less emphasis on attenuated psychotic symptoms and predictive values. Besides providing a handy tool for increasing awareness and referral, the instructions of such a screening questionnaire should be carefully worded.

Recursive feature selection with significant variables of support vectors.

The development of DNA microarray makes researchers screen thousands of genes simultaneously and it also helps determine high- and low-expression level genes in normal and disease tissues. Selecting relevant genes for cancer classification is an important issue. Most of the gene selection methods use univariate ranking criteria and arbitrarily choose a threshold to choose genes. However, the parameter setting may not be compatible to the selected classification algorithms. In this paper, we propose a new gene selection method (SVM-t) based on the use of t-statistics embedded in support vector machine. We compared the performance to two similar SVM-based methods: SVM recursive feature elimination (SVMRFE) and recursive support vector machine (RSVM). The three methods were compared based on extensive simulation experiments and analyses of two published microarray datasets. In the simulation experiments, we found that the proposed method is more robust in selecting informative genes than SVMRFE and RSVM and capable to attain good classification performance when the variations of informative and noninformative genes are different. In the analysis of two microarray datasets, the proposed method yields better performance in identifying fewer genes with good prediction accuracy, compared to SVMRFE and RSVM.

Mixed sequence reader: a program for analyzing DNA sequences with heterozygous base calling.

The direct sequencing of PCR products generates heterozygous base-calling fluorescence chromatograms that are useful for identifying single-nucleotide polymorphisms (SNPs), insertion-deletions (indels), short tandem repeats (STRs), and paralogous genes. Indels and STRs can be easily detected using the currently available Indelligent or ShiftDetector programs, which do not search reference sequences. However, the detection of other genomic variants remains a challenge due to the lack of appropriate tools for heterozygous base-calling fluorescence chromatogram data analysis. In this study, we developed a free web-based program, Mixed Sequence Reader (MSR), which can directly analyze heterozygous base-calling fluorescence chromatogram data in .abi file format using comparisons with reference sequences. The heterozygous sequences are identified as two distinct sequences and aligned with reference sequences. Our results showed that MSR may be used to (i) physically locate indel and STR sequences and determine STR copy number by searching NCBI reference sequences; (ii) predict combinations of microsatellite patterns using the Federal Bureau of Investigation Combined DNA Index System (CODIS); (iii) determine human papilloma virus (HPV) genotypes by searching current viral databases in cases of double infections; (iv) estimate the copy number of paralogous genes, such as ß-defensin 4 (DEFB4) and its paralog HSPDP3.