Heterogeneity Of Defects Research Articles

Boosted Relief Feature Subset Selection and Heterogeneous Cross Project Defect Prediction using Firefly Particle Swarm Optimization

The exponential growth in the field of information technology, need for quality-based software development is highly demanded. The important factor to be focused during the software development is software defect detection in earlier stages. Failure to detect hidden faults will affect the effectiveness and quality of the software usage and its maintenance. In traditional software defect prediction models, projects with same metrics are involved in prediction process. In recent years, active topic is dealing with Cross Project Defect Prediction (CPDP) to predict defects on software project from other software projects dataset. Still, traditional cross project defect prediction approaches also require common metrics among the dataset of two projects for constructing the defect prediction techniques. Suppose if cross project dataset with different metrics has to be used for defect prediction then these methods become infeasible. To overcome the issues in software defect prediction using Heterogeneous cross projects dataset, this paper introduced a Boosted Relief Feature Subset Selection (BRFSS) to handle the two different projects with Heterogeneous feature sets. BRFSS employs the mapping approach to embed the data from two different domains into a comparable feature space with a lower dimension. Based on the similarity measure the difference among the mapped domains of dataset are used for prediction process. This work used five different software groups with six different datasets to perform heterogeneous cross project defect prediction using firefly particle swarm optimization. To produce optimal defect prediction in the Heterogeneous environment, the knowledge of particle swarm optimization by inducing firefly algorithm. The simulation result is compared with other standard models, the outcome of the result proved the efficiency of the prediction process while using firefly enabled particle swarm optimization.

An Empirical Study on Heterogeneous Defect Prediction Approaches

Software defect prediction has always been a hot research topic in the field of software engineering owing to its capability of allocating limited resources reasonably. Compared with cross-project defect prediction (CPDP), heterogeneous defect prediction (HDP) further relaxes the limitation of defect data used for prediction, permitting different metric sets to be contained in the source and target projects. However, there is still a lack of a holistic understanding of existing HDP studies due to different evaluation strategies and experimental settings. In this paper, we provide an empirical study on HDP approaches. We review the research status systematically and compare the HDP approaches proposed from 2014 to June 2018. Furthermore, we also investigate the feasibility of HDP approaches in CPDP. Through extensive experiments on 30 projects from five datasets, we have the following findings: (1) metric transformation-based HDP approaches usually result in better prediction effects, while metric selection-based approaches have better interpretability. Overall, the HDP approach proposed by Li <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> (CTKCCA) currently has the best performance. (2) Handling class imbalance problems can boost the prediction effects, but the improvements are usually limited. In addition, utilizing mixed project data cannot improve the performance of HDP approaches consistently since the label information in the target project is not used effectively. (3) HDP approaches are feasible for cross-project defect prediction in which the source and target projects have the same metric set.

Heterogeneous Defect Prediction Based on Transfer Learning to Handle Extreme Imbalance

Software systems are now ubiquitous and are used every day for automation purposes in personal and enterprise applications; they are also essential to many safety-critical and mission-critical systems, e.g., air traffic control systems, autonomous cars, and Supervisory Control And Data Acquisition (SCADA) systems. With the availability of massive storage capabilities, high speed Internet, and the advent of Internet of Things devices, modern software systems are growing in both size and complexity. Maintaining a high quality of such complex systems while manually keeping the error rate at a minimum is a challenge. This paper proposed a heterogeneous defect prediction method considering class extreme imbalance problem in real software datasets. In the first stage, Sampling with the Majority method (SWIM) based on Mahalanobis Distance is used to balance the dataset to reduce the influence of minority samples in defect data. Due to the negative impact of uncorrelated features on the classification algorithm, the second stage uses ensemble learning and joint similarity measurement to select the most relevant and representative features between the source project and the target project. The third phase realizes the transfer learning from the source project to the target project in the Grassmann manifold space. Our experiments, conducted using nine projects of three public domain software defect libraries and compared with four existing advanced methods to verify the effectiveness of the proposed method in this paper. The experimental results indicate that the proposed method is more accurate in terms of Area under curve (AUC).

