Attention-based generative adversarial networks improve prognostic outcome prediction of cancer from multimodal data.

The prediction of prognostic outcome is critical for the development of efficient cancer therapeutics and potential personalized medicine. However, due to the heterogeneity and diversity of multimodal data of cancer, data integration and feature selection remain a challenge for prognostic outcome prediction. We proposed a deep learning method with generative adversarial network based on sequential channel-spatial attention modules (CSAM-GAN), a multimodal data integration and feature selection approach, for accomplishing prognostic stratification tasks in cancer. Sequential channel-spatial attention modules equipped with an encoder-decoder are applied for the input features of multimodal data to accurately refine selected features. A discriminator network was proposed to make the generator and discriminator learning in an adversarial way to accurately describe the complex heterogeneous information of multiple modal data. We conducted extensive experiments with various feature selection and classification methods and confirmed that the CSAM-GAN via the multilayer deep neural network (DNN) classifier outperformed these baseline methods on two different multimodal data sets with miRNA expression, mRNA expression and histopathological image data: lower-grade glioma and kidney renal clear cell carcinoma. The CSAM-GAN via the multilayer DNN classifier bridges the gap between heterogenous multimodal data and prognostic outcome prediction.

LIDER: cell embedding based deep neural network classifier for supervised cell type identification.

Background: Automatic cell type identification has been an urgent task for the rapid development of single-cell RNA-seq techniques. Generally, the current approach for cell type identification is to generate cell clusters by unsupervised clustering and later assign labels to each cell cluster with manual annotation. Methods: Here, we introduce LIDER (celL embeddIng based Deep nEural netwoRk classifier), a deep supervised learning method that combines cell embedding and deep neural network classifier for automatic cell type identification. Based on a stacked denoising autoencoder with a tailored and reconstructed loss function, LIDER identifies cell embedding and predicts cell types with a deep neural network classifier. LIDER was developed upon a stacked denoising autoencoder to learn encoder-decoder structures for identifying cell embedding. Results: LIDER accurately identifies cell types by using stacked denoising autoencoder. Benchmarking against state-of-the-art methods across eight types of single-cell data, LIDER achieves comparable or even superior enhancement performance. Moreover, LIDER suggests comparable robust to batch effects. Our results show a potential in deep supervised learning for automatic cell type identification of single-cell RNA-seq data. The LIDER codes are available at https://github.com/ShiMGLab/LIDER.

Prognostic outcome prediction by semi-supervised least squares classification.

Although great progress has been made in prognostic outcome prediction, small sample size remains a challenge in obtaining accurate and robust classifiers. We proposed the Rescaled linear square Regression based Least Squares Learning (RRLSL), a jointly developed semi-supervised feature selection and classifier, for predicting prognostic outcome of cancer patients. RRLSL used the least square regression to identify the scale factors and then rank the features in available multiple types of molecular data. We applied the unlabeled multiple molecular data in conjunction with the labeled data to develop a similarity graph. RRLSL produced the constraint with kernel functions to bridge the gap between label information and geometry information from messenger RNA and microRNA expression profiling. Importantly, this semi-supervised model proposed the least squares learning with L2 regularization to develop a semi-supervised classifier. RRLSL suggested the performance improvement in the prognostic outcome prediction and successfully discriminated between the recurrent patients and non-recurrent ones. We also demonstrated that RRLSL improved the accuracy and Area Under the Precision Recall Curve (AUPRC) as compared to the baseline semi-supervised methods. RRLSL is available for a stand-alone software package (https://github.com/ShiMGLab/RRLSL). A short abstract We proposed the Rescaled linear square Regression based Least Squares Learning (RRLSL), a jointly developed semi-supervised feature selection and classifier, for predicting prognostic outcome of cancer patients. RRLSL used the least square regression to identify the scale factors to rank the features in available multiple types of molecular data. RRLSL produced the constraint with kernel functions to bridge the gap between label information and geometry information from messenger RNA and microRNA expression profiling. Importantly, this semi-supervised model proposed the least squares learning with L2 regularization to develop the semi-supervised classifier. RRLSL suggested the performance improvement in the prognostic outcome prediction and successfully discriminated between the recurrent patients and non-recurrent ones.

