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Jaques Reifman

Publications and source records attributed to Jaques Reifman.

14 recordsLinked to original sources
PubMed2006

Oligonucleotide fingerprint identification for microarray-based pathogen diagnostic assays.

MOTIVATION: Advances in DNA microarray technology and computational methods have unlocked new opportunities to identify 'DNA fingerprints', i.e. oligonucleotide sequences that uniquely identify a specific genome. We present an integrated approach for the computational identification of DNA fingerprints for design of microarray-based pathogen diagnostic assays. We provide a quantifiable definition of a DNA fingerprint stated both from a computational as well as an experimental point of view, and the analytical proof that all in silico fingerprints satisfying the stated definition are found using our approach. RESULTS: The presented computational approach is implemented in an integrated high-performance computing (HPC) software tool for oligonucleotide fingerprint identification termed TOFI. We employed TOFI to identify in silico DNA fingerprints for several bacteria and plasmid sequences, which were then experimentally evaluated as potential probes for microarray-based diagnostic assays. Results and analysis of approximately 150 in silico DNA fingerprints for Yersinia pestis and 250 fingerprints for Francisella tularensis are presented. AVAILABILITY: The implemented algorithm is available upon request.

Algorithms↗
PubMed2006

Error bounds for data-driven models of dynamical systems.

This work provides a technique for estimating error bounds about the predictions of data-driven models of dynamical systems. The bootstrap technique is applied to predictions from a set of dynamical system models, rather than from the time-series data, to estimate the reliability (in the form of prediction intervals) for each prediction. The technique is illustrated using human core temperature data, modeled by a hybrid (autoregressive plus first principles) approach. The temperature prediction intervals obtained are in agreement with those from the Camp-Meidell inequality. Moreover, as expected, the prediction intervals increase with the prediction horizon, time-series data variability, and model inaccuracy.

Algorithms↗
PubMed2006

Algorithms to qualify respiratory data collected during the transport of trauma patients.

We developed a quality indexing system to numerically qualify respiratory data collected by vital-sign monitors in order to support reliable post-hoc mining of respiratory data. Each monitor-provided (reference) respiratory rate (RR(R)) is evaluated, second-by-second, to quantify the reliability of the rate with a quality index (QI(R)). The quality index is calculated from: (1) a breath identification algorithm that identifies breaths of 'typical' sizes and recalculates the respiratory rate (RR(C)); (2) an evaluation of the respiratory waveform quality (QI(W)) by assessing waveform ambiguities as they impact the calculation of respiratory rates and (3) decision rules that assign a QI(R) based on RR(R), RR(C) and QI(W). RR(C), QI(W) and QI(R) were compared to rates and quality indices independently determined by human experts, with the human measures used as the 'gold standard', for 163 randomly chosen 15 s respiratory waveform samples from our database. The RR(C) more closely matches the rates determined by human evaluation of the waveforms than does the RR(R) (difference of 3.2 +/- 4.6 breaths min(-1) versus 14.3 +/- 19.3 breaths min(-1), mean +/- STD, p < 0.05). Higher QI(W) is found to be associated with smaller differences between calculated and human-evaluated rates (average differences of 1.7 and 8.1 breaths min(-1) for the best and worst QI(W), respectively). Establishment of QI(W) and QI(R), which ranges from 0 for the worst-quality data to 3 for the best, provides a succinct quantitative measure that allows for automatic and systematic selection of respiratory waveforms and rates based on their data quality.

Algorithms↗
PubMed2006

A method for automatic identification of reliable heart rates calculated from ECG and PPG waveforms.

OBJECTIVE: The development and application of data-driven decision-support systems for medical triage, diagnostics, and prognostics pose special requirements on physiologic data. In particular, that data are reliable in order to produce meaningful results. The authors describe a method that automatically estimates the reliability of reference heart rates (HRr) derived from electrocardiogram (ECG) waveforms and photoplethysmogram (PPG) waveforms recorded by vital-signs monitors. The reliability is quantitatively expressed through a quality index (QI) for each HRr. DESIGN: The proposed method estimates the reliability of heart rates from vital-signs monitors by (1) assessing the quality of the ECG and PPG waveforms, (2) separately computing heart rates from these waveforms, and (3) concisely combining this information into a QI that considers the physical redundancy of the signal sources and independence of heart rate calculations. The assessment of the waveforms is performed by a Support Vector Machine classifier and the independent computation of heart rate from the waveforms is performed by an adaptive peak identification technique, termed ADAPIT, which is designed to filter out motion-induced noise. RESULTS: The authors evaluated the method against 158 randomly selected data samples of trauma patients collected during helicopter transport, each sample consisting of 7-second ECG and PPG waveform segments and their associated HRr. They compared the results of the algorithm against manual analysis performed by human experts and found that in 92% of the cases, the algorithm either matches or is more conservative than the human's QI qualification. In the remaining 8% of the cases, the algorithm infers a less conservative QI, though in most cases this was because of algorithm/human disagreement over ambiguous waveform quality. If these ambiguous waveforms were relabeled, the misclassification rate would drop from 8% to 3%. CONCLUSION: This method provides a robust approach for automatically assessing the reliability of large quantities of heart rate data and the waveforms from which they are derived.

