Methionine Sulfoxide Speciation in Mouse Hippocampus Revealed by Global Proteomics Exhibits Age- and Alzheimer's Disease-Dependent Changes Targeted to Mitochondrial and Glycolytic Pathways.

Methionine oxidation to the sulfoxide form (MSox) is a poorly understood post-translational modification of proteins associated with non-specific chemical oxidation from reactive oxygen species (ROS), whose chemistries are linked to various disease pathologies, including neurodegeneration. Emerging evidence shows MSox site occupancy is, in some cases, under enzymatic regulatory control, mediating cellular signaling, including phosphorylation and/or calcium signaling, and raising questions as to the speciation and functional nature of MSox across the proteome. The 5XFAD lineage of the C57BL/6 mouse has well-defined Alzheimer's and aging states. Using this model, we analyzed age-, sex-, and disease-dependent MSox speciation in the mouse hippocampus. In addition, we explored the chemical stability and statistical variance of oxidized peptide signals to understand the needed power for MSox-based proteome studies. Our results identify mitochondrial and glycolytic pathway targets with increases in MSox with age as well as neuroinflammatory targets accumulating MSox with AD in proteome studies of the mouse hippocampus. Further, this paper establishes a foundation for reproducible and rigorous experimental MSox-omics appropriate for novel target identification in biological discovery and for biomarker analysis in ROS and other oxidation-linked diseases.

Possible Role of Correlation Coefficients and Network Analysis of Multiple Intracellular Proteins in Blood Cells of Patients with Bipolar Disorder in Studying the Mechanism of Lithium Responsiveness: A Proof-Concept Study.

Background: The mechanism of lithium treatment responsiveness in bipolar disorder (BD) remains unclear. The aim of this study was to explore the utility of correlation coefficients and protein-to-protein interaction (PPI) network analyses of intracellular proteins in monocytes and CD4+ lymphocytes of patients with BD in studying the potential mechanism of lithium treatment responsiveness. Methods: Patients with bipolar I or II disorder who were diagnosed with the MINI for DSM-5 and at any phase of the illness with at least mild symptom severity and received lithium (serum level ≥ 0.6 mEq/L) for 16 weeks were divided into two groups, responders (≥50% improvement in Montgomery-Asberg Depression Rating Scale and/or Young Mania Rating Scale scores from baseline) and non-responders. Twenty-eight intracellular proteins/analytes in CD4+ lymphocytes and monocytes were analyzed with a tyramine-based signal-amplified flow cytometry procedure. Correlation coefficients between analytes at baseline were estimated in both responders and non-responders and before and after lithium treatment in responders. PPI network, subnetwork, and pathway analyses were generated based on fold change/difference in studied proteins/analytes between responders and non-responders. Results: Of the 28 analytes from 12 lithium-responders and 11 lithium-non-responders, there were more significant correlations between analytes in responders than in non-responders at baseline. Of the nine lithium responders with pre- and post-lithium blood samples available, the correlations between most analytes were weakened after lithium treatment with cell-type specific patterns in CD4+ lymphocytes and monocytes. PPI network/subnetwork and pathway analyses showed that lithium response was involved in four pathways, including prolactin, leptin, neurotrophin, and brain-derived neurotrophic factor pathways. Glycogen synthase kinase 3 beta and nuclear factor NF-kappa-B p65 subunit genes were found in all four pathways. Conclusions: Using correlation coefficients, PPI network/subnetwork, and pathway analysis with multiple intracellular proteins appears to be a workable concept for studying the mechanism of lithium responsiveness in BD. Larger sample size studies are necessary to determine its utility.

Exploring Temporal and Sex-Linked Dysregulation in Alzheimer's Disease Phospho-Proteome.