Integration of Cancer Genomics Data for Tree-based Dimensionality Reduction and Cancer Outcome Prediction.

Accurate outcome prediction is crucial for precision medicine and personalized treatment of cancer. Researchers have found that multi-dimensional cancer omics studies outperform each data type (mRNA, microRNA, methylation or somatic copy number alteration) study in human disease research. Existing methods leveraging multiple level of molecular data often suffer from various limitations, e. g., heterogeneity, poor robustness or loss of generality. To overcome these limitations, we presented the tree-based dimensionality reduction approach for the identification of smooth tree graph and developed accurate predictive model for clinical outcome prediction. We demonstrated that 1) Discriminative Dimensionality Reduction via learning a Tree (DDRTree) achieved reduced dimension space tree with statistical significance; 2) Tree based support vector machine (SVM) classifier improved prediction performance of cancer recurrence as compared to t-test based SVM classifier; 3) Tree based SVM classifier was robust with regard to the different number of multi-markers; 4) Combining multiple omics data improved prediction performance of cancer recurrence as compared to a single-omics data; and 5) Tree based SVM classifier achieved similar or better prediction performance when compared to the features from state-of-the-art feature selection methods. Our results demonstrated great potential of the tree-based dimensionality reduction approach based clinical outcome prediction.

Development and validation of GMI signature based random survival forest prognosis model to predict clinical outcome in acute myeloid leukemia.

BACKGROUND: Acute myeloid leukemia (AML) is a disease with marked molecular heterogeneity and a high early death rate. Our aim was to investigate an integrated Gene expression, Mirna and miRNA-mRNA Interactions (GMI) signature for improving risk stratification of AML. METHODS: We identified differentially expressed genes by pooling a large number of 861 human AML patients and 75 normal cases. We then used miRWalk to identify the functional miRNA-mRNA regulatory module. The GMI signature based random survival forest (RSF) prognosis model was developed from training data set and evaluated in independent patient cohorts from The Cancer Genome Atlas (TCGA) dataset (N = 147). Univariate and multivariate Cox proportional hazards regression analyses were applied to evaluate the prognostic value of GMI signature. RESULTS: We identified 139 differentially expressed genes between normal and abnormal AML samples. We discovered the functional miRNA-mRNA regulatory module which participate in the network of cancer progression. We named 23 differentially expressed genes and 16 validated target miRNAs as the GMI signature. The RSF model-based scores separated independent patient cohorts into two groups with significantly different overall survival (C-index = 0.59, hazard ratio [HR], 2.12; 95% confidence interval [CI], 1.11-4.03; p = 0.019). Similar results were obtained with reversed training and testing datasets (C-index = 0.58, hazard ratio [HR], 2.08; 95% confidence interval [CI], 1.02-4.24; p = 0.038). The GMI signature score contributed more information about recurrence than standard clinical covariates. CONCLUSION: The GMI signature based RSF prognosis model not only reflects regulatory relationships from identified miRNA-mRNA module but also informs patient prognosis. While in the TCGA data set the GMI signature score contributed additional information about recurrence in comparison to standard clinical covariates, further studies are needed to determine its clinical significance.

Group Analysis of Q Values Calculated with Tangential Radius of Curvature from Human Anterior Corneal Surface.