Few-Shot Learning Based Balanced Distribution Adaptation for Heterogeneous Defect Prediction

Heterogeneous defect prediction (HDP) aims to predict the defect tendency of modules in one project using heterogeneous data collected from other projects. It sufficiently incorporates the two characteristics of the defect prediction data: (1) datasets could have different metrics and distribution, and (2) data could be highly imbalanced. In this paper, we propose a few-shot learning based balanced distribution adaptation (FSLBDA) approach for heterogeneous defect prediction, which takes into consideration the two characteristics of the defect prediction data. Class imbalance of the defect datasets can be solved with undersampling, but the scale of the training datasets will be smaller. Specifically, we first remove redundant metrics of datasets with extreme gradient boosting. Then, we reduce the data difference between the source domain and the target domain with the balanced distribution adaptation. It considers the marginal distribution and the probability of conditional distribution differences and adaptively assigns different weights to them. Finally, we use adaptive boosting to relieve the influence caused by the size of the training dataset is smaller, which can improve the accuracy of the defect prediction model. We conduct experiments on 17 projects from 4 datasets using 3 indicators (i.e., AUC, G-mean, F-measure). Compared to three classic approaches, the experimental results show that FSLBDA can effectively improve the prediction performance.

Conditional Domain Adversarial Adaptation for Heterogeneous Defect Prediction

Heterogeneous defect prediction (HDP) has become a very active research field in software engineering, which predicts the maximum number of bug-suspiciousness modules of a target project by prediction models built on source project with heterogeneous metric set. At present, some researchers have proposed some HDP models with a promising performance. Most of existing HDP models adopted unsupervised transfer learning to map source project and target project into the same feature space, which only considered the metrics space, not the label information from source project and few part of target project. Meanwhile, the predictive ability of these HDP models in effort-aware context have not been compared. Therefore, we set up to investigate the effectiveness of label information on HDP, and to propose a HDP model for improving the predicting performance in classification and effort-aware contexts. In order to use these label information, we propose a novel conditional domain adversarial adaptation (CDAA) approach to tackle heterogeneous problem in SDP, which is motivated by generative adversarial networks (GANs). There are three networks in architecture of our CDAA, including one generator, one discriminator and one classifier. The generator learns how to transfer source instance space to target instance space. The discriminator learns how to identify the fake instances generated by generator. The classifier learns how to correctly classify the label of instances. In our CDAA, the loss function of classifier and discriminator are both back propagate to generator. Then, to ensure a fair comparison between state-of-the art methods and CDAA, we take AUC, MCC and $P_{opt}$ as measures to evaluate 28 open-source projects. Experimental results demonstrate that CDAA method could take advantage of label information to effectively map source project to target project and improve the predictive performance. Also, experimental results demonstrate that our CDAA method is not affected by the number of same metrics between source project and target project.

Re: Prostate-specific Membrane Antigen Heterogeneity and DNA Repair Defects in Prostate Cancer.

Re: Prostate-specific Membrane Antigen Heterogeneity and DNA Repair Defects in Prostate Cancer.

Congenital factor XI deficiency, complete genotype and phenotype of two Iranian families.

: Congenital factor XI (FXI) deficiency is a mild trauma-related bleeding disorder with estimated worldwide prevalence of one per 1 million. The disorder is less frequent in Iran and a few studies have been performed on Iranian patients. In the current study, we assessed molecular, laboratory and clinical features of two Iranian patients with congenital FXI deficiency and their families. Clinical features and demographic data of the patients were assessed by the physician and a staff member trained specifically to deal with patients with bleeding disorders. FXI activity and antigen assays were performed for seven members of the two families and genotyping was performed by direct sequencing of all F11 gene exons and intron-exon boundaries as well as the untranslated regions. Five members of the two families were affected by FXI deficiency. Both patients experienced prolonged epistaxis, whereas other family members were asymptomatic. Two gene defects were observed in the patients and their families. Two disease-causing mutations were c.943G>A (p.Glu315Lys) missense and the four-nucleotide deletion (g.27849-27852del) in exon 15. The gene deletion was observed in homozygote state in the patient with severe FXI deficiency (FXI activity <1%) and heterozygote state in the parent, whereas the c.943G>A mutation was detected in heterozygote state and was accompanied by epistaxis in the patient. FXI deficiency is a mild bleeding disorder that is caused by heterogeneous molecular defects.

Multivalvular Disease: Percutaneous Management in 2019 and Beyond.