This study aims to characterize dysregulation of phosphorylation for the 5XFAD mouse model of Alzheimer's disease (AD). Employing global phosphoproteome measurements, we analyze temporal (3, 6, 9 months) and sex-dependent effects on mouse hippocampus tissue to unveil molecular signatures associated with AD initiation and progression. Our results indicate 1.9 to 4.4 times higher phosphorylation prevalence compared to protein expression across all time points, with approximately 4.5 times greater prevalence in females compared to males at 3 and 9 months. Moreover, our findings reveal consistent phosphorylation of known AD biomarkers APOE and GFAP in 5XFAD mice, alongside novel candidates BIG3, CLCN6 and STX7, suggesting their potential as biomarkers for AD pathology. In addition, we identify PDK1 as a significantly dysregulated kinase at 9 months in females, and the regulation of gap junction activity as a key pathway associated with Alzheimer's disease across all time points. AD-Xplorer, the interactive browser of our dataset, enables exploration of AD-related changes in phosphorylation, protein expression, kinase activities, and pathways. AD-Xplorer aids in biomarker discovery and therapeutic target identification, emphasizing temporal and sex-specific nature of significant phosphoproteomic signatures. Available at: https://yilmazs.shinyapps.io/ADXplorer.

Systematic Investigation of Meta-Analysis Data on Treatment Effectiveness for Physical, Psychological, and Sexual Intimate Partner Violence Perpetration.

Intimate partner violence can lead to physical, economical, mental, and sexual well-being issues, and even death, and it is most commonly experienced by women. There exist a number of treatment models for the prevention and treatment of intimate partner violence (IPV). In this study, we provided a comprehensive meta-regression analysis of the effectiveness of batterer treatment programs, with a view to characterizing the interplay between different forms of IPV (physical, psychological, and sexual). Using meta-regression, we explore the effect sizes and whether IPV treatment methods have distinct impacts on the outcomes. We use the difference normalized by pretreatment mean and variance foldchange to uncover the relationship between different violence subtypes and how they drive each other. Specifically, our study found that studies with more pre-treatment psychological and/or sexual violence, lead to less favorable outcomes while the studies that start with more physical violence are able to demonstrate their effects more effectively. Results of this study can be used to help the clinician effectively select the treatment for the perpetrator based on the violence type and severity of violence in order to more effectively treat the needs for each specific relationship.

La violencia de pareja puede llegar a afectar al bienestar físico, económico, mental y sexual e incluso llevar a la muerte, siendo experimentada con más frecuencia por las mujeres. Hay diversos modelos de prevención y tratamiento de la violencia de pareja (VP). En este estudio se lleva a cabo un análisis global de meta-regresión de la eficacia de los programas de tratamiento para maltratadores centrado en caracterizar la interacción entre diferentes formas de VP (física, psicológica y sexual). Mediante meta-regresión se explora el tamaño del efecto y si los distintos métodos de tratamiento de la VP influyen de modo distinto en los resultados. Se utiliza la diferencia normalizada por la media y la reducción de la heterogeneidad (varianza) del pretratamiento para analizar la relación entre los distintos tipos de violencia y cómo se influyen mutuamente. En concreto en este trabajo encontramos que los estudios con más violencia psicológica y/o sexual en el pretratamiento tienen resultados menos favorables, mientras que los que comienzan con más violencia física pueden demostrar sus efectos de un modo más eficaz. Los resultados de este estudio pueden ser de ayuda para que el profesional seleccione de modo más eficaz el tratamiento para el agresor teniendo en cuenta el tipo de violencia y su gravedad, con el fin de tratar de forma más adecuada las necesidades de cada relación específica.

Characterizing Disparities in the Treatment of Intimate Partner Violence.

Exposure to Intimate Partner Violence (IPV) has lasting adverse effects on the physical, behavioral, cognitive, and emotional health of survivors. To this end, it is critical to understand the effectiveness of IPV treatment strategies in reducing IPV and its debilitating effects. Meta-analyses designed to comprehensively describe the effectiveness of treatments offer unique advantages. However, the heterogeneity within and between studies poses challenges in interpreting findings. Meta-analyses are therefore unlikely to identify the factors that underlie disparities in treatment efficacy. To characterize the effect of demographic and social factors on treatment effectiveness, we develop a comprehensive computational and statistical framework that uses Meta-regression to characterize the effect of demographic and social variables on treatment outcomes. The innovations in our methodology include (i) standardization of outcome variables to enable meaningful comparisons among studies, and (ii) two parallel meta-regression pipelines to reliably handle missing data.

Comprehensive Analysis of Electronic Health Records to Characterize The Association Between Intimate Partner Violence and Mental Health.