OBJECTIVE: To calculate the Q values from the human anterior corneal surface with the tangential radius of curvature and analyze its distribution characteristics in different age and refractive status groups. METHODS: Tangential power maps of the anterior cornea from Orbscan II were acquired for 201 subjects' right eyes. They were divided into groups of adults and children and then divided further into subgroups according to the refraction status. The Q values of each semimeridian were calculated by the tangential radius with a linear regression equation. The Q value distribution in both the nasal cornea and temporal cornea were analyzed. RESULTS: The mean temporal Q values of the emmetropia group of adults and all children's groups were significantly different from the mean nasal Q value. The mean nasal corneal Q values were more negative in children. The adult group showed differences only in the low myopia group. The mean Q value of the nasal cornea among different refractive groups of children was significantly different, and so was the temporal cornea between the adult myopia and emmetropia group. CONCLUSION: The method using the tangential radius of curvature combined with linear regression to obtain anterior surface Q values for both adults and children was stable and reliable. When we analyzed the anterior corneal Q value, area division was necessary.

Spectral clustering using Nyström approximation for the accurate identification of cancer molecular subtypes.

A major challenge in clinical cancer research is the identification of accurate molecular subtype. While unsupervised clustering methods have been applied for class discovery, this clustering method remains a bottleneck in developing accurate method for molecular subtype discovery. In this analysis, we hypothesize that spectral clustering method could identify molecular subtypes in correlation with survival outcomes. We propose an accurate subtype identification method, Cancer Subtype Identification with Spectral Clustering using Nyström approximation (CSISCN), for the discovery of molecular subtypes, based on spectral clustering method. CSISCN could be used to improve gene expression-based identification of breast cancer molecular subtypes. We demonstrated that CSISCN identified the molecular subtypes with distinct clinical outcomes and was valid for the number of molecular subtypes. Furthermore, CSISCN identified molecular subtypes for improving clinical and molecular relevance which significantly outperformed consensus clustering and spectral clustering methods. To test the general applicability of the CSISCN, we further applied it on human CRC datasets and AML datasets and demonstrated superior performance as compared to consensus clustering method. In summary, CSISCN demonstrated the great potential in gene expression-based subtype identification.

ColoFinder: a prognostic 9-gene signature improves prognosis for 871 stage II and III colorectal cancer patients.

Colorectal cancer (CRC) is a heterogeneous disease with a high mortality rate and is still lacking an effective treatment. Our goal is to develop a robust prognosis model for predicting the prognosis in CRC patients. In this study, 871 stage II and III CRC samples were collected from six gene expression profilings. ColoFinder was developed using a 9-gene signature based Random Survival Forest (RSF) prognosis model. The 9-gene signature recurrence score was derived with a 5-fold cross validation to test the association with relapse-free survival, and the value of AUC was gained with 0.87 in GSE39582(95% CI [0.83-0.91]). The low-risk group had a significantly better relapse-free survival (HR, 14.8; 95% CI [8.17-26.8]; P < 0.001) than the high-risk group. We also found that the 9-gene signature recurrence score contributed more information about recurrence than standard clinical and pathological variables in univariate and multivariate Cox analyses when applied to GSE17536(p = 0.03 and p = 0.01 respectively). Furthermore, ColoFinder improved the predictive ability and better stratified the risk subgroups when applied to CRC gene expression datasets GSE14333, GSE17537, GSE12945and GSE24551. In summary, ColoFinder significantly improves the risk assessment in stage II and III CRC patients. The 9-gene prognostic classifier informs patient prognosis and treatment response.

SNRFCB: sub-network based random forest classifier for predicting chemotherapy benefit on survival for cancer treatment.

