Developing a scoring function for NMR structure-based assignments using machine learning

Determining the assignment of signals received from the ex- periments (peaks) to speci_c nuclei of the target molecule in Nuclear Magnetic Resonance (NMR1) spectroscopy is an important challenge. Nuclear Vector Replacement (NVR) ([2, 3]) is a framework for structure- based assignments which combines multiple types of NMR data such as chemical shifts, residual dipolar couplings, and NOEs. NVR-BIP [1] is a tool which utilizes a scoring function with a binary integer programming (BIP) model to perform the assignments. In this paper, support vector machines (SVM) and boosting are employed to combine the terms in NVR-BIP's scoring function by viewing the assignment as a classi_ca- tion problem. The assignment accuracies obtained using this approach show that boosting improves the assignment accuracy of NVR-BIP on our data set when RDCs are not available and outperforms SVMs. With RDCs, boosting and SVMs o_er mixed results

Developing a Scoring Function for NMR Structure-based Assignments using Machine Learning

Abstract. Determining the assignment of signals received from the experiments (peaks) to specific nuclei of the target molecule in Nuclear Magnetic Resonance (NMR 1 ) spectroscopy is an important challenge. Nuclear Vector Replacement (NVR) ([2, 3]) is a framework for structurebased assignments which combines multiple types of NMR data such as chemical shifts, residual dipolar couplings, and NOEs. NVR-BIP [1] is a tool which utilizes a scoring function with a binary integer programming (BIP) model to perform the assignments. In this paper, support vector machines (SVM) and boosting are employed to combine the terms in NVR-BIP's scoring function by viewing the assignment as a classification problem. The assignment accuracies obtained using this approach show that boosting improves the assignment accuracy of NVR-BIP on our data set when RDCs are not available and outperforms SVMs. With RDCs, boosting and SVMs offer mixed results

Conference of (Directors of) Research Institutes on Disarmament, held in the Palais des Nations, Geneva, Switzerland, during 16-18 November 1981

Protein structure determination is crucial to understand a protein's function and to develop drugs against diseases. Nuclear Magnetic Resonance (NMR1) spectroscopy is an experimental technique that allows one to study protein structure in solution. In NMR Structure-based assignment problem, the aim is to assign experimentally observed peaks to the specific nuclei of the target molecule by using a template protein and it is an important computational challenge. NVR-BIP is a tool that utilizes a scoring function based on NVR’s [5, 6] framework and computes assignments for given NMR data. In this paper, we incorporate HADAMAC experiment—which helps predict an amino acid class for each peak— with NVRBIP's scoring function. Experiments show that the new scoring function results in higher assignment accuracies compared to the previous approaches

A deep learning approach to sentiment analysis in Turkish

This study proposes using deep learning for sentiment analysis in Turkish. Traditional machine learning methods such as logistic regression or Naive Bayes are often applied to this problem however their applicability is limited since they use bag of -words model which does not take into account the order of the words in a sentence. In this study we compare these approaches with a modern technique called recurrent neural networks using LSTM units on a dataset crawled from Turkish shopping and movie websites. Our results show that RNN based approaches improve the classification accuracies