Intimate partner violence (IPV) involves physical, emotional, and sexual harm to the survivor. To characterize the relationship between mental health and IPV, we utilized electronic health records (EHR) data from IBM Explorys. Focusing on 15 mental health conditions and IPV, we queried cohorts of patients with these conditions to discover additional medical terms, including symptoms, findings, and diagnoses that are prevalent in these cohorts. We then systematically assessed the (i) direct association (co-occurrence, i.e., relative prevalence of a medical term in a cohort compared to the background population) and (ii) indirect association (the similarity between co-occurrence profiles) between all pairs of these mental health conditions. Our results showed that direct and indirect measures of association provide complementary insights into the relationship between pairs of conditions. Using this framework, we discovered several patterns of association among 16 different mental health related conditions.

Systematic Investigation of Meta-Analysis Data on Treatment Effectiveness for Physical, Psychological, and Sexual Intimate Partner Violence Perpetration / [Investigación sistemática de los datos de meta-análisis sobre la eficacia del tratamiento de la agresión de pareja física, psicológica y sexual]

Intimate partner violence can lead to physical, economical, mental, and sexual well-being issues, and even death, and it is most commonly experienced by women. There exist a number of treatment models for the prevention and treatment of intimate partner violence (IPV). In this study, we provided a comprehensive meta-regression analysis of the effectiveness of batterer treatment programs, with a view to characterizing the interplay between different forms of IPV (physical, psychological, and sexual). Using meta-regression, we explore the effect sizes and whether IPV treatment methods have distinct impacts on the outcomes. We use the difference normalized by pretreatment mean and variance foldchange to uncover the relationship between different violence subtypes and how they drive each other. Specifically, our study found that studies with more pre-treatment psychological and/or sexual violence, lead to less favorable outcomes while the studies that start with more physical violence are able to demonstrate their effects more effectively. Results of this study can be used to help the clinician effectively select the treatment for the perpetrator based on the violence type and severity of violence in order to more effectively treat the needs for each specific relationship.(AU)

La violencia de pareja puede llegar a afectar al bienestar físico, económico, mental y sexual e incluso llevar a la muerte, siendo experimentada con más frecuencia por las mujeres. Hay diversos modelos de prevención y tratamiento de la violencia de pareja (VP). En este estudio se lleva a cabo un análisis global de meta-regresión de la eficacia de los programas de tratamiento para maltratadores centrado en caracterizar la interacción entre diferentes formas de VP (física, psicológica y sexual). Mediante meta-regresión se explora el tamaño del efecto y si los distintos métodos de tratamiento de la VP influyen de modo distinto en los resultados. Se utiliza la diferencia normalizada por la media y la reducción de la heterogeneidad (varianza) del pretratamiento para analizar la relación entre los distintos tipos de violencia y cómo se influyen mutuamente. En concreto en este trabajo encontramos que los estudios con más violencia psicológica y/o sexual en el pretratamiento tienen resultados menos favorables, mientras que los que comienzan con más violencia física pueden demostrar sus efectos de un modo más eficaz. Los resultados de este estudio pueden ser de ayuda para que el profesional seleccione de modo más eficaz el tratamiento para el agresor teniendo en cuenta el tipo de violencia y su gravedad, con el fin de tratar de forma más adecuada las necesidades de cada relación específica.(AU)

Adverse health correlates of intimate partner violence against older women: Mining electronic health records.

Intimate partner violence (IPV) is often studied as a problem that predominantly affects younger women. However, studies show that older women are also frequently victims of abuse even though the physical effects of abuse are harder to detect. In this study, we mined the electronic health records (EHR) available through IBM Explorys to identify health correlates of IPV that are specific to older women. Our analyses suggested that diagnostic terms that are co-morbid with IPV in older women are dominated by substance abuse and associated toxicities. When we considered differential co-morbidity, i.e., terms that are significantly more associated with IPV in older women compared to younger women, we identified terms spanning mental health issues, musculoskeletal issues, neoplasms, and disorders of various organ systems including skin, ears, nose and throat. Our findings provide pointers for further investigation in understanding the health effects of IPV among older women, as well as potential markers that can be used for screening IPV.

Differences in intracellular protein levels in monocytes and CD4+ lymphocytes between bipolar depressed patients and healthy controls: A pilot study with tyramine-based signal-amplified flow cytometry.

