Publications
74 results found
Eaton JW, Menzies NA, Stover J, et al., 2014, Health benefits, costs, and cost-effectiveness of earlier eligibility for adult antiretroviral therapy and expanded treatment coverage: a combined analysis of 12 mathematical models, The Lancet Global Health, Vol: 2, Pages: E23-E34, ISSN: 2214-109X
BackgroundNew WHO guidelines recommend initiation of antiretroviral therapy for HIV-positive adults with CD4 counts of 500 cells per μL or less, a higher threshold than was previously recommended. Country decision makers have to decide whether to further expand eligibility for antiretroviral therapy accordingly. We aimed to assess the potential health benefits, costs, and cost-effectiveness of various eligibility criteria for adult antiretroviral therapy and expanded treatment coverage.MethodsWe used several independent mathematical models in four settings—South Africa (generalised epidemic, moderate antiretroviral therapy coverage), Zambia (generalised epidemic, high antiretroviral therapy coverage), India (concentrated epidemic, moderate antiretroviral therapy coverage), and Vietnam (concentrated epidemic, low antiretroviral therapy coverage)—to assess the potential health benefits, costs, and cost-effectiveness of various eligibility criteria for adult antiretroviral therapy under scenarios of existing and expanded treatment coverage, with results projected over 20 years. Analyses assessed the extension of eligibility to include individuals with CD4 counts of 500 cells per μL or less, or all HIV-positive adults, compared with the previous (2010) recommendation of initiation with CD4 counts of 350 cells per μL or less. We assessed costs from a health-system perspective, and calculated the incremental cost (in US$) per disability-adjusted life-year (DALY) averted to compare competing strategies. Strategies were regarded very cost effective if the cost per DALY averted was less than the country's 2012 per-head gross domestic product (GDP; South Africa: $8040; Zambia: $1425; India: $1489; Vietnam: $1407) and cost effective if the cost per DALY averted was less than three times the per-head GDP.FindingsIn South Africa, the cost per DALY averted of extending eligibility for antiretroviral therapy to adult patients with CD4 counts of 500 cells per &
Pretorius C, Menzies NA, Chindelevitch L, et al., 2014, The potential effects of changing HIV treatment policy on tuberculosis outcomes in South Africa: results from three tuberculosis-HIV transmission models, AIDS, Vol: 28, Pages: S25-S34, ISSN: 0269-9370
- Author Web Link
- Cite
- Citations: 33
Chindelevitch L, Ma C-Y, Liao C-S, et al., 2013, Optimizing a global alignment of protein interaction networks, Bioinformatics, Vol: 29, Pages: 2765-2773, ISSN: 1367-4803
Motivation: The global alignment of protein interaction networks is a widely studied problem. It is an important first step in understanding the relationship between the proteins in different species and identifying functional orthologs. Furthermore, it can provide useful insights into the species’ evolution.Results: We propose a novel algorithm, PISwap, for optimizing global pairwise alignments of protein interaction networks, based on a local optimization heuristic that has previously demonstrated its effectiveness for a variety of other intractable problems. PISwap can begin with different types of network alignment approaches and then iteratively adjust the initial alignments by incorporating network topology information, trading it off for sequence information. In practice, our algorithm efficiently refines other well-studied alignment techniques with almost no additional time cost. We also show the robustness of the algorithm to noise in protein interaction data. In addition, the flexible nature of this algorithm makes it suitable for different applications of network alignment. This algorithm can yield interesting insights into the evolutionary dynamics of related species.Availability: Our software is freely available for non-commercial purposes from our Web site, http://piswap.csail.mit.edu/.Contact:bab@csail.mit.edu or csliao@ie.nthu.edu.twSupplementary information:Supplementary data are available at Bioinformatics online.