Patients with multivalvular disease (MVD) are common and often present with heterogeneous valve defects. Evaluation is complicated by interactions among various valve pathologies. Trials and guidelines focus primarily on single-valve disease, providing few recommendations for the treatment of patients with MVD. This article provides an insight into percutaneous treatment possibilities for this heterogeneous patient population.

Heterogeneous cross-project defect prediction with multiple source projects based on transfer learning.

Cross-project defect prediction (CPDP) aims to predict the defect proneness of target project with the defect data of source project. Existing CPDP methods are based on the assumption that source and target projects should have the same metrics. Heterogeneous cross-project defect prediction (HCPDP) builds a prediction model using heterogeneous source and target projects. Existing HCPDP methods just focus on one source project or multiple source projects with the same metrics. These methods limit the scope of getting the source project. In this paper, we propose Heterogeneous Defect Prediction with Multiple source projects (HDPM) which can use multiple heterogeneous source projects for defect prediction. HDPM based on transfer learning which can learn knowledge from one domain and use it to help with other domain. HDPM constructs a projective matrix between heterogeneous source and target projects to make the distributions of source and target projects similar. We conduct experiments on 14 projects from four public datasets and the results show that HDPM can achieve better performance compared with existing CPDP methods, and outperforms or is comparable to within-project defect prediction method. The use of multiple heterogeneous source projects for defect prediction can effectively extend the data acquisition range of defect prediction and make software defect prediction better applied to software engineering.

1524. Presentation of Acute Focal Bacterial Nephritis in Children

BackgroundAscending infections are thought as the main route of infection in acute pyelonephritis (APN). However, among patients diagnosed with APN, a subset has atypical presentation unexplainable by ascending routes of infections. This study aimed to review 8 cases initially diagnosed as APN, however presenting with no or minimal pyuria and presenting as acute focal bacterial nephritis (AFBN).MethodsThis was a retrospective cohort study of children <18 years old without underlying diseases, treated for their first episode of APN during 2006 and 2016. Those that fit the following inclusion criteria were diagnosed as AFBN: (1) characteristic CT findings compatible AFBS, (2) atypical presentation of APN including lack of urinary symptoms, and (3) no antibiotic administration prior to urine culture. Electronic medical records were analyzed, and radiologic images re-evaluated.ResultsDuring the 11-year period, of the total of 359 patients were diagnosed with the first episode of APN. Of these, 8 were re-diagnosed as AFBN. The mean age was 9.8 years old (1.9–17.4). Abdominal pain with nausea and vomiting were chief complaints (62.5%), and none had urinary symptoms including dysuria, incontinence, or increased frequency. Initial urinalysis in all the patients showed WBC <10–19 hpf, and were negative for nitrite. High initial WBC and CRP levels were observed (median 17.892 × 103/mm3 and 13.1 mg/dL, respectively). Cultures were positive for E. coli in 3 cases. CT findings showed nephromegaly with multifocal wedge-shaped heterogeneous enhancement defects corresponding to blood supply patterns of renal segmental arteries, no hydronephrosis, and no inflammation in the pelviureteric calyceal system. A mean duration of fever was 6.6 days, and time to defervescence after initiation of antibiotics was an average of 3 days.ConclusionLack of urinary symptoms, insignificant pyuria, and CT findings of AFBN support the possibility of an alternative route of infection, other than the ascending route.Disclosures All authors: No reported disclosures.

Heterogeneous structural defects to prompt charge shuttle in g-C3N4 plane for boosting visible-light photocatalytic activity

Structural defect engineering toward g-C3N4 plane usually has great benefit on modulating electron structure and photocatalytic performance. Here, we report a porous g-C3N4 material with heterogeneous structural defects, oxygen atom replacing edge nitrogen and cyano group, obtained via a facile treatment method. The reconstructed material shows narrowing band gap, high light absorption and fast charge separation. Theoretical calculation discloses that the doped oxygen atom and the nearby atoms accept electrons as reduction site to produce hydrogen, while the undoped fraction and cyano group take the duty to oxidize water. The delocalization of reactive sites drives charge shuttle on the plane, limiting recombination of charge carriers. Consequently, the modified g-C3N4 shows excellent photocatalytic activity with apparent quantum efficiency of 8.41% under 420 nm wavelength, surpassing pure g-C3N4 and other reported materials with defect compilation. We think that this work provides a new avenue to understand the function of structural defect on prompt charge separation.