BACKGROUND: Molecular biomarkers for bipolar disorder (BD) that distinguish it from other manifestations of depressive symptoms remain unknown. The aim of this study was to determine if a very sensitive tyramine-based signal-amplification technology for flow cytometry (CellPrint™) could facilitate the identification of cell-specific analyte expression profiles of peripheral blood cells for bipolar depression (BPD) versus healthy controls (HCs). METHODS: The diagnosis of psychiatric disorders was ascertained with Mini International Neuropsychiatric Interview for DSM-5. Expression levels for eighteen protein analytes previously shown to be related to bipolar disorder were assessed with CellPrint™ in CD4+ T cells and monocytes of bipolar patients and HCs. Implementation of protein-protein interaction (PPI) network and pathway analysis was subsequently used to identify new analytes and pathways for subsequent interrogations. RESULTS: Fourteen drug-naïve or -free patients with bipolar I or II depression and 17 healthy controls (HCs) were enrolled. The most distinguishable changes in analyte expression based on t-tests included GSK3ß, HMGB1, IRS2, phospho-GSK3αß, phospho-RELA, and TSPO in CD4+ T cells and calmodulin, GSK3ß, IRS2, and phospho-HS1 in monocytes. Subsequent PPI and pathway analysis indicated that prolactin, leptin, BDNF, and interleukin-3 signal pathways were significantly different between bipolar patients and HCs. LIMITATION: The sample size of the study was small and 2 patients were on medications. CONCLUSION: In this pilot study, CellPrint™ was able to detect differences in cell-specific protein levels between BPD patients and HCs. A subsequent study including samples from patients with BPD, major depressive disorder, and HCs is warranted.

Divergent Directionality of Immune Cell-Specific Protein Expression between Bipolar Lithium Responders and Non-Responders Revealed by Enhanced Flow Cytometry.

Background and Objectives: There is no biomarker to predict lithium response. This study used CellPrint™ enhanced flow cytometry to study 28 proteins representing a spectrum of cellular pathways in monocytes and CD4+ lymphocytes before and after lithium treatment in patients with bipolar disorder (BD). Materials and Methods: Symptomatic patients with BD type I or II received lithium (serum level ≥ 0.6 mEq/L) for 16 weeks. Patients were assessed with standard rating scales and divided into two groups, responders (≥50% improvement from baseline) and non-responders. Twenty-eight intracellular proteins in CD4+ lymphocytes and monocytes were analyzed with CellPrint™, an enhanced flow cytometry procedure. Data were analyzed for differences in protein expression levels. Results: The intent-to-treat sample included 13 lithium-responders (12 blood samples before treatment and 9 after treatment) and 11 lithium-non-responders (11 blood samples before treatment and 4 after treatment). No significant differences in expression between the groups was observed prior to lithium treatment. After treatment, the majority of analytes increased expression in responders and decreased expression in non-responders. Significant increases were seen for PDEB4 and NR3C1 in responders. A significant decrease was seen for NR3C1 in non-responders. Conclusions: Lithium induced divergent directionality of protein expression depending on the whether the patient was a responder or non-responder, elucidating molecular characteristics of lithium responsiveness. A subsequent study with a larger sample size is warranted.

Prediction of Kinase-Substrate Associations Using The Functional Landscape of Kinases and Phosphorylation Sites.

Protein phosphorylation is a key post-translational modification that plays a central role in many cellular processes. With recent advances in biotechnology, thousands of phosphorylated sites can be identified and quantified in a given sample, enabling proteome-wide screening of cellular signaling. However, for most (> 90%) of the phosphorylation sites that are identified in these experiments, the kinase(s) that target these sites are unknown. To broadly utilize available structural, functional, evolutionary, and contextual information in predicting kinase-substrate associations (KSAs), we develop a network-based machine learning framework. Our framework integrates a multitude of data sources to characterize the landscape of functional relationships and associations among phosphosites and kinases. To construct a phosphosite-phosphosite association network, we use sequence similarity, shared biological pathways, co-evolution, co-occurrence, and co-phosphorylation of phosphosites across different biological states. To construct a kinase-kinase association network, we integrate protein-protein interactions, shared biological pathways, and membership in common kinase families. We use node embeddings computed from these heterogeneous networks to train machine learning models for predicting kinase-substrate associations. Our systematic computational experiments using the PhosphositePLUS database shows that the resulting algorithm, NetKSA, outperforms two state-of-the-art algorithms, including KinomeXplorer and LinkPhinder, in overall KSA prediction. By stratifying the ranking of kinases, NetKSA also enables annotation of phosphosites that are targeted by relatively less-studied kinases.Availability: The code and data are available at compbio.case.edu/NetKSA/.