Chindelevitch L, Ziemek D, Enayetallah A, et al., 2012, Causal reasoning on biological networks: interpreting transcriptional changes, BIOINFORMATICS, Vol: 28, Pages: 1114-1121, ISSN: 1367-4803
- Author Web Link
- Cite
- Citations: 99
Huang C-L, Lamb J, Chindelevitch L, et al., 2012, Correlation set analysis: detecting active regulators in disease populations using prior causal knowledge, BMC Bioinformatics, Vol: 13, Pages: 46-46, ISSN: 1471-2105
Background: Identification of active causal regulators is a crucial problem in understanding mechanism of diseasesor finding drug targets. Methods that infer causal regulators directly from primary data have been proposed andsuccessfully validated in some cases. These methods necessarily require very large sample sizes or a mix ofdifferent data types. Recent studies have shown that prior biological knowledge can successfully boost a method’sability to find regulators.Results: We present a simple data-driven method, Correlation Set Analysis (CSA), for comprehensively detectingactive regulators in disease populations by integrating co-expression analysis and a specific type of literaturederived causal relationships. Instead of investigating the co-expression level between regulators and theirregulatees, we focus on coherence of regulatees of a regulator. Using simulated datasets we show that ourmethod performs very well at recovering even weak regulatory relationships with a low false discovery rate. Usingthree separate real biological datasets we were able to recover well known and as yet undescribed, activeregulators for each disease population. The results are represented as a rank-ordered list of regulators, and revealsboth single and higher-order regulatory relationships.Conclusions: CSA is an intuitive data-driven way of selecting directed perturbation experiments that are relevantto a disease population of interest and represent a starting point for further investigation. Our findingsdemonstrate that combining co-expression analysis on regulatee sets with a literature-derived network cansuccessfully identify causal regulators and help develop possible hypothesis to explain disease progression.
Chindelevitch L, Loh P-R, Enayetallah A, et al., 2012, Assessing statistical significance in causal graphs, BMC Bioinformatics, Vol: 13, Pages: 35-35, ISSN: 1471-2105
BackgroundCausal graphs are an increasingly popular tool for the analysis of biological datasets. In particular, signed causal graphs--directed graphs whose edges additionally have a sign denoting upregulation or downregulation--can be used to model regulatory networks within a cell. Such models allow prediction of downstream effects of regulation of biological entities; conversely, they also enable inference of causative agents behind observed expression changes. However, due to their complex nature, signed causal graph models present special challenges with respect to assessing statistical significance. In this paper we frame and solve two fundamental computational problems that arise in practice when computing appropriate null distributions for hypothesis testing.ResultsFirst, we show how to compute a p-value for agreement between observed and model-predicted classifications of gene transcripts as upregulated, downregulated, or neither. Specifically, how likely are the classifications to agree to the same extent under the null distribution of the observed classification being randomized? This problem, which we call "Ternary Dot Product Distribution" owing to its mathematical form, can be viewed as a generalization of Fisher's exact test to ternary variables. We present two computationally efficient algorithms for computing the Ternary Dot Product Distribution and investigate its combinatorial structure analytically and numerically to establish computational complexity bounds.Second, we develop an algorithm for efficiently performing random sampling of causal graphs. This enables p-value computation under a different, equally important null distribution obtained by randomizing the graph topology but keeping fixed its basic structure: connectedness and the positive and negative in- and out-degrees of each vertex. We provide an algorithm for sampling a graph from this distribution uniformly at random. We also highlight theoretical challenges unique to sign
Chindelevitch L, Stanley S, Hung D, et al., 2012, MetaMerge: scaling up genome-scale metabolic reconstructions, with application to Mycobacterium tuberculosis, Genome Biology, Vol: 13, Pages: R6-R6, ISSN: 1465-6906
Reconstructed models of metabolic networks are widely used for studying metabolism in various organisms. Many different reconstructions of the same organism often exist concurrently, forcing researchers to choose one of them at the exclusion of the others. We describe MetaMerge, an algorithm for semi-automatically reconciling a pair of existing metabolic network reconstructions into a single metabolic network model. We use MetaMerge to combine two published metabolic networks for Mycobacterium tuberculosis into a single network, which allows many reactions that could not be active in the individual models to become active, and predicts essential genes with a higher positive predictive value.