Adjuvant chemotherapy (CTX) should be individualized to provide potential survival benefit and avoid potential harm to cancer patients. Our goal was to establish a computational approach for making personalized estimates of the survival benefit from adjuvant CTX. We developed Sub-Network based Random Forest classifier for predicting Chemotherapy Benefit (SNRFCB) based gene expression datasets of lung cancer. The SNRFCB approach was then validated in independent test cohorts for identifying chemotherapy responder cohorts and chemotherapy non-responder cohorts. SNRFCB involved the pre-selection of gene sub-network signatures based on the mutations and on protein-protein interaction data as well as the application of the random forest algorithm to gene expression datasets. Adjuvant CTX was significantly associated with the prolonged overall survival of lung cancer patients in the chemotherapy responder group (P = 0.008), but it was not beneficial to patients in the chemotherapy non-responder group (P = 0.657). Adjuvant CTX was significantly associated with the prolonged overall survival of lung cancer squamous cell carcinoma (SQCC) subtype patients in the chemotherapy responder cohorts (P = 0.024), but it was not beneficial to patients in the chemotherapy non-responder cohorts (P = 0.383). SNRFCB improved prediction performance as compared to the machine learning method, support vector machine (SVM). To test the general applicability of the predictive model, we further applied the SNRFCB approach to human breast cancer datasets and also observed superior performance. SNRFCB could provide recurrent probability for individual patients and identify which patients may benefit from adjuvant CTX in clinical trials.

Network-based sub-network signatures unveil the potential for acute myeloid leukemia therapy.

Although gene expression profiling studies of acute myeloid leukemia (AML) patients have provided key insights into potential diagnostic and prognostic markers and therapeutic targets, it is not clear that the patterns of molecular heterogeneity affect the tumor biology and respond to the treatment. We hypothesized that network-based gene expression signatures of AML represent the mechanistically important genes and may improve the predicted performance of prognosis and clinical outcome. We provided the random walk with restart (RWR) analysis to discover the sub-network of genomic alterations. The RWR approach integrates the signature genes derived from the random forest (RF) analysis as "seeds" to identify genes critical to the AML recurrence phenotype. To test whether the 81-gene biomarkers could predict AML recurrence, we developed Survival Support Vector Machine (SSVM) models using a gene expression dataset and test on an independent dataset. The random forest classifier was built based on 81-gene biomarkers to separate the AML patients into "recurrence" and "non-recurrence" groups. The 81-gene biomarkers showed significant enrichment related to cancer pathophysiology and provided good coverage of sub-network biomarkers and AML-related signaling pathways. The SSVM-based score was significantly associated with overall survival (hazard ratio [HR], 2.16; 95% confidence interval [CI], 1.18-3.97; p = 0.01). Similar results were obtained with reversed training and testing datasets (hazard ratio [HR], 1.6; 95% confidence interval [CI], 1.08-2.37; p = 0.02). The 81-gene biomarker based RF classifier improved classification performance. Overall, 81-gene biomarkers might be useful prognostic and predictive molecular markers to predict the clinical outcome of AML patients.

Three-dimensional model for human anterior corneal surface.

The anterior corneal asphericity (Q) with the tangential radius is calculated, and a three-dimensional (3-D) anterior corneal model is constructed. Tangential power maps from Orbscan II are acquired for 66 young adult subjects. The Q-value of each semimeridian in the near-horizontal region is calculated with the tangential radius. Polynomial fitting is used to model the 360-semimeridional variation of Q-values, and to fit the Q-values in the near-vertical region. Furthermore, a customized 3-D anterior corneal model is constructed. The 360-semimeridional variation of Q-values is well fitted with a seventh-degree polynomial function for all subjects. The goodness of fit of the polynomial function was >0.9, and the median value was 0.94. The Q-value distribution of the anterior corneal surface showed bimodal variation. Additionally, the Q-values gradually become less negative from the horizontal to the vertical semimeridians in the four quadrants. The 3-D surface plot of the anterior corneal surface approximated a prolate ellipsoid. Using a method to calculate the Q-value with the tangential radius combined with polynomial fitting, we are able to obtain the Q-value of any semimeridian. Compared with general models, this method generates a complete shape of the anterior corneal surface using asphericity.

Anterior corneal asphericity calculated by the tangential radius of curvature.