Algorithms↗
PubMed2005

Classifying noisy protein sequence data: a case study of immunoglobulin light chains.

SUMMARY: The classification of protein sequences obtained from patients with various immunoglobulin-related conformational diseases may provide insight into structural correlates of pathogenicity. However, clinical data are very sparse and, in the case of antibody-related proteins, the collected sequences have large variability with only a small subset of variations relevant to the protein pathogenicity (function). On this basis, these sequences represent a model system for development of strategies to recognize the small subset of function-determining variations among the much larger number of primary structure diversifications introduced during evolution. Under such conditions, most protein classification algorithms have limited accuracy. To address this problem, we propose a support vector machine (SVM)-based classifier that combines sequence and 3D structural averaging information. Each amino acid in the sequence is represented by a set of six physicochemical properties: hydrophobicity, hydrophilicity, volume, surface area, bulkiness and refractivity. Each position in the sequence is described by the properties of the amino acid at that position and the properties of its neighbors in 3D space or in the sequence. A structure template is selected to determine neighbors in 3D space and a window size is used to determine the neighbors in the sequence. The test data consist of 209 proteins of human antibody immunoglobulin light chains, each represented by aligned sequences of 120 amino acids. The methodology is applied to the classification of protein sequences collected from patients with and without amyloidosis, and indicates that the proposed modified classifiers are more robust to sequence variability than standard SVM classifiers, improving classification error between 5 and 25% and sensitivity between 9 and 17%. The classification results might also suggest possible mechanisms for the propensity of immunoglobulin light chains to amyloid formation.

Algorithms↗
PubMed2005

Identification of genomic signatures for the design of assays for the detection and monitoring of anthrax threats.

Sequences that are present in a given species or strain while absent from or different in any other organisms can be used to distinguish the target organism from other related or un-related species. Such DNA signatures are particularly important for the identification of genetic source of drug resistance of a strain or for the detection of organisms that can be used as biological agents in warfare or terrorism. Most approaches used to find DNA signatures are laboratory based, require a great deal of effort and can only distinguish between two organisms at a time. We propose a more efficient and cost-effective bioinformatics approach that allows identification of genomic fingerprints for a target organism. We validated our approach using a custom microarray, using sequences identified as DNA fingerprints of Bacillus anthracis. Hybridization results showed that the sequences found using our algorithm were truly unique to B. anthracis and were able to distinguish B. anthracis from its close relatives B. cereus and B. thuringiensis.

Animals↗
PubMed2004

Applications and challenges of DNA microarray technology in military medical research.

This review discusses the challenges and applications of DNA microarray technology as it is being used in each of the four major research areas of the U.S. Army Medical Research and Materiel Command: military infectious diseases, combat casualty care, military operational medicine, and medical chemical and biological defense. The overall objective of this review is two-fold. First, the objective is to increase awareness in senior military leadership of the challenges and opportunities presented by DNA microarray technology and the emerging and rapidly changing field of bioinformatics. Second, the aim is to publicize to the civilian research community the additional challenges associated with the use of microarray technology in military medical research. This discussion contains material that would be useful for making programmatic recommendations that team strategic research investments and emerging technologies with U.S. Army Medical Research and Materiel Command resources.

Biological Warfare↗
PubMed2004

Alternative methods for modeling fatigue and performance.

The use of nonparametric approaches and semiparametric approaches for modeling fatigue and performance are analyzed. Nonparametric approaches in the form of stand-alone artificial neural networks and semiparametric (hybrid) approaches that combine neural networks with prior process knowledge are explored and compared with existing parametric approaches based on the two-process model of sleep regulation. Within the context of a military application, we explore two notional semiparametric approaches for real-time prediction of cognitive performance on the basis of individualized on-line measurements of physiologic variables. Initial analysis indicates that these alternative modeling approaches may address key technological gaps and advance fatigue and performance modeling. Most notably, these approaches seem amenable to predicting individual performance and quantitatively assessing the reliability of model predictions through estimation of statistical error bounds, which have eluded researchers for the last two decades.

