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PubMed2004

Decoupling dynamical systems for pathway identification from metabolic profiles.

RATIONALE: Modern molecular biology is generating data of unprecedented quantity and quality. Particularly exciting for biochemical pathway modeling and proteomics are comprehensive, time-dense profiles of metabolites or proteins that are measurable, for instance, with mass spectrometry, nuclear magnetic resonance or protein kinase phosphorylation. These profiles contain a wealth of information about the structure and dynamics of the pathway or network from which the data were obtained. The retrieval of this information requires a combination of computational methods and mathematical models, which are typically represented as systems of ordinary differential equations. RESULTS: We show that, for the purpose of structure identification, the substitution of differentials with estimated slopes in non-linear network models reduces the coupled system of differential equations to several sets of decoupled algebraic equations, which can be processed efficiently in parallel or sequentially. The estimation of slopes for each time series of the metabolic or proteomic profile is accomplished with a 'universal function' that is computed directly from the data by cross-validated training of an artificial neural network (ANN). CONCLUSIONS: Without preprocessing, the inverse problem of determining structure from metabolic or proteomic profile data is challenging and computationally expensive. The combination of system decoupling and data fitting with universal functions simplifies this inverse problem very significantly. Examples show successful estimations and current limitations of the method. AVAILABILITY: A preliminary Web-based application for ANN smoothing is accessible at http://bioinformatics.musc.edu/webmetabol/. S-systems can be interactively analyzed with the user-friendly freeware PLAS (http://correio.cc.fc.ul.pt/~aenf/plas.html) or with the MATLAB module BSTLab (http://bioinformatics.musc.edu/bstlab/), which is currently being beta-tested.

Algorithms↗

PubMed2006

Adaptive reproducing kernel particle method for extraction of the cortical surface.

We propose a novel adaptive approach based on the Reproducing Kernel Particle Method (RKPM) to extract the cortical surfaces of the brain from three-dimensional (3-D) magnetic resonance images (MRIs). To formulate the discrete equations of the deformable model, a flexible particle shape function is employed in the Galerkin approximation of the weak form of the equilibrium equations. The proposed support generation method ensures that support of all particles cover the entire computational domains. The deformable model is adaptively adjusted by dilating the shape function and by inserting or merging particles in the high curvature regions or regions stopped by the target boundary. The shape function of the particle with a dilation parameter is adaptively constructed in response to particle insertion or merging. The proposed method offers flexibility in representing highly convolved structures and in refining the deformable models. Self-intersection of the surface, during evolution, is prevented by tracing backward along gradient descent direction from the crest interface of the distance field, which is computed by fast marching. These operations involve a significant computational cost. The initial model for the deformable surface is simple and requires no prior knowledge of the segmented structure. No specific template is required, e.g., an average cortical surface obtained from many subjects. The extracted cortical surface efficiently localizes the depths of the cerebral sulci, unlike some other active surface approaches that penalize regions of high curvature. Comparisons with manually segmented landmark data are provided to demonstrate the high accuracy of the proposed method. We also compare the proposed method to the finite element method, and to a commonly used cortical surface extraction approach, the CRUISE method. We also show that the independence of the shape functions of the RKPM from the underlying mesh enhances the convergence speed of the deformable model.

Algorithms↗

PubMed2000

Automating parallel implementation of neural learning algorithms.

Neural learning algorithms generally involve a number of identical processing units, which are fully or partially connected, and involve an update function, such as a ramp, a sigmoid or a Gaussian function for instance. Some variations also exist, where units can be heterogeneous, or where an alternative update technique is employed, such as a pulse stream generator. Associated with connections are numerical values that must be adjusted using a learning rule, and and dictated by parameters that are learning rule specific, such as momentum, a learning rate, a temperature, amongst others. Usually, neural learning algorithms involve local updates, and a global interaction between units is often discouraged, except in instances where units are fully connected, or involve synchronous updates. In all of these instances, concurrency within a neural algorithm cannot be fully exploited without a suitable implementation strategy. A design scheme is described for translating a neural learning algorithm from inception to implementation on a parallel machine using PVM or MPI libraries, or onto programmable logic such as FPGAs. A designer must first describe the algorithm using a specialised Neural Language, from which a Petri net (PN) model is constructed automatically for verification, and building a performance model. The PN model can be used to study issues such as synchronisation points, resource sharing and concurrency within a learning rule. Specialised constructs are provided to enable a designer to express various aspects of a learning rule, such as the number and connectivity of neural nodes, the interconnection strategies, and information flows required by the learning algorithm. A scheduling and mapping strategy is then used to translate this PN model onto a multiprocessor template. We demonstrate our technique using a Kohonen and backpropagation learning rules, implemented on a loosely coupled workstation cluster, and a dedicated parallel machine, with PVM libraries.

