Ultra-Scalable Spectral Clustering and Ensemble Clustering

This paper focuses on scalability and robustness of spectral clustering for extremely large-scale datasets with limited resources. Two novel algorithms are proposed, namely, ultra-scalable spectral clustering (U-SPEC) and ultra-scalable ensemble clustering (U-SENC). In U-SPEC, a hybrid representative selection strategy and a fast approximation method for K-nearest representatives are proposed for the construction of a sparse affinity sub-matrix. By interpreting the sparse sub-matrix as a bipartite graph, the transfer cut is then utilized to efficiently partition the graph and obtain the clustering result. In U-SENC, multiple U-SPEC clusterers are further integrated into an ensemble clustering framework to enhance the robustness of U-SPEC while maintaining high efficiency. Based on the ensemble generation via multiple U-SEPC's, a new bipartite graph is constructed between objects and base clusters and then efficiently partitioned to achieve the consensus clustering result. It is noteworthy that both U-SPEC and U-SENC have nearly linear time and space complexity, and are capable of robustly and efficiently partitioning ten-million-level nonlinearly-separable datasets on a PC with 64GB memory. Experiments on various large-scale datasets have demonstrated the scalability and robustness of our algorithms. The MATLAB code and experimental data are available at https://www.researchgate.net/publication/330760669.Comment: To appear in IEEE Transactions on Knowledge and Data Engineering, 201

Effective reranking for extracting protein-protein interactions from biomedical literature

A semantic parser based on the hidden vector state (HVS) model has been proposed for extracting protein-protein interactions. The HVS model is an extension of the basic discrete hidden Markov model, in which context is encoded as a stack-oriented state vector and state transitions are factored into a stack shift operation followed by the push of a new preterminal category label. In this paper, we investigate three different models, log-linear regression (LLR), neural networks (NNs) and support vector machines (SVMs), to rerank parses generated by the HVS model for protein-protein interactions extraction. Features used for reranking are manually defined which include the parse information, the structure information, and the complexity information. The experimental results show that reranking can indeed improve the performance of protein-protein interactions extraction, and reranking based on SVM gives more stable performance than LLR and NN

Gene, Environment and Methylation (GEM): a tool suite to efficiently navigate large scale epigenome wide association studies and integrate genotype and interaction between genotype and environment

10.1186/s12859-016-1161-zBMC bioinformatics171Article number 299GUSTO (Growing up towards Healthy Outcomes

Microinpelletation Technique for Studying the Localized Action of Hormones and Some Results of its Use in the Mammary Gland

Biochemistr

Determinants of patient preferences for total knee replacement: African-Americans and whites

Introduction: Patient preferences contribute to marked racial disparities in the utilization of total knee replacement (TKR). The objectives of this study were to identify the determinants of knee osteoarthritis (OA) patients' preferences regarding TKR by race and to identify the variables that may mediate racial differences in willingness to undergo TKR. Methods: Five hundred fourteen White (WH) and 285 African-American (AA) patients with chronic knee pain and radiographic evidence of OA participated in the study. Participants were recruited from the community, an academic medical center, and a Veterans Affairs hospital. Structured interviews were conducted to collect socio-demographics, disease severity, socio-cultural determinants, and treatment preferences. Logistic regression was performed, stratified by race, to identify determinants of preferences. Clinical and socio-cultural factors were entered simultaneously into the models. Stepwise selection identified factors for inclusion in the final models (p < 0.20). Results: Compared to WHs, AAs were less willing to undergo TKR (80% vs. 62%, respectively). Better expectations regarding TKR surgery outcomes determined willingness to undergo surgery in both AAs (odds ratio (OR) 2.08, 95% confidence interval (CI) 0.91-4.79 for 4th vs. 1st quartile) and WHs (OR 5.11, 95% CI 2.31-11.30 for 4th vs. 1st quartile). Among AAs, better understanding of the procedure (OR 1.80, 95% CI 0.97-3.35), perceiving a short hospital course (OR 0.81, 95% CI 0.58-1.13), and believing in less post-surgical pain (OR 0.73, 95% CI 0.39-1.35) and walking difficulties (OR 0.66, 95% CI 0.37-1.16) also determined willingness. Among WHs, having surgical discussion with a physician (OR 1.96, 95% CI 1.05-3.68), not ever receiving surgical referral (OR 0.56, 95% CI 0.32-0.99), and higher trust in the healthcare system (OR 1.58, 95% CI 0.75-3.31 for 4th vs. 1st quartile) additionally determined willingness. Among the variables considered, only knowledge-related matters pertaining to TKR attenuated the racial difference in knee OA patients' treatment preference. Conclusions: Expectations of surgical outcomes influence preference for TKR in all patients, but clinical and socio-cultural factors exist that shape marked racial differences in preferences for TKR. Interventions to reduce or eliminate racial disparities in the utilization of TKR should consider and target these factors