Fatigue↗
PubMed2004

Life sign decision support algorithms.

There is a pressing need in the military for a system that interprets data from a suite of wearable physiological sensors to infer a soldier's current clinical state on the battlefield. The Warfighter Physiological Status Monitoring (WPSM) concept is envisioned by the US Army to address this need. Life sign detection is a key component. The future WPSM system will consist of a body-worn network of biosensors with a central processing control unit containing firmware for assessing the soldier's physiological status. In the present application, the system will diagnose a Dead, Alive, or Unknown (DAU) physiological state, and this information will be made available to field medics and others over separate communication channels. This paper describes the various modules in the DAU determination algorithms and how they interact with each other, as well as a simulator system built for parametric studies of the overall system.

Algorithms↗
PubMed2002

Support vector machines with selective kernel scaling for protein classification and identification of key amino acid positions.

MOTIVATION: Data that characterize primary and tertiary structures of proteins are now accumulating at a rapid and accelerating rate and require automated computational tools to extract critical information relating amino acid changes with the spectrum of functionally attributes exhibited by a protein. We propose that immunoglobulin-type beta-domains, which are found in approximate 400 functionally distinct forms in humans alone, provide the immense genetic variation within limited conformational changes that might facilitate the development of new computational tools. As an initial step, we describe here an approach based on Support Vector Machine (SVM) technology to identify amino acid variations that contribute to the functional attribute of pathological self-assembly by some human antibody light chains produced during plasma cell diseases. RESULTS: We demonstrate that SVMs with selective kernel scaling are an effective tool in discriminating between benign and pathologic human immunoglobulin light chains. Initial results compare favorably against manual classification performed by experts and indicate the capability of SVMs to capture the underlying structure of the data. The data set consists of 70 proteins of human antibody kappa1 light chains, each represented by aligned sequences of 120 amino acids. We perform feature selection based on a first-order adaptive scaling algorithm, which confirms the importance of changes in certain amino acid positions and identifies other positions that are key in the characterization of protein function.

Algorithms↗
PubMed2002

Combat medical informatics: present and future.

In this paper, we describe a U.S. Army concept to monitor soldier physiologic status and provide computer-based medical support to increase the likelihood of soldier survival on the battlefield. Supported by an underlying platform of complex wearable computerized systems, the "Warfighter Physiological Status Monitoring" (WPSM) concept consists of an array of biosensors embedded in the soldier's uniform integrated with a database management system and a decision support system that will provide assistance in casualty prevention and casualty management. We discuss the main components of the WPSM, its present status, key requirements and outstanding challenges, and near- and far-term research directions.

Biosensing Techniques↗
PubMed1997

Premature Saturation in Backpropagation Networks: Mechanism and Necessary Conditions.

The mechanism that gives rise to the phenomenon of premature saturation of the output units of feedforward multilayer neural networks during training with the standard backpropagation algorithm is described. The entire process of premature saturation is characterized by three distinct stages, and it is concluded that the momentum term plays the leading role in the occurrence of the phenomenon. The necessary conditions for the occurrence of premature saturation are presented, and a method is proposed, based on these conditions, that eliminates the occurrence of the phenomenon. The validity of the conditions and the proposed method are illustrated through simulation results. Copyright 1997 Elsevier Science Ltd.

Journal Article↗
PubMedDate not supplied

Military research needs in biomedical informatics.

The 2001 U.S. Army Medical Research and Materiel Command (USAMRMC) Biomedical Informatics Roadmap Meeting was devoted to developing a strategic plan in four focus areas: Hospital and Clinical Informatics, E-Health, Combat Health Informatics, and Bioinformatics and Biomedical Computation. The driving force of this Roadmap Meeting was the recent accelerated pace of change in biomedical informatics in which emerging technologies have the potential to affect significantly the Army research portfolio and investment strategy in these focus areas. The meeting was structured so that the first two days were devoted to presentations from experts in the field, including representatives from the three services, other government agencies, academia, and the private sector, and the morning of the last day was devoted to capturing specific biomedical informatics research needs in the four focus areas. This white paper summarizes the key findings and recommendations and should be a powerful tool for the crafting of future requests for proposals to help align USAMRMC new strategic research investments with new developments and emerging technologies.

Computational Biology↗