Algorithms↗

PubMed2006

Automated semantic analysis of changes in image sequences of neurons in culture.

Quantitative studies of dynamic behaviors of live neurons are currently limited by the slowness, subjectivity, and tedium of manual analysis of changes in time-lapse image sequences. Challenges to automation include the complexity of the changes of interest, the presence of obfuscating and uninteresting changes due to illumination variations and other imaging artifacts, and the sheer volume of recorded data. This paper describes a highly automated approach that not only detects the interesting changes selectively, but also generates quantitative analyses at multiple levels of detail. Detailed quantitative neuronal morphometry is generated for each frame. Frame-to-frame neuronal changes are measured and labeled as growth, shrinkage, merging, or splitting, as would be done by a human expert. Finally, events unfolding over longer durations, such as apoptosis and axonal specification, are automatically inferred from the short-term changes. The proposed method is based on a Bayesian model selection criterion that leverages a set of short-term neurite change models and takes into account additional evidence provided by an illumination-insensitive change mask. An automated neuron tracing algorithm is used to identify the objects of interest in each frame. A novel curve distance measure and weighted bipartite graph matching are used to compare and associate neurites in successive frames. A separate set of multi-image change models drives the identification of longer term events. The method achieved frame-to-frame change labeling accuracies ranging from 85% to 100% when tested on 8 representative recordings performed under varied imaging and culturing conditions, and successfully detected all higher order events of interest. Two sequences were used for training the models and tuning their parameters; the learned parameter settings can be applied to hundreds of similar image sequences, provided imaging and culturing conditions are similar to the training set. The proposed approach is a substantial innovation over manual annotation and change analysis, accomplishing in minutes what it would take an expert hours to complete.

Algorithms↗

PubMed2005

Predicting protein subnuclear location with optimized evidence-theoretic K-nearest classifier and pseudo amino acid composition.

The nucleus is the brain of eukaryotic cells that guides the life processes of the cell by issuing key instructions. For in-depth understanding of the biochemical process of the nucleus, the knowledge of localization of nuclear proteins is very important. With the avalanche of protein sequences generated in the post-genomic era, it is highly desired to develop an automated method for fast annotating the subnuclear locations for numerous newly found nuclear protein sequences so as to be able to timely utilize them for basic research and drug discovery. In view of this, a novel approach is developed for predicting the protein subnuclear location. It is featured by introducing a powerful classifier, the optimized evidence-theoretic K-nearest classifier, and using the pseudo amino acid composition [K.C. Chou, PROTEINS: Structure, Function, and Genetics, 43 (2001) 246], which can incorporate a considerable amount of sequence-order effects, to represent protein samples. As a demonstration, identifications were performed for 370 nuclear proteins among the following 9 subnuclear locations: (1) Cajal body, (2) chromatin, (3) heterochromatin, (4) nuclear diffuse, (5) nuclear pore, (6) nuclear speckle, (7) nucleolus, (8) PcG body, and (9) PML body. The overall success rates thus obtained by both the re-substitution test and jackknife cross-validation test are significantly higher than those by existing classifiers on the same working dataset. It is anticipated that the powerful approach may also become a useful high throughput vehicle to bridge the huge gap occurring in the post-genomic era between the number of gene sequences in databases and the number of gene products that have been functionally characterized. The OET-KNN classifier will be available at www.pami.sjtu.edu.cn/people/hbshen.

Amino Acid Sequence↗

PubMed2006

Biclustering of gene expression data by Non-smooth Non-negative Matrix Factorization.