We propose a method of calculating the corneal asphericity (Q) and analyze the characteristics of the anterior corneal shape using the tangential radius. Fifty-eight right eyes of 58 subjects were evaluated using the Orbscan II corneal topographer. The Q-values of the flat principal semi-meridians calculated by the sagittal radius were compared to those by the tangential radius. Variation in the Q-value with semi-meridian in the nasal and temporal cornea calculated by the tangential radius was analyzed. There were significant differences in Q-values (P<0.001) between the two methods. The mean Q-values of the flat principal semi-meridians calculated by tangential radius with -0.33 ± 0.10 in the nasal and -0.22 ± 0.12 in the temporal showed more negative than the corresponding Q-values calculated by the sagittal radius. The Q-values calculated by tangential radius became less negative gradually from horizontal semi-meridians to oblique semi-meridians in both nasal and temporal cornea. Variation in Q-value with semi-meridian was more obvious in the nasal cornea. The method of calculating corneal Q using the tangential radius could provide more reasonable and complete Q-value than that by the sagittal radius. The model of a whole anterior corneal surface could be reconstructed on the basis of the above method.

A network-based gene expression signature informs prognosis and treatment for colorectal cancer patients.

BACKGROUND: Several studies have reported gene expression signatures that predict recurrence risk in stage II and III colorectal cancer (CRC) patients with minimal gene membership overlap and undefined biological relevance. The goal of this study was to investigate biological themes underlying these signatures, to infer genes of potential mechanistic importance to the CRC recurrence phenotype and to test whether accurate prognostic models can be developed using mechanistically important genes. METHODS AND FINDINGS: We investigated eight published CRC gene expression signatures and found no functional convergence in Gene Ontology enrichment analysis. Using a random walk-based approach, we integrated these signatures and publicly available somatic mutation data on a protein-protein interaction network and inferred 487 genes that were plausible candidate molecular underpinnings for the CRC recurrence phenotype. We named the list of 487 genes a NEM signature because it integrated information from Network, Expression, and Mutation. The signature showed significant enrichment in four biological processes closely related to cancer pathophysiology and provided good coverage of known oncogenes, tumor suppressors, and CRC-related signaling pathways. A NEM signature-based Survival Support Vector Machine prognostic model was trained using a microarray gene expression dataset and tested on an independent dataset. The model-based scores showed a 75.7% concordance with the real survival data and separated patients into two groups with significantly different relapse-free survival (p = 0.002). Similar results were obtained with reversed training and testing datasets (p = 0.007). Furthermore, adjuvant chemotherapy was significantly associated with prolonged survival of the high-risk patients (p = 0.006), but not beneficial to the low-risk patients (p = 0.491). CONCLUSIONS: The NEM signature not only reflects CRC biology but also informs patient prognosis and treatment response. Thus, the network-based data integration method provides a convergence between biological relevance and clinical usefulness in gene signature development.

Semi-supervised learning improves gene expression-based prediction of cancer recurrence.

MOTIVATION: Gene expression profiling has shown great potential in outcome prediction for different types of cancers. Nevertheless, small sample size remains a bottleneck in obtaining robust and accurate classifiers. Traditional supervised learning techniques can only work with labeled data. Consequently, a large number of microarray data that do not have sufficient follow-up information are disregarded. To fully leverage all of the precious data in public databases, we turned to a semi-supervised learning technique, low density separation (LDS). RESULTS: Using a clinically important question of predicting recurrence risk in colorectal cancer patients, we demonstrated that (i) semi-supervised classification improved prediction accuracy as compared with the state of the art supervised method SVM, (ii) performance gain increased with the number of unlabeled samples, (iii) unlabeled data from different institutes could be employed after appropriate processing and (iv) the LDS method is robust with regard to the number of input features. To test the general applicability of this semi-supervised method, we further applied LDS on human breast cancer datasets and also observed superior performance. Our results demonstrated great potential of semi-supervised learning in gene expression-based outcome prediction for cancer patients. CONTACT: bing.zhang@vanderbilt.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

[Medpor plus titanic mesh implant in the repair of orbital blowout fractures].

