PubMed Health⌕ Search

Biomedical subjects

Yoram Singer

Publications and source records attributed to Yoram Singer.

8 recordsLinked to original sources
PubMed2005

Home death--the caregivers' experiences.

The objectives of this study were to evaluate caregivers' experiences concerning the care of a terminally ill loved one at home, and to compare the death experiences of caregivers with and without access to homecare programs. The primary caregivers of all the patients who died of cancer 6-18 months before the study period (1999-2001) in the Negev area were contacted. This group included 240 caregivers of patients who died in the home palliative care program and 404 caregivers of patients who died with no access to a home palliative care program. A total of 159 caregivers were interviewed, 76 from the home palliative program and 83 who had no access to a palliative care program. Death at home occurred for 80.3% of patients with access to homecare and 20.5% of those without access. Despite the fact that caring for a loved one at home was a greater financial and emotional burden, there was a greater overall satisfaction with the caring experience of those whose loved ones died at home and had access to the homecare program. Given appropriate professional support systems, home-based care at the end of life is preferable to most caregivers.

Adolescent↗
PubMed2005

Online ranking by projecting.

We discuss the problem of ranking instances. In our framework, each instance is associated with a rank or a rating, which is an integer in 1 to k. Our goal is to find a rank-prediction rule that assigns each instance a rank that is as close as possible to the instance's true rank. We discuss a group of closely related online algorithms, analyze their performance in the mistake-bound model, and prove their correctness. We describe two sets of experiments, with synthetic data and with the EachMovie data set for collaborative filtering. In the experiments we performed, our algorithms outperform online algorithms for regression and classification applied to ranking.

Algorithms↗
PubMed2005

Spikernels: predicting arm movements by embedding population spike rate patterns in inner-product spaces.

Inner-product operators, often referred to as kernels in statistical learning, define a mapping from some input space into a feature space. The focus of this letter is the construction of biologically motivated kernels for cortical activities. The kernels we derive, termed Spikernels, map spike count sequences into an abstract vector space in which we can perform various prediction tasks. We discuss in detail the derivation of Spikernels and describe an efficient algorithm for computing their value on any two sequences of neural population spike counts. We demonstrate the merits of our modeling approach by comparing the Spikernel to various standard kernels in the task of predicting hand movement velocities from cortical recordings. All of the kernels that we tested in our experiments outperform the standard scalar product used in linear regression, with the Spikernel consistently achieving the best performance.

Action Potentials↗
PubMed2003

Pain control in ambulatory cancer patients--can we do better?

To evaluate the degree of pain control among ambulatory cancer patients visiting the outpatient clinics of three oncology centers in south Israel, these patients were interviewed using the Brief Pain Inventory translated into Hebrew (BPI-Heb). Patients suffering from pain at least three times a week or reporting taking daily analgesics during the last two weeks were enrolled. Non-Hebrew speakers and patients too frail or ill were excluded. The study population included 218 subjects. Substantial pain was experienced by 77%, the majority was not adequately treated (81%), and 75% were undermedicated. The daily living activities of the majority of patients (64%) were moderately to severely impacted. Pain control was not associated with any of the sociodemographic or previous treatment profile variables, or by physicians' pain assessment. The physicians' and the patients' ratings of the extent to which pain interfered with the patients' activities fully agreed (+/-2) in fewer than half of the patients. Physicians estimated more severe pain levels, but underestimated its impact on everyday life. These data indicate that better pain control for ambulatory cancer patients is needed and that more information about patients' pain and its impact should be solicited. Further training of care providers is needed to improve the relief from cancer pain and the quality of life of patients.

Adult↗
PubMed2003

Protein family classification using sparse markov transducers.

We present a method for classifying proteins into families based on short subsequences of amino acids using a new probabilistic model called sparse Markov transducers (SMT). We classify a protein by estimating probability distributions over subsequences of amino acids from the protein. Sparse Markov transducers, similar to probabilistic suffix trees, estimate a probability distribution conditioned on an input sequence. SMTs generalize probabilistic suffix trees by allowing for wild-cards in the conditioning sequences. Since substitutions of amino acids are common in protein families, incorporating wild-cards into the model significantly improves classification performance. We present two models for building protein family classifiers using SMTs. As protein databases become larger, data driven learning algorithms for probabilistic models such as SMTs will require vast amounts of memory. We therefore describe and use efficient data structures to improve the memory usage of SMTs. We evaluate SMTs by building protein family classifiers using the Pfam and SCOP databases and compare our results to previously published results and state-of-the-art protein homology detection methods. SMTs outperform previous probabilistic suffix tree methods and under certain conditions perform comparably to state-of-the-art protein homology methods.

Algorithms↗
PubMed2002

Using substitution matrices to estimate probability distributions for biological sequences.

Accurately estimating probabilities from observations is important for probabilistic-based approaches to problems in computational biology. In this paper we present a biologically-motivated method for estimating probability distributions over discrete alphabets from observations using a mixture model of common ancestors. The method is an extension of substitution matrix-based probability estimation methods. In contrast to previous such methods, our method has a simple Bayesian interpretation and has the advantage over Dirichlet mixtures that it is both effective and simple to compute for large alphabets. The method is applied to estimate amino acid probabilities based on observed counts in an alignment and is shown to perform comparably to previous methods. The method is also applied to estimate probability distributions over protein families and improves protein classification accuracy.

Amino Acid Sequence↗