Temporal and Sex-Linked Protein Expression Dynamics in a Familial Model of Alzheimer's Disease.

Mouse models of Alzheimer's disease (AD) show progression through stages reflective of human pathology. Proteomics identification of temporal and sex-linked factors driving AD-related pathways can be used to dissect initiating and propagating events of AD stages to develop biomarkers or design interventions. In the present study, we conducted label-free proteome measurements of mouse hippocampus tissue with variables of time (3, 6, and 9 months), genetic background (5XFAD versus WT), and sex (equal males and females). These time points are associated with well-defined phenotypes with respect to the following: Aß42 plaque deposition, memory deficits, and neuronal loss, allowing correlation of proteome-based molecular signatures with the mouse model stages. Our data show 5XFAD mice exhibit increases in known human AD biomarkers as amyloid-beta peptide, APOE, GFAP, and ITM2B are upregulated across all time points/stages. At the same time, 23 proteins are here newly associated with Alzheimer's pathology as they are also dysregulated in 5XFAD mice. At a pathways level, the 5XFAD-specific upregulated proteins are significantly enriched for DNA damage and stress-induced senescence at 3-month only, while at 6-month, the AD-specific proteome signature is altered and significantly enriched for membrane trafficking and vesicle-mediated transport protein annotations. By 9-month, AD-specific dysregulation is also characterized by significant neuroinflammation with innate immune system, platelet activation, and hyper-reactive astrocyte-related enrichments. Aside from these temporal changes, analysis of sex-linked differences in proteome signatures uncovered novel sex and AD-associated proteins. Pathway analysis revealed sex-linked differences in the 5XFAD model to be involved in the regulation of well-known human AD-related processes of amyloid fibril formation, wound healing, lysosome biogenesis, and DNA damage. Verification of the discovery results by Western blot and parallel reaction monitoring confirm the fundamental conclusions of the study and poise the 5XFAD model for further use as a molecular tool for understanding AD.

Functional characterization of co-phosphorylation networks.

MOTIVATION: Protein phosphorylation is a ubiquitous regulatory mechanism that plays a central role in cellular signaling. According to recent estimates, up to 70% of human proteins can be phosphorylated. Therefore, the characterization of phosphorylation dynamics is critical for understanding a broad range of biological and biochemical processes. Technologies based on mass spectrometry are rapidly advancing to meet the needs for high-throughput screening of phosphorylation. These technologies enable untargeted quantification of thousands of phosphorylation sites in a given sample. Many labs are already utilizing these technologies to comprehensively characterize signaling landscapes by examining perturbations with drugs and knockdown approaches, or by assessing diverse phenotypes in cancers, neuro-degerenational diseases, infectious diseases and normal development. RESULTS: We comprehensively investigate the concept of 'co-phosphorylation' (Co-P), defined as the correlated phosphorylation of a pair of phosphosites across various biological states. We integrate nine publicly available phosphoproteomics datasets for various diseases (including breast cancer, ovarian cancer and Alzheimer's disease) and utilize functional data related to sequence, evolutionary histories, kinase annotations and pathway annotations to investigate the functional relevance of Co-P. Our results across a broad range of studies consistently show that functionally associated sites tend to exhibit significant positive or negative Co-P. Specifically, we show that Co-P can be used to predict with high precision the sites that are on the same pathway or that are targeted by the same kinase. Overall, these results establish Co-P as a useful resource for analyzing phosphoproteins in a network context, which can help extend our knowledge on cellular signaling and its dysregulation. AVAILABILITY AND IMPLEMENTATION: github.com/msayati/Cophosphorylation. This research used the publicly available datasets published by other researchers as cited in the manuscript. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Protein Biomarkers in Monocytes and CD4+ Lymphocytes for Predicting Lithium Treatment Response of Bipolar Disorder: a Feasibility Study with Tyramine-Based Signal-Amplified Flow Cytometry.