OBJECTIVE: To study the efficacy of porous polyethylene (Medpor) plus titanic mesh sheets in the repair of orbital blowout fractures. METHODS: A total of 20 patients underwent open surgical reduction with the combined usage of Medpor and titanic mesh. And they were followed up for average period of 14.5 months (range: 9 - 18). RESULTS: There is no infection or extrusion of medpor and titanic mesh in follow-up periods. There was no instance of decreased visual acuity at post-operation. And all cases of enophthalmos were corrected. The post-operative protrusion degree of both eyes was almost identical at less than 2 mm. The movement of eye balls was satisfactory in all directions. Diplopia disappeared in 18 cases with a cure rate of 90%, 1 case improved and 1 case persisted. CONCLUSION: Medpor plus titanic mesh implant is a safe and effective treatment in the repair of orbital blow out fractures.

[Orbscan II-assisted intraocular lens power calculation after LASIK].

OBJECTIVE: To evaluate the accuracy of keratometric value derived from one zone of Orbscan II mean power map after Laser in situ keratomileusis (LASIK) in combination with Holladay IOL Consultant software to calculate the intraocular lens (IOL) power. METHODS: A two-part study was conducted at a referral practice. Part 1 was a prospective study of 118 eyes undergoing LASIK. The changes in Orbscan II mean power maps at four central zones (1.5, 2.0, 2.5 and 3.0 mm) were compared with the cornea power calculated from pre-LASIK data to determine the optimum Orbscan II correlation zone. In Part 2, the power of optimum measured by Orbscan II after LASIK was applied to IOL calculations for 62 eyes undergoing LASIK. And the results were compared with the IOL power calculated by the pre-LASIK data. RESULTS: (1) An analysis at the Orbscan II 1.5 mm measurement zone demonstrated an underestimation of net cornea power after LASIK while the 3.0 mm zones demonstrated an overestimation. The 2.0 mm and 2.5 mm zones best approximated the net cornea power calculated from pre-LASIK data; (2) The cornea power at 2.5 mm from Orbscan II was selected for IOL calculations in combination with Holladay IOL Consultant software Holladay II and HofferQ formula. The refractive error calculated by Holladay II and HofferQ formula were (0.47 ± 0.75) D and (0.52 ± 0.83) D versus the IOL power calculated by clinical history method. The refractive errors of two formula within ± 0.50 D were 48.4% and 43.5% and within ± 1.0 D 80.6% and 74.2%. CONCLUSIONS: The cornea power from 2.5 mm Orbscan II zone after LASIK in combination with Holladay II or HofferQ formula can accurately predict the IOL power for cataract surgery.

[Estimation of preoperative corneal power in patients with myopic LASIK based on postoperative corneal data measured from Orbscan II].

OBJECTIVE: Compare the change of pre- and post-operative posterior corneal curvature measurements in peripheral fitting zones using the Orbscan II topographer in patients undergoing myopic laser in situ keratomileusis (LASIK). To establish a new ratio of the anterior and posterior corneal curvature for the calculation of preoperative total corneal power using postoperative corneal data alone in patients after LASIK. METHODS: Retrospectively analyzed the changes of posterior corneal curvature in peripheral 7 to 10 mm fitting zones in 151 right eyes (151 individuals) 3 month after LASIK as measured by Orbscan II. The new ratio between the anterior and posterior corneal curvature was measured. The new ratio was used to estimate pre-operative total corneal refractive power in 30 eyes. The estimated total corneal refractive power was compared with the value measured from Orbscan II preoperatively. The accuracy of intraocular lens power estimated in 2 cases underwent cataract surgery after corneal refractive surgery was studied. RESULTS: The mean difference in estimated pre- and post-operative power of the posterior cornea was (-0.005 ± 0.154) mm (t = 0.417, P = 0.677). The ratio between the preoperative anterior central (0 - 7 mm) corneal curvature and the posterior peripheral (7 to 10 mm) corneal curvature was 1.167 ± 0.030. Estimated mean pre-operative total corneal power was (43.49 ± 1.79) D, whereas the value measuring from Orbscan II preoperatively was (43.77 ± 1.53) D. The mean difference between these two measurements was (-0.28 ± 1.00) D (t = -1.523, P = 0.139). The spherical equivalent refraction after cataract extraction of the two cases undertaken cataract surgery after corneal refractive surgery was 0.54 D and 0.69 D, respectively. CONCLUSIONS: The difference in posterior corneal curvature measurement following myopic LASIK using the peripheral fitting zone with the Orbscan II, as compared to the preoperative values, is clinically insignificant. The pre-LASIK corneal power can be estimated using post-LASIK data together with the new calculated ratio. This will be useful in post-LASIK patients requiring cataract surgery but without the availability of pre-LASIK corneal data for the estimation of the intraocular lens power.

