سخنرانیهای گروه آمار
مدل بندی جمعی توابع چگالی و کاربرد آن در مسائل کلاسبندی و پیشگویی ساختار پروتئین
توسط جناب آقای دکتر مهدی معدولیت از دانشگاه مارکوئیت ایالات متحده در تاریخ 11 خرداد ماه 1395 برگزار شد.
Abstract: "This talk develops a method for simultaneous estimation of density functions for a collection of populations of protein backbone angle pairs using a data-driven, shared basis that is constructed by bivariate spline functions defined on a triangulation of the bivariate domain. The circular nature of angular data is taken into account by imposing appropriate smoothness constraints across boundaries of the triangles. Maximum penalized likelihood is used to fit the model and an alternating block-wise Newton-type algorithm is developed for computation. A simulation study shows that the collective estimation approach is statistically more efficient than estimating the densities individually. The proposed method was used to estimate neighbor-dependent distributions of protein backbone dihedral angles (i.e., Ramachandran distributions). The estimated distributions were applied to protein loop modeling, one of the most challenging open problems in protein structure prediction, by feeding them into an angular-sampling-based loop structure prediction framework. Our estimated distributions compared favorably to the Ramachandran distributions estimated by fitting a hierarchical Dirichlet process model; and in particular, our distributions showed significant improvements on the hard cases where existing methods do not work well."
پلی ما بین فرآیندهای نقطهای فضایی و علوم شناختی
توسط سرکار خانم دکتر فرزانه صفوی منش از دانشگاه شهید بهشتی در تاریخ 4 اسفند ماه 1394 برگزار شد.
Abstract:Analyzing point patterns with linear structures has recently been of interest in e.g. neuroscience and geography. To detect anisotropy in such cases, we introduce a functional summary statistic, called the cylindrical K-function, since it is a directional K-function whose structuring element is a cylinder. Further we introduce a class of anisotropic Cox point processes, called Poisson line cluster point processes. The points of such a process are random displacements of Poisson point processes defined on the lines of a Poisson line process. Parameter estimation based on moment methods or Bayesian inference for this model is discussed when the underlying Poisson line process and the cluster memberships are treated as hidden processes. To illustrate the methodologies, we analyze a two and a three-dimensional point pattern data set. The 3D data set is of particular interest as it relates to the minicolumn hypothesis in neuroscience, claiming that pyramidal and other brain cells have a columnar arrangement perpendicular to the pial surface of the brain.
پیشبینی موقعیت استقرار پروتئینها در اجزای زیرسلولی با استفاده از رویکرد پیشنهاد شخصی براساس شبکههای دوبخشی
توسط جناب آقای دکتر چنگیز اصلاحچی از دانشگاه شهید بهشتی در تاریخ 28 فروردین ماه 1395 برگزار شد.
Motivation: The importance of protein subcellular localization problem is due to the importance of protein’s functions in different cell parts. Moreover Prediction of subcellular locations helps to identify the potential molecular targets for drugs and has an important role in genome annotation. Most of the existing prediction methods assign only one location for each protein. Since proteins move between different subcellular locations, each protein can have multiple subcellular locations. In recent years, some multiple location predictors have been introduced. However, their performances are not accurate enough and there is much room for improvement. Results: In this paper we introduced a method, pmLplr, to predict locations for a protein. In pmLplr, we use a personal recommender method, NBI, to tackle the location prediction problem. In order to predict locations for each protein, the similarity of the proteins considered. This similarity is derived from STRING, protein-protein interaction database. PmLplr predicts a list of locations for each protein and it can properly overcome the multiple location prediction problem. For evaluating the performance of PmLplr, we considered two datasets, proteins of HUMAN and RAT. The performance of this algorithm is compared with three state-of-the-art algorithms, YLoc, WOLF-PSORT and prediction channel. The results indicate that our proposed method is significantly superior compare to the mentioned methods.
Variable selection in finite mixture of survival models for biomedical genomic studies.
دکتر فرهاد شکوهی از دانشگاه مکگیل کانادا ، مورخ 03/07/95
تاریخچه نظریه اطلاع
دکتر غلامرضا محتشمی برزادران از دانشگاه فردوسی مشهد، مورخ 28/07/95
اندازه گیری گذر از مدرسه به کار
دکتر فرهاد مهران از دانشگاه نوشاتل سوئیس، مورخ 30/09/95
مدلسازی مطالعات طولی
"دکتر سمانه افتخاری مهابادی" عضو هیأت علمی دانشگاه تهران، مورخ 26/11/95
Incorporating Prior Information into the Problem of Multiple Hypothesis Testing with Application to the Analysis of Genetic Association Data
"دکترعلی کریمنژاد" عضو هیأت علمی دانشگاه صنعتی خواجه نصیرالدین طوسی، 95/12/17
An important objective in simultaneous hypothesis testing terminology is to control false discoveries. In most of studies in the literature, local false discovery rates (LFDRs) are estimated based on a combined analysis in which all features presented together are combined to be analyzed together. However, sometimes the nature of data or presence of some covariates suggests a separate analysis in which features are assigned to some reference classes, leading to more reliable estimates of LFDRs. Estimating LFDRs corresponding to single nucleotide polymorphisms (SNPs) associated with the coronary artery disease is a promising example in which, based on some available biological information, SNPs can be assigned to some biological reference classes such as exonic and ncRNA. Conducting both separate and combined analyses might lead to two different decisions based on estimated values of LFDRs. Thus, it is a debatable issue to determine if any of the SNPs is associated with the disease. In this talk, we introduce novel approaches including robust Bayes and information-theoretic approaches to estimate LFDRs, overcoming with the uncertainty in making a wise decision based on both separate and combined analysis.
Confidence Interval in Randomized Nomination Sampling
"دکتر محمد نورمحمدی" رئیس پژوهشکده آمار، مورخ 96/02/12
Machine Learner Fusion for Regression Problems
"دکتر علی شمسالدینی" عضو هیأت علمی دانشگاه تربیت مدرس، مورخ 96/03/02