BACKGROUND: The extended use of microarray technologies has enabled the generation and accumulation of gene expression datasets that contain expression levels of thousands of genes across tens or hundreds of different experimental conditions. One of the major challenges in the analysis of such datasets is to discover local structures composed by sets of genes that show coherent expression patterns across subsets of experimental conditions. These patterns may provide clues about the main biological processes associated to different physiological states. RESULTS: In this work we present a methodology able to cluster genes and conditions highly related in sub-portions of the data. Our approach is based on a new data mining technique, Non-smooth Non-Negative Matrix Factorization (nsNMF), able to identify localized patterns in large datasets. We assessed the potential of this methodology analyzing several synthetic datasets as well as two large and heterogeneous sets of gene expression profiles. In all cases the method was able to identify localized features related to sets of genes that show consistent expression patterns across subsets of experimental conditions. The uncovered structures showed a clear biological meaning in terms of relationships among functional annotations of genes and the phenotypes or physiological states of the associated conditions. CONCLUSION: The proposed approach can be a useful tool to analyze large and heterogeneous gene expression datasets. The method is able to identify complex relationships among genes and conditions that are difficult to identify by standard clustering algorithms.

Algorithms↗

PubMed2005

Automated 3-dimensional elastic registration of whole-body PET and CT from separate or combined scanners.

UNLABELLED: Registration and fusion of whole-body functional PET and anatomic CT is significant for accurate differentiation of viable tumors from benign masses, radiotherapy planning and monitoring treatment response, and cancer staging. Whole-body PET and CT acquired on separate scanners are misregistered because of differences in patient positions and orientations, couch shapes, and breathing protocols. Although a combined PET/CT scanner removes many of these misalignments, breathing-related nonrigid mismatches still persist. METHODS: We have developed a new, fully automated normalized mutual information-based 3-dimensional elastic image registration technique that can accurately align whole-body PET and CT images acquired on stand-alone scanners as well as a combined PET/CT scanner. The algorithm morphs the PET image to align spatially with the CT image by generating an elastic transformation field by interpolating quaternions and translations from multiple 6-parameter rigid-body registrations, each obtained for hierarchically subdivided image subvolumes. Fifteen whole-body (spanning thorax and abdomen) PET/CT image pairs acquired separately and 5 image pairs acquired on a combined scanner were registered. The cases were selected on the basis of the availability of both CT and PET images, without any other screening criteria, such as a specific clinical condition or prognosis. A rigorous quantitative validation was performed by evaluating algorithm performance in the context of variability among 3 clinical experts in the identification of up to 32 homologous anatomic landmarks. RESULTS: The average execution time was 75 and 45 min for images acquired using separate scanners and combined scanner, respectively. Visual inspection indicated improved matching of homologous structures in all cases. The mean registration accuracy (5.5 and 5.9 mm for images from separate scanners and combined scanner, respectively) was found comparable to the mean interexpert difference in landmark identification (5.6 +/- 2.4 and 6.6 +/- 3.4 mm, respectively). The variability in landmark identification did not show statistically significant changes on replacing any expert by the algorithm. CONCLUSION: We have presented a new and automated elastic registration algorithm to correct for nonrigid misalignments in whole-body PET/CT images as well as improve the "mechanical" registration of a combined PET/CT scanner. The algorithm performance was on par with the average opinion of 3 experts.

Algorithms↗

PubMed2006

A method to improve visual similarity of breast masses for an interactive computer-aided diagnosis environment.

The purpose of this study was to develop and test a method for selecting "visually similar" regions of interest depicting breast masses from a reference library to be used in an interactive computer-aided diagnosis (CAD) environment. A reference library including 1000 malignant mass regions and 2000 benign and CAD-generated false-positive regions was established. When a suspicious mass region is identified, the scheme segments the region and searches for similar regions from the reference library using a multifeature based k-nearest neighbor (KNN) algorithm. To improve selection of reference images, we added an interactive step. All actual masses in the reference library were subjectively rated on a scale from 1 to 9 as to their "visual margins speculations". When an observer identifies a suspected mass region during a case interpretation he/she first rates the margins and the computerized search is then limited only to regions rated as having similar levels of spiculation (within +/-1 scale difference). In an observer preference study including 85 test regions, two sets of the six "similar" reference regions selected by the KNN with and without the interactive step were displayed side by side with each test region. Four radiologists and five nonclinician observers selected the more appropriate ("similar") reference set in a two alternative forced choice preference experiment. All four radiologists and five nonclinician observers preferred the sets of regions selected by the interactive method with an average frequency of 76.8% and 74.6%, respectively. The overall preference for the interactive method was highly significant (p < 0.001). The study demonstrated that a simple interactive approach that includes subjectively perceived ratings of one feature alone namely, a rating of margin "spiculation," could substantially improve the selection of "visually similar" reference images.