Predicting protein-protein interactions from sequence using correlation coefficient and high-quality interaction dataset.

Identifying protein-protein interactions (PPIs) is critical for understanding the cellular function of the proteins and the machinery of a proteome. Data of PPIs derived from high-throughput technologies are often incomplete and noisy. Therefore, it is important to develop computational methods and high-quality interaction dataset for predicting PPIs. A sequence-based method is proposed by combining correlation coefficient (CC) transformation and support vector machine (SVM). CC transformation not only adequately considers the neighboring effect of protein sequence but describes the level of CC between two protein sequences. A gold standard positives (interacting) dataset MIPS Core and a gold standard negatives (non-interacting) dataset GO-NEG of yeast Saccharomyces cerevisiae were mined to objectively evaluate the above method and attenuate the bias. The SVM model combined with CC transformation yielded the best performance with a high accuracy of 87.94% using gold standard positives and gold standard negatives datasets. The source code of MATLAB and the datasets are available on request under smgsmg@mail.ustc.edu.cn.

TGFB1 as a susceptibility gene for high myopia: a replication study with new findings.

OBJECTIVE: To investigate the genetic association between transforming growth factor beta1 (TGFB1) gene polymorphisms and high myopia in a Chinese population. METHODS: Six hundred adults were recruited for this case-control study, including 300 subjects with high myopia (-8.0 diopters or worse) and 300 control subjects (within +/-1.0 diopters). Seven tag single-nucleotide polymorphisms (SNPs) and 1 coding SNP were genotyped. Their frequencies were compared between cases and controls by statistical tests. RESULTS: Four SNPs in the 5' half of the gene showed significant differences in allele and genotype frequencies between cases and controls. The results remained significant after correction for multiple comparisons. The previously reported association of the coding SNP rs1800470 with high myopia was successfully replicated. The tag SNP rs4803455 in intron 2 was found to account for the positive results of the other 3 SNPs by stepwise logistic regression. The minor allele T of rs4803455 was protective against high myopia with an odds ratio of 0.67 (95% confidence interval, 0.53-0.86; P= .001). CONCLUSION: TGFB1 is a myopia susceptibility gene. CLINICAL RELEVANCE: TGFB1 is the first myopia susceptibility gene successfully replicated. The functional significance of rs4803455 or the genuine causative SNPs in linkage disequilibrium with it remains to be determined.

A protein interaction network analysis for yeast integral membrane protein.

Although the yeast Saccharomyces cerevisiae is the best exemplified single-celled eukaryote, the vast number of protein-protein interactions of integral membrane proteins of Saccharomyces cerevisiae have not been characterized by experiments. Here, based on the kernel method of Greedy Kernel Principal Component analysis plus Linear Discriminant Analysis, we identify 300 protein-protein interactions involving 189 membrane proteins and get the outcome of a highly connected protein-protein interactions network. Furthermore, we study the global topological features of integral membrane proteins network of Saccharomyces cerevisiae. These results give the comprehensive description of protein-protein interactions of integral membrane proteins and reveal global topological and robustness of the interactome network at a system level. This work represents an important step towards a comprehensive understanding of yeast protein interactions.