Chindelevitch L, Regev A, Berger B, 2010, Metabolic Network Analysis Demystified, INTERNATIONAL CONFERENCE ON RESEARCH IN COMPUTATIONAL MOLECULAR BIOLOGY
Chindelevitch L, Ziemek D, Enayetallah A, et al., 2010, Causal Reasoning on Biological Networks: Interpreting Transcriptional Changes, INTERNATIONAL CONFERENCE ON RESEARCH IN COMPUTATIONAL MOLECULAR BIOLOGY
Chindelevitch L, Liao C-S, Berger B, 2009, Local optimization for global alignment of protein interaction networks, Pacific Symposium on Biocomputing 2010, Publisher: World Scientific, Pages: 123-132
We propose a novel algorithm, PISwap, for computing global pairwise alignments of protein interaction networks, based on a local optimization heuristic that has previously demonstrated its effectiveness for a variety of other NP-hard problems, such as the Traveling Salesman Problem. Our algorithm begins with a sequence-based network alignment and then iteratively adjusts the alignment by incorporating network structure information. It has a worst-case pseudo-polynomial running-time bound and is very efficient in practice. It is shown to produce improved alignments in several well-studied cases. In addition, the flexible nature of this algorithm makes it suitable for different applications of network alignments. Finally, this algorithm can yield interesting insights into the evolutionary history of the compared species.
Schnall-Levin M, Chindelevitch L, Berger B, 2008, Inverting the viterbi algorithm: An abstract framework for structure design, Pages: 904-911
Probabilistic grammatical formalisms such as hidden Markov models (HMMs) and stochastic context-free grammars (SCFGs) have been extensively studied and widely applied in a number of fields. Here, we introduce a new algorithmic problem on HMMs and SCFGs that arises naturally from protein and RNA design, and which has not been previously studied. The problem can be viewed as an inverse to the one solved by the Viterbi algorithm on HMMs or by the CKY algorithm on SCFGs. We study this problem theoretically and obtain the first algorithmic results. We prove that the problem is NP-complete, even for a 3-letter emission alphabet, via a reduction from 3-SAT, a result that has implications for the hardness of RNA secondary structure design. We then develop a number of approaches for making the problem tractable. In particular, for HMMs we develop a branch-and-bound algorithm, which can be shown to have fixed-parameter tractable worst-case running time, exponential in the number of states of the HMM but linear in the length of the structure. We also show how to cast the problem as a Mixed Integer Linear Program. Copyright 2008 by the author(s)/owner(s).
Schnall-Levin M, Chindelevitch L, Berger B, 2008, Inverting the Viterbi algorithm, the 25th international conference, Publisher: ACM Press
Chindelevitch L, Nicholls DP, Nigam N, 2007, Error analysis and preconditioning for an enhanced DtN-FE algorithm for exterior scattering problems, JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS, Vol: 204, Pages: 493-504, ISSN: 0377-0427
- Author Web Link
- Cite
- Citations: 5
CHINDELEVITCH L, LI Z, BLAIS E, et al., 2006, On the inference of parsimonious indel evolutionary scenarios, Journal of Bioinformatics and Computational Biology, Vol: 04, Pages: 721-744, ISSN: 0219-7200
Given a multiple alignment of orthologous DNA sequences and a phylogenetic tree for these sequences, we investigate the problem of reconstructing a most parsimonious scenario of insertions and deletions capable of explaining the gaps observed in the alignment. This problem, called the Indel Parsimony Problem, is a crucial component of the problem of ancestral genome reconstruction, and its solution provides valuable information to many genome functional annotation approaches. We first show that the problem is NP-complete. Second, we provide an algorithm, based on the fractional relaxation of an integer linear programming formulation. The algorithm is fast in practice, and the solutions it produces are, in most cases, provably optimal. We describe a divide-and-conquer approach that makes it possible to solve very large instances on a simple desktop machine, while retaining guaranteed optimality. Our algorithms are tested and shown efficient and accurate on a set of 1.8 Mb mammalian orthologous sequences in the CFTR region.
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.