Purpose: To determine if enhanced flow cytometry (CellPrint™) can identify intracellular proteins of lithium responsiveness in monocytes and CD4+ lymphocytes from patients with bipolar disorder. Methods: Eligible bipolar I or II patients were openly treated with lithium for 16-weeks. Baseline levels of Bcl2, BDNF, calmodulin, Fyn, phospho-Fyn/phospho-Yes, GSK3ß, phospho-GSK3αß, HMGB1, iNOS, IRS2, mTor, NLPR3, PGM1, PKA C-α, PPAR-γ, phospho-RelA, and TPH1 in monocytes and CD4+ lymphocytes of lithium responders and non-responders were measured with CellPrint™. Their utility of discriminating responders from non-responders was explored. Protein-protein network and pathway enrichment analyses were conducted. Results: Of the 24 intent-to-treat patients, 12 patients completed the 16-week study. Eleven of 13 responders and 8 of 11 non-responders were available for this analysis. The levels of the majority of analytes in lithium responders were lower than non-responders in both cell types, but only the level of GSK3ß in monocytes was significantly different (p = 0.034). The combination of GSK3ß and phospho-GSK3αß levels in monocytes correctly classified 11/11 responders and 5/8 non-responders. Combination of GSK3ß, phospho-RelA, TPH1 and PGM1 correctly classified 10/11 responders and 6/7 non-responders, both with a likelihood of ≥ 85%. Prolactin, leptin, BDNF, neurotrophin, and epidermal growth factor/epidermal growth factor receptor signaling pathways are involved in the lithium treatment response. GSK3ß and RelA genes are involved in 4 of 5 these pathways. Conclusion: CellPrint™ flow cytometry was able to detect differences in multiple proteins in monocytes and CD4+ lymphocytes between lithium responders and non-responders. A large study is warranted to confirm or refute these findings.

Circulating microbial content in myeloid malignancy patients is associated with disease subtypes and patient outcomes.

Although recent work has described the microbiome in solid tumors, microbial content in hematological malignancies is not well-characterized. Here we analyze existing deep DNA sequence data from the blood and bone marrow of 1870 patients with myeloid malignancies, along with healthy controls, for bacterial, fungal, and viral content. After strict quality filtering, we find evidence for dysbiosis in disease cases, and distinct microbial signatures among disease subtypes. We also find that microbial content is associated with host gene mutations and with myeloblast cell percentages. In patients with low-risk myelodysplastic syndrome, we provide evidence that Epstein-Barr virus status refines risk stratification into more precise categories than the current standard. Motivated by these observations, we construct machine-learning classifiers that can discriminate among disease subtypes based solely on bacterial content. Our study highlights the association between the circulating microbiome and patient outcome, and its relationship with disease subtype.

Uncovering complementary sets of variants for predicting quantitative phenotypes.

MOTIVATION: Genome-wide association studies show that variants in individual genomic loci alone are not sufficient to explain the heritability of complex, quantitative phenotypes. Many computational methods have been developed to address this issue by considering subsets of loci that can collectively predict the phenotype. This problem can be considered a challenging instance of feature selection in which the number of dimensions (loci that are screened) is much larger than the number of samples. While currently available methods can achieve decent phenotype prediction performance, they either do not scale to large datasets or have parameters that require extensive tuning. RESULTS: We propose a fast and simple algorithm, Macarons, to select a small, complementary subset of variants by avoiding redundant pairs that are likely to be in linkage disequilibrium. Our method features two interpretable parameters that control the time/performance trade-off without requiring parameter tuning. In our computational experiments, we show that Macarons consistently achieves similar or better prediction performance than state-of-the-art selection methods while having a simpler premise and being at least two orders of magnitude faster. Overall, Macarons can seamlessly scale to the human genome with ∼107 variants in a matter of minutes while taking the dependencies between the variants into account. AVAILABILITYAND IMPLEMENTATION: Macarons is available in Matlab and Python at https://github.com/serhan-yilmaz/macarons. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Node similarity-based graph convolution for link prediction in biological networks.