Prostate-specific Membrane Antigen Heterogeneity and DNA Repair Defects in Prostate Cancer

BackgroundProstate-specific membrane antigen (PSMA; folate hydrolase) prostate cancer (PC) expression has theranostic utility. ObjectiveTo elucidate PC PSMA expression and associate this with defective DNA damage repair (DDR). Design, setting, and participantsMembranous PSMA (mPSMA) expression was scored immunohistochemically from metastatic castration-resistant PC (mCRPC) and matching, same-patient, diagnostic biopsies, and correlated with next-generation sequencing (NGS) and clinical outcome data. Outcome measurements and statistical analysisExpression of mPSMA was quantitated by modified H-score. Patient DNA was tested by NGS. Gene expression and activity scores were determined from mCRPC transcriptomes. Statistical correlations utilised Wilcoxon signed rank tests, survival was estimated by Kaplan-Meier test, and sample heterogeneity was quantified by Shannon's diversity index. Results and limitationsExpression of mPSMA at diagnosis was associated with higher Gleason grade (p=0.04) and worse overall survival (p=0.006). Overall, mPSMA expression levels increased at mCRPC (median H-score [interquartile range]: castration-sensitive prostate cancer [CSPC] 17.5 [0.0–60.0] vs mCRPC 55.0 [2.8–117.5]). Surprisingly, 42% (n=16) of CSPC and 27% (n=16) of mCRPC tissues sampled had no detectable mPSMA (H-score <10). Marked intratumour heterogeneity of mPSMA expression, with foci containing no detectable PSMA, was observed in all mPSMA expressing CSPC (100%) and 37 (84%) mCRPC biopsies. Heterogeneous intrapatient mPSMA expression between metastases was also observed, with the lowest expression in liver metastases. Tumours with DDR had higher mPSMA expression (p=0.016; 87.5 [25.0–247.5] vs 20 [0.3–98.8]; difference in medians 60 [5.0–95.0]); validation cohort studies confirmed higher mPSMA expression in patients with deleterious aberrations in BRCA2 (p<0.001; median H-score: 300 [165–300]; difference in medians 195.0 [100.0–270.0]) and ATM (p=0.005; 212.5 [136.3–300]; difference in medians 140.0 [55.0–200]) than in molecularly unselected mCRPC biopsies (55.0 [2.75–117.5]). Validation studies using mCRPC transcriptomes corroborated these findings, also indicating that SOX2 high tumours have low PSMA expression. ConclusionsMembranous PSMA expression is upregulated in some but not all PCs, with mPSMA expression demonstrating marked inter- and intrapatient heterogeneity. DDR aberrations are associated with higher mPSMA expression and merit further evaluation as predictive biomarkers of response for PSMA-targeted therapies in larger, prospective cohorts. Patient summaryThrough analysis of prostate cancer samples, we report that the presence of prostate-specific membrane antigen (PSMA) is extremely variable both within one patient and between different patients. This may limit the usefulness of PSMA scans and PSMA-targeted therapies. We show for the first time that prostate cancers with defective DNA repair produce more PSMA and so may respond better to PSMA-targeting treatments.

Effect of Welding Parameters on Junction Structure and Fracture Behavior of Friction-Stir-Welded Joints of 2024 Aluminum Alloys

In this paper, the effect of welding speed and tool rotation speed on the junction structure and fracture behavior of friction-stir-welded joints was investigated with use of 2024 aluminum alloys of 3.2 mm thick. Results show that the formation of junction structure is more affected by tool rotation speed than by welding speed due to the differences of heat input levels. When welding speed is at 100 mm/min and tool rotation speed is in the range of 400-600 r/min, the joint junction structure causes complete bonding with no welding defects. While under improper welding parameters, the junction structure causes the precipitate aggregation on the bonding line and the partial bonding with the appearance of both tunnel defects and kissing bond defects. Meanwhile, the results of tensile tests show that the tensile fracture of all FSW joints occur at the weld center. The failure mode of joints with defects-free junction structure characterized by shear fracture is ductile. While the failure mode of joints with defects-existent junction structure characterized by layered fracture surface is brittle. Furthermore, that heterogeneous microstructure, precipitate aggregation and welding defects converge inside the junction structure is the main factor affecting the tensile properties and fracture behavior of the friction-stir-welded joint.