Kronik tip a aort diseksiyonunun cerrahi tedavisinde erken dönem sonuçlar

Aim: Approximately 10% of acute type A aortic dissections become chronic due to lack of symptoms or nondiagnosis. Clinical course, surgical strategy and outcomes differ. This study aims to analyze early outcomes of surgical treatment for chronic type A aortic dissection. Materials and Methods: Forty-one patients operated for chronic type A aortic dissection between 2001 and 2014, were included in this study and the data were analyzed retrospectively. Mean age 55.9±13 years and 68% were male. The common risk factors for aortic dissection were hypertension (65%) and coronary artery disease (22%). Thirteen patients (31%) were previously operated for aortic or other cardiac procedure. Surgical incision was median sternotomy in most of patients (95%). Results: The common procedures were tube graft replacement (20 patients), valved-conduit graft replacement for aortic root (15 patients) and total arch replacement (6 patients). Deep hypothermic circulatory arrest was used in 34 patients and additional antegrad cerebral perfusion in 7 patients, as brain protection strategy. The mean duration of cardiopulmonary bypass, myocardial ischemia time and cerebral ischemia were 210.1±67, 116.3±43 and 27.6±9 min, respectively. The common complications were re-exploration for bleeding in 3 patients, need for prolonged ventilator support in 5 patients. Mean intensive care unit and hospital stay was found 4.1±5 days and 9.8±8 days, respectively. Permanent stroke was observed in one patient because of multiple embolisms. Mortality was observed in one patient (%2) due to embolic stroke. Conclusion: A low mortality rate can be achieved with appropriate strategy in the surgical treatment of chronic type A aortic dissection. Stroke seems to be the common cause of mortality.Amaç: Akut tip A aort diseksiyonlarının yaklaşık %10’u, semptomların olmaması veya tanı konulamamasına bağlı olarak kronikleşmektedir. Bu hastalarda klinik gidiş, cerrahi strateji ve sonuçlar farklılıklar göstermektedir. Bu çalışmada amaç kronik tip A aort diseksiyonu nedeniyle opere edilen hastaların erken dönem sonuçlarını analiz etmektir. Gereç ve Yöntem: Çalışmaya 2001-2014 yılları arasında, kronik tip A aort diseksiyonu nedeniyle ameliyat edilen toplam 41 hastanın verileri retrospektif olarak analiz edildi. Bu hastaların 28’ i erkek (%68) ve yaş ortalaması 55,9±13 yıl idi. Hastaların öyküsünde en sık saptanan risk faktörleri hipertansiyon (%65), koroner arter hastalığı (%22) idi. Tüm hastaların 13 tanesinde (%31,7) geçirilmiş aort veya diğer kardiyak cerrahi öyküsü mevcuttu. Cerrahi insizyon olarak hastaların çoğunda (%95) median sternotomi uygulandı. Bulgular: Cerrahi teknik olarak 20 hastaya basit tüp greft replasmanı, 15 hastada aort kökü kondüit greft replasmanı, 6 hastada total arkus replasmanı uygulandı. Beyin koruma stratejisi olarak derin hipotermik sirkülatuar arrest (%82,9), ve antegrad serebral perfüzyon (%17,1) uygulandı. Tüm hastalarda saptanan ortalama kardiyopulmoner by-pass süresi 210,1±67 dk, miyokard iskemi süresi ise 116,3±43 dk olarak saptandı. Ortalama serebral iskemi süresi 27,6±9 dakika olarak bulundu. Postoperatif 3 hastada reeksplorasyon gerektiren kanama, 5 hastada uzamış mekanik ventilatör desteği gerekmiştir. Hastaların ortalama yoğun bakım kalış süresi 4,1±5 gün iken hastanede kalış süresi 9,8±8 gün olarak bulundu. Bir hastada multipl embolilere bağlı kalıcı strok gelişti. Mortalite bir hastada (%2) görülmüş olup bu hastada ölüm nedeni embolik strok olarak kaydedilmiştir. Sonuç: Kronik tip A aort diseksiyonlu hastaların cerrahi tedavisinde düşük mortalite oranı uygun strateji ile sağlanabilir. Strok en önemli mortalite nedeni olarak göze çarpmaktadır

Using amino acid typing to improve the accuracy of NMR structure based assignments

Nuclear Magnetic Resonance (NMR1) spectroscopy is an important experimental technique that allows one to study protein structure in solution. An important challenge in NMR protein structure determination is the assignment of NMR peaks to corresponding nuclei. In structure-based assignment (SBA), the aim is to perform the assignments with the help of a homologous protein. NVR-BIP [1] is a tool that uses Nuclear Vector Replacement's (NVR) ([9], [10]) scoring function and binary integer programming to solve SBA problem. In this work, we introduce a method to improve NVR-BIP's assignment accuracy with amino acid typing. We use CRAACK that takes the chemical shifts of C, N and H atoms and returns the possible amino acids along with their confidence scores. We obtain improved assignment accuracies and our results show the effectiveness of integrating amino acid typing with NVR