Algorithms↗

PubMed2004

Non-invasive coronary angiography with multislice computed tomography. Technology, methods, preliminary experience and prospects.

The recent technical developments in multislice computed tomography (MSCT), with ECG retro-gated image reconstruction, have elicited great interest in the possibility of accurate non-invasive imaging of the coronary arteries. The latest generation of MSCT systems with 8-16 rows of detectors permits acquisition of the whole cardiac volume during a single 15-20 s breath-hold with a submillimetric definition of the images and an outstanding signal-to-noise ratio. Thus the race which, between MSCT, electron beam computed tomography and cardiac magnetic resonance imaging, can best provide routine and reliable imaging of the coronary arteries in clinical practice has recommenced. Currently available MSCT systems offer different options for both cardiac image acquisition and reconstruction, including multiplanar and curved multiplanar reconstruction, three-dimensional volume rendering, maximum intensity projection, and virtual angioscopy. In our preliminary experience including 176 patients suffering from known or suspected coronary artery disease, MSCT was feasible in 161 (91.5%) and showed a sensitivity of 80.4% and a specificity of 80.3%, with respect to standard coronary angiography, in detecting critical stenosis in coronary arteries and artery or venous bypass grafts. These results correspond to a positive predictive value of 58.6% and a negative predictive value of 92.2%. The true role that MSCT is likely to play in the future in non-invasive coronary imaging is still to be defined. Nevertheless, the huge amount of data obtainable by MSCT along with the rapid technological advances, shorter acquisition times and reconstruction algorithm developments will make the technique stronger, and possible applications are expected not only for non-invasive coronary angiography, but also for cardiac function and myocardial perfusion evaluation, as an all-in-one examination.

Artificial Intelligence↗

PubMed2006

Integrated analysis of gene expression by Association Rules Discovery.

BACKGROUND: Microarray technology is generating huge amounts of data about the expression level of thousands of genes, or even whole genomes, across different experimental conditions. To extract biological knowledge, and to fully understand such datasets, it is essential to include external biological information about genes and gene products to the analysis of expression data. However, most of the current approaches to analyze microarray datasets are mainly focused on the analysis of experimental data, and external biological information is incorporated as a posterior process. RESULTS: In this study we present a method for the integrative analysis of microarray data based on the Association Rules Discovery data mining technique. The approach integrates gene annotations and expression data to discover intrinsic associations among both data sources based on co-occurrence patterns. We applied the proposed methodology to the analysis of gene expression datasets in which genes were annotated with metabolic pathways, transcriptional regulators and Gene Ontology categories. Automatically extracted associations revealed significant relationships among these gene attributes and expression patterns, where many of them are clearly supported by recently reported work. CONCLUSION: The integration of external biological information and gene expression data can provide insights about the biological processes associated to gene expression programs. In this paper we show that the proposed methodology is able to integrate multiple gene annotations and expression data in the same analytic framework and extract meaningful associations among heterogeneous sources of data. An implementation of the method is included in the Engene software package.

Algorithms↗

PubMed2003

Isotropic-sequence-order learning in a closed-loop behavioural system.