Dependence of the Selective Doping Effect in Heterogeneous Defect Domains of Hexagonal-WS$_2$ on Wet-Transfer Methods

Dependence of the Selective Doping Effect in Heterogeneous Defect Domains of Hexagonal-WS$_2$ on Wet-Transfer Methods

Heterogeneous defect prediction with two-stage ensemble learning

Heterogeneous defect prediction (HDP) refers to predicting defect-prone software modules in one project (target) using heterogeneous data collected from other projects (source). Recently, several HDP methods have been proposed. However, these methods do not sufficiently incorporate the two characteristics of the defect data: (1) data could be linear inseparable, and (2) data could be highly imbalanced. These two data characteristics make it challenging to build an effective HDP model. In this paper, we propose a novel Two-Stage Ensemble Learning (TSEL) approach to HDP, which contains two stages: ensemble multi-kernel domain adaptation (EMDA) stage and ensemble data sampling (EDS) stage. In the EMDA stage, we develop an Ensemble Multiple Kernel Correlation Alignment (EMKCA) predictor, which combines the advantage of multiple kernel learning and domain adaptation techniques. In the EDS stage, we employ RESample with replacement (RES) technique to learn multiple different EMKCA predictors and use average ensemble to combine them together. These two stages create an ensemble of defect predictors. Extensive experiments on 30 public projects show that the proposed TSEL approach outperforms a range of competing methods. The improvement is 20.14–33.92% in AUC, 36.05–54.78% in f-measure, and 5.48–19.93% in balance, respectively.

Comparing the American Joint Committee on Cancer (AJCC) breast cancer staging eighth versus seventh edition and breast cancer biology in large databases.

e12077 Background: The AJCC 8th edition Breast Cancer Staging system includes biomarkers (estrogen receptor, progesterone receptor, Her2), grade and a genomic assay, to better reflect outcomes, compared to the 7th edition. We used databases with anatomic, biomarker and genomic information to determine if the 8th edition reflects biology better than the 7th edition. Methods: 696 breast cancer patients in The Cancer Genome Atlas (TCGA) and Text Information Extraction System (TIES) were staged both with 7th and 8th edition. Results: From the 7th to 8th editions, 66% of stage 2 patients migrated (64% to stage 1, 1.5% to stage 3). 36% of stage 3 patients migrated (22% to stage 1, 13% to stage 2). We analyzed the differences between stage 1 and 2 in both editions, and the overall survival hazard ratio calculated using Cox regression was greater in the 8th edition than the 7th (HR=1.50, HR=1.22 respectively). We performed Gene Set Enrichment Analysis (GSEA) and found that gene sets related to cell cycle and cell proliferation (E2F targets, G2M checkpoint) were significantly enriched in higher stage in 8th edition but not 7th. Mutant Allele Tumor Heterogeneity (MATH) and Homologous Recombination Defects (HRD) were elevated in higher stages in the 8th edition (p=0.022, p&lt;0.001 respectively), but not the 7th. Immune cells (CD8 Tcell, NK cell) as well as CYT (Immune cytolytic activity score), and Tcell exhaustion markers (PD1, PDL1) were all found to be elevated in higher stage in 8th edition, but not 7th. Conclusions: The 8th edition AJCC Breast Cancer Staging system categorizes cancers by aggressive biology better than the 7th edition. We also found that higher stage tumors trigger an enhanced immune response, but are unable to overcome the aggressive cancer biology.

PSMA heterogeneity and DNA repair defects in prostate cancer.