BACKGROUND: Link prediction is an important and well-studied problem in network biology. Recently, graph representation learning methods, including Graph Convolutional Network (GCN)-based node embedding have drawn increasing attention in link prediction. MOTIVATION: An important component of GCN-based network embedding is the convolution matrix, which is used to propagate features across the network. Existing algorithms use the degree-normalized adjacency matrix for this purpose, as this matrix is closely related to the graph Laplacian, capturing the spectral properties of the network. In parallel, it has been shown that GCNs with a single layer can generate more robust embeddings by reducing the number of parameters. Laplacian-based convolution is not well suited to single-layered GCNs, as it limits the propagation of information to immediate neighbors of a node. RESULTS: Capitalizing on the rich literature on unsupervised link prediction, we propose using node similarity-based convolution matrices in GCNs to compute node embeddings for link prediction. We consider eight representative node-similarity measures (Common Neighbors, Jaccard Index, Adamic-Adar, Resource Allocation, Hub- Depressed Index, Hub-Promoted Index, Sorenson Index and Salton Index) for this purpose. We systematically compare the performance of the resulting algorithms against GCNs that use the degree-normalized adjacency matrix for convolution, as well as other link prediction algorithms. In our experiments, we use three-link prediction tasks involving biomedical networks: drug-disease association prediction, drug-drug interaction prediction and protein-protein interaction prediction. Our results show that node similarity-based convolution matrices significantly improve the link prediction performance of GCN-based embeddings. CONCLUSION: As sophisticated machine-learning frameworks are increasingly employed in biological applications, historically well-established methods can be useful in making a head-start. AVAILABILITY AND IMPLEMENTATION: Our method, SiGraC, is implemented as a Python library and is freely available at https://github.com/mustafaCoskunAgu/SiGraC.

Data-driven identification of subtypes of intimate partner violence.

Intimate partner violence (IPV) is a complex problem with multiple layers of heterogeneity. We took a data-driven approach to characterize this heterogeneity. We integrated data from different studies, representing 640 individuals from various backgrounds. We used hierarchical clustering to systematically group cases in terms of their similarities according to violence variables. Results suggested that the cases can be clustered into 12 hierarchically organized subgroups, with verbal abuse and negotiation being the main discriminatory factors at higher levels. The presence of physical assault, injury, and sexual coercion was discriminative at lower levels of the hierarchy. Subgroups also exhibited significant differences in terms of relationship dynamics and individual factors. This study represents an attempt toward using integrative data analysis to understand the etiology of violence. These results can be useful in informing treatment efforts. The integrative data analysis framework we develop can also be applied to various other problems.

Co-occurrence Patterns of Intimate Partner Violence.

Intimate partner violence (IPV) is an important social and public health problem, affecting millions of women worldwide. Violence in a relationship can occur in multiple ways, including physical violence, psychological aggression, and sexual violence. In this study, utilizing data from the National Intimate Partner and Sexual Violence Survey (NISVS), we comprehensively investigate the interplay between physical, psychological, and sexual violence, in terms of their co-occurrence patterns, their relation to trauma symptoms and overall health of victims. For this purpose, we perform network analysis and develop a visualization technique that enables in-depth navigation of the three-dimensional (physical, psychological, sexual) space of violence. Our findings show that physical violence tends to significantly co-occur with psychological abuse, and violence intensifies when both are present. We also find that sexual violence tends to overlap less with other types of violence, particularly with physical violence. Milder forms of psychological abuse are prominent in the population and seem to represent a separate type of abuse (micro-aggression) in terms of its occurrence patterns. Finally, we observe that trauma symptoms and health problems tend to be reported more by survivors at the presence of intense psychological aggression. Our findings can be useful in developing treatments that target different patterns of IPV.

Robust inference of kinase activity using functional networks.

Mass spectrometry enables high-throughput screening of phosphoproteins across a broad range of biological contexts. When complemented by computational algorithms, phospho-proteomic data allows the inference of kinase activity, facilitating the identification of dysregulated kinases in various diseases including cancer, Alzheimer's disease and Parkinson's disease. To enhance the reliability of kinase activity inference, we present a network-based framework, RoKAI, that integrates various sources of functional information to capture coordinated changes in signaling. Through computational experiments, we show that phosphorylation of sites in the functional neighborhood of a kinase are significantly predictive of its activity. The incorporation of this knowledge in RoKAI consistently enhances the accuracy of kinase activity inference methods while making them more robust to missing annotations and quantifications. This enables the identification of understudied kinases and will likely lead to the development of novel kinase inhibitors for targeted therapy of many diseases. RoKAI is available as web-based tool at http://rokai.io .