The simplest form of sensor-motor control is obtained with a reflex. In this case the reflex can be interpreted as part of a closed-loop control paradigm which measures a sensor input and generates a motor reaction as soon as the sensor signal deviates from its desired (resting) state. This is a typical case of feedback control. However, reflex reactions are tardy, because they occur always only after a (for example, unpleasant) reflex-eliciting sensor event. This defines an objective problem for an organism which can only be avoided if the corresponding motor reaction is generated earlier. The goal of this study is to design a closed-loop control situation where temporal-sequence learning supersedes a tardy reflex reaction with a proactive anticipatory action. We achieve this by employing a second, earlier-occurring and causally coupled sensor event. An appropriate motor reaction to this early event prevents triggering of the original, primary reflex. Such causally coupled sensor events are common for animals, for example when smell predicts taste or when heat radiation precedes pain. We show that trying to achieve anticipatory control is a fundamentally different goal from trying to model a classical conditioning paradigm, which is an open-loop condition. To this end, we use a novel learning rule for temporal-sequence learning called isotropic-sequence-order (ISO) learning, which performs a confounded correlation between the primary sensor signal associated to the reflex and a predictive, earlier-occurring sensor input: this way the system learns the relation between the primary reflex and the earlier sensor input in order to create an earlier-occurring motor reaction. As a consequence of learning, the primary reflex will not be triggered any more, thereby permanently remaining in its desired resting state. In a robot application, we demonstrate that ISO learning can successfully solve the classical obstacle-avoidance task by learning to correlate a built-in reflex behaviour (retraction after touching) with earlier arising signals from range finders (before touching). Finally, we show that avoidance and attraction tasks can be combined in the same agent.

Adaptation, Physiological↗

PubMed2023

The Use of ChatGPT to Assist in Diagnosing Glaucoma Based on Clinical Case Reports.

INTRODUCTION: The purpose of this study was to evaluate the capabilities of large language models such as Chat Generative Pretrained Transformer (ChatGPT) to diagnose glaucoma based on specific clinical case descriptions with comparison to the performance of senior ophthalmology resident trainees. METHODS: We selected 11 cases with primary and secondary glaucoma from a publicly accessible online database of case reports. A total of four cases had primary glaucoma including open-angle, juvenile, normal-tension, and angle-closure glaucoma, while seven cases had secondary glaucoma including pseudo-exfoliation, pigment dispersion glaucoma, glaucomatocyclitic crisis, aphakic, neovascular, aqueous misdirection, and inflammatory glaucoma. We input the text of each case detail into ChatGPT and asked for provisional and differential diagnoses. We then presented the details of 11 cases to three senior ophthalmology residents and recorded their provisional and differential diagnoses. We finally evaluated the responses based on the correct diagnoses and evaluated agreements. RESULTS: The provisional diagnosis based on ChatGPT was correct in eight out of 11 (72.7%) cases and three ophthalmology residents were correct in six (54.5%), eight (72.7%), and eight (72.7%) cases, respectively. The agreement between ChatGPT and the first, second, and third ophthalmology residents were 9, 7, and 7, respectively. CONCLUSIONS: The accuracy of ChatGPT in diagnosing patients with primary and secondary glaucoma, using specific case examples, was similar or better than senior ophthalmology residents. With further development, ChatGPT may have the potential to be used in clinical care settings, such as primary care offices, for triaging and in eye care clinical practices to provide objective and quick diagnoses of patients with glaucoma.

Artificial intelligence (AI)↗

PubMed1991

An approach to evaluating the accuracy of DXplain.

DXplain is a computer-based decision support system which generates a differential diagnosis (ddx) from a given list of clinical manifestations (Barnett et al., J. Am. Med. Assoc. 258 (1987) 67-74). An approach was developed to evaluate the accuracy of the ddx's produced by DXplain. The first step involves the collection of 65 benchmark cases drawn from a variety of sources and authors. Despite their diverse origins, the cases share in common that they are all clinical cases upon which a consulting physician might be asked to produce a differential. This helps to ensure that the evaluation of the system will be done in an environment similar to that in which the system is actually used. In the second step, all cases are reviewed by five board-certified physicians (experts) as well as DXplain. For each case, the evaluators (experts and DXplain) produce a rank-ordered ddx list along with an indication of how strongly each disease was felt to be supported by the case findings. A scoring technique was devised which rewards concordance with the gold standard: a consensus of the evaluators' ddx lists. Each evaluator receives a score which is proportional to the degree of agreement achieved with the consensus on the ddx submitted. Preliminary results on a trial evaluation of 46 cases indicate that DXplain, on average, did well in agreeing with the consensus. Agreement was achieved both in regard to the specific diagnoses listed in the ddx and the degree to which the diseases were felt to be supported by the case findings. A discussion of some important issues in the evaluation of knowledge-based systems is undertaken.

Algorithms↗

PubMed2006

Machine learning approaches to supporting the identification of photoreceptor-enriched genes based on expression data.

BACKGROUND: Retinal photoreceptors are highly specialised cells, which detect light and are central to mammalian vision. Many retinal diseases occur as a result of inherited dysfunction of the rod and cone photoreceptor cells. Development and maintenance of photoreceptors requires appropriate regulation of the many genes specifically or highly expressed in these cells. Over the last decades, different experimental approaches have been developed to identify photoreceptor enriched genes. Recent progress in RNA analysis technology has generated large amounts of gene expression data relevant to retinal development. This paper assesses a machine learning methodology for supporting the identification of photoreceptor enriched genes based on expression data. RESULTS: Based on the analysis of publicly-available gene expression data from the developing mouse retina generated by serial analysis of gene expression (SAGE), this paper presents a predictive methodology comprising several in silico models for detecting key complex features and relationships encoded in the data, which may be useful to distinguish genes in terms of their functional roles. In order to understand temporal patterns of photoreceptor gene expression during retinal development, a two-way cluster analysis was firstly performed. By clustering SAGE libraries, a hierarchical tree reflecting relationships between developmental stages was obtained. By clustering SAGE tags, a more comprehensive expression profile for photoreceptor cells was revealed. To demonstrate the usefulness of machine learning-based models in predicting functional associations from the SAGE data, three supervised classification models were compared. The results indicated that a relatively simple instance-based model (KStar model) performed significantly better than relatively more complex algorithms, e.g. neural networks. To deal with the problem of functional class imbalance occurring in the dataset, two data re-sampling techniques were studied. A random over-sampling method supported the implementation of the most powerful prediction models. The KStar model was also able to achieve higher predictive sensitivities and specificities using random over-sampling techniques. CONCLUSION: The approaches assessed in this paper represent an efficient and relatively inexpensive in silico methodology for supporting large-scale analysis of photoreceptor gene expression by SAGE. They may be applied as complementary methodologies to support functional predictions before implementing more comprehensive, experimental prediction and validation methods. They may also be combined with other large-scale, data-driven methods to facilitate the inference of transcriptional regulatory networks in the developing retina. Furthermore, the methodology assessed may be applied to other data domains.

Animals↗

PubMed2006

Outstanding scientist of the year 2006: Rogel Patawaran, BS.

Rogel Patawaran is cofounder of iQSecure Solutions, Inc., a secure web mail company, which began in 2002 and is currently based in Santa Monica, California. Its unique service enables hospitals, clinics, and doctors' offices the opportunity to have secure web-based video and telephone conferencing by simply switching online services offered by this innovative company. In addition, he is cofounder of AuthoTecq, based in Long Beach, California, and inventor of the AuthoTecq system in 1999. The AuthoTecq system is an online credit card processing company. Rogel Patawaran sought to remedy one of the problems facing users of internet transactions. Because internet merchants act as their own gateway for financial transactions, they have been storing credit card numbers in their own databases, thereby failing to address the necessary security provisions involved in the storage of such sensitive information. AuthoTecq removes this responsibility from the merchant by processing financial transactions on behalf of the merchants. Its system dramatically reduces credit card theft, and thereby decreases the amount of credit card fraud. In his landmark book Saving Lives & Saving Money, Newt Gingrich, former Speaker of House of Representatives, describes a new approach to the challenge of creating a better system of personal health and health care for the 21st century, a system that saves lives and saves money. He indicates that you could be visiting a clinic with electronic medical records, electronic laboratory reports, and electronic drug prescriptions. This entirely electronic clinic saves money and lives because it is far more accurate than a paper system. Such clinics exist at the Kaiser Permanente Hospitals, the Mayo Clinic in Jacksonville, Florida, the Cleveland Clinic, many Harvard hospitals, and all Veteran Administration hospitals in the US. He indicates that healthcare is the only industry in America that can give you a disease and then charge you to cure it. He further reports that out of 100 hospital patients, five or six will be the victim of a preventable error. He states that medication errors alone kill 7000 people each year, adding $2 billion to the overall cost of healthcare. He stresses that these 7000 needless deaths are not only unacceptable but un-American as well. During the 1990s, the pharmaceutical industry sought help from the Food and Drug Administration (FDA) to develop a standard by which a paperless system could be adopted for healthcare. In March of 1997, the FDA issued its final ruling, which established the criteria for which the FDA will recognize the transmission of authenticity within electronic data as well as establishing standards of authenticity for electronic data equivalent to the validity of signatures on paper documents and records. The FDA also recognizes the growth that can be experienced by converting healthcare technology to a paperless system, such as the expediting of patient files in a more organized manner, increasing the speed of file transfer of such documents, reducing prescription errors, enabling computer-generated data analysis and statistics, and reducing storage space. Rogel Patawaran and his colleagues also realized that electronic healthcare technology using the highest standard of data encryption to transfer data could also dramatically improve the safety of patients in our nation and the world. To answer the needs within healthcare technology, Rogel Patawaran created a system for healthcare providers using the most superior encryption methods, and in the process creating iQSolutions, Inc. Its web mail system uses the Public Key Infrastructure (PKI) format, which is the strongest known encryption format. PKI is used in combination with the Advance Encryption Standard (AES) digital encryption algorithm, which is the current algorithm used by banks and government agencies, thereby protecting the privacy and accuracy of the information. Rogel Patawaran's remarkable contributions to the encryption process will aid in the number of lives saved as well as helping to avoid medical malpractice by ensuring the accuracy of medical records, as well as reducing the number of medication errors or inappropriate prescriptions being filled. These remarkable accomplishments in patient safety are accomplished at a rapid speed that ensures that data-sensitive patient files can be securely transmitted between and within hospitals. Finally, with the growing need for an electronic system within the medical field, doctors will be able to securely access all such data from anywhere and at any time, thus reducing the age-old problem of time constraints that a paper system presented doctors and hospitals alike. In recognition of Rogel Patawaran's contributions to the medical field through the use of encryption teamed with the transferring of sensitive data such as hospital records, Rogel Patawaran has been selected as the Outstanding Scientist of 2006.

Artificial Intelligence↗

PubMed2006

Discovering semantic features in the literature: a foundation for building functional associations.

BACKGROUND: Experimental techniques such as DNA microarray, serial analysis of gene expression (SAGE) and mass spectrometry proteomics, among others, are generating large amounts of data related to genes and proteins at different levels. As in any other experimental approach, it is necessary to analyze these data in the context of previously known information about the biological entities under study. The literature is a particularly valuable source of information for experiment validation and interpretation. Therefore, the development of automated text mining tools to assist in such interpretation is one of the main challenges in current bioinformatics research. RESULTS: We present a method to create literature profiles for large sets of genes or proteins based on common semantic features extracted from a corpus of relevant documents. These profiles can be used to establish pair-wise similarities among genes, utilized in gene/protein classification or can be even combined with experimental measurements. Semantic features can be used by researchers to facilitate the understanding of the commonalities indicated by experimental results. Our approach is based on non-negative matrix factorization (NMF), a machine-learning algorithm for data analysis, capable of identifying local patterns that characterize a subset of the data. The literature is thus used to establish putative relationships among subsets of genes or proteins and to provide coherent justification for this clustering into subsets. We demonstrate the utility of the method by applying it to two independent and vastly different sets of genes. CONCLUSION: The presented method can create literature profiles from documents relevant to sets of genes. The representation of genes as additive linear combinations of semantic features allows for the exploration of functional associations as well as for clustering, suggesting a valuable methodology for the validation and interpretation of high-throughput experimental data.

Abstracting and Indexing↗

PubMed1993

Behaviorally based modeling and computational approaches to neuroscience.

The almost incredible advances that have recently occurred in the power of computers available to scientists in all disciplines have encouraged an explosion of neural network and behavioral models. Some of these have been constrained more by the imagination of the programmer than by rude biological facts. Their relevance for the experimental neuroscientist thus varies from case to case. Some models (e.g. Grillner's model of lamprey swimming movements) are so closely based on known neuroanatomy and neurophysiology that it becomes