5002 Background: Prostate-specific membrane antigen (PSMA) is a promising target for theranostics in metastatic castration resistant prostate cancer (mCRPC). Methods: Membranous PSMA (mPSMA) expression was immunohistochemically evaluated in castration sensitive (CSPC) (n = 38) and mCRPC (n = 60) tissue biopsies, and associations with molecular aberrations (next-generation sequencing; NGS) and clinical outcome were determined. Results: mPSMA expression was significantly higher (p = 0.005) in mCRPC biopsies (median H-score [interquartile range]; 55.0 [2.8-117.5]) compared to CSPC biopsies (17.5 [0.0-60.0]). Furthermore, patients with higher mPSMA expression ( &gt; median H-score) at diagnosis had higher Gleason Grade (p = 0.04) and shorter OS (p = 0.006). Critically, 42% (16/38) of CSPC biopsies and 27% (16/60) of mCRPC biopsies were completely negative for mPSMA expression. In addition, CSPC and mCRPC biopsies expressing mPSMA demonstrated marked intra-tumor heterogeneity in expression levels, commonly exhibiting areas without detectable PSMA (CSPC – 100%; mCRPC – 84%), while heterogeneous mPSMA expression between metastases from the same patient was also observed. Subsequent genomic analysis showed that mCRPC patients with deleterious DNA damage repair (DDR) aberrations have higher (p = 0.016) mPSMA expression (87.5 [25.0-247.5]) than those without these (20 [0.3-98.8]). Furthermore, 9 of the 11 patients (82%) responding to PARP inhibition had a mPSMA H-Score above the median. The association between mPSMA expression and DDR aberrations was validated in an independent cohort with known DDR aberrations. Tumors with DDR aberrations had significantly higher mPSMA (ATM 212.5 [136.3-300] p = 0.005; BRCA2 300 [165-300] p = &lt; 0.001) than unselected mCRPC biopsies (55.0 [2.75-117.5]). Finally, analyses of 122 mCRPC biopsy transcriptomes confirmed a negative correlation between PSMA and BRCA2 mRNA expression (p = 1.5x10-5). Conclusions: mPSMA expression in CSPC and mCRPC exhibits marked intra- and inter-patient heterogeneity, limiting the clinical utility of PSMA-targeted theranostics. We show for the first time that DDR gene aberrations associate with high mPSMA expression and may serve as predictive biomarkers for PSMA-targeted therapies.

On the Multiple Sources and Privacy Preservation Issues for Heterogeneous Defect Prediction

Heterogeneous defect prediction (HDP) refers to predicting defect-proneness of software modules in a target project using heterogeneous metric data from other projects. Existing HDP methods mainly focus on predicting target instances with single source. In practice, there exist plenty of external projects. Multiple sources can generally provide more information than a single project. Therefore, it is meaningful to investigate whether the HDP performance can be improved by employing multiple sources. However, a precondition of conducting HDP is that the external sources are available. Due to privacy concerns, most companies are not willing to share their data. To facilitate data sharing, it is essential to study how to protect the privacy of data owners before they release their data. In this paper, we study the above two issues in HDP. Specifically, to utilize multiple sources effectively, we propose a multi-source selection based manifold discriminant alignment (MSMDA) approach. To protect the privacy of data owners, a sparse representation based double obfuscation algorithm is designed and applied to HDP. Through a case study of 28 projects, our results show that MSMDA can achieve better performance than a range of baseline methods. The improvement is 3.4-$15.3$15.3 percent in g-measure and 3.0-$19.1$19.1 percent in AUC.

Unsupervised Deep Domain Adaptation for Heterogeneous Defect Prediction

Unsupervised Deep Domain Adaptation for Heterogeneous Defect Prediction

Curation and bioinformatic analysis of strabismus genes supports functional heterogeneity and proposes candidate genes with connections to RASopathies

Strabismus refers to the misalignment of the eyes and occurs in 2–4% of individuals. The low-resolution label “strabismus” covers a range of heterogeneous defects, which makes it challenging to unravel this condition. Consequently a coherent understanding of the causes is lacking. Here, we attempt to gain a better understanding of the underlying genetics by combining gene curation, diverse bioinformatic analyses (including gene ontology, pathway mapping, expression and network-based methods) and literature review. Through a phenotype-based curation process, we identify high-confidence and permissive sets of 54 and 233 genes potentially involved in strabismus. These genes can be grouped into 10 modules that together span a heterogeneous set of biological and molecular functions, and can be linked to clinical sub-phenotypes. Multiple lines of evidence associate retina and cerebellum biology with the strabismus genes. We further highlight a potential role of the Ras-MAPK pathway. Independently, sets of 11 genes and 15 loci tied to strabismus with definitive genetic basis have been compiled from the literature. We identify strabismus candidate genes for 5 of the 15 reported loci (CHD7; SLC9A6; COL18A1, COL6A2; FRY, BRCA2, SPG20; PARK2). Finally, we synthesize a Strabismus Candidate Gene Collection, which together with our curated gene sets will serve as a resource for future research. The results of this informatics study support the heterogeneity and complexity of strabismus and point to specific biological pathways and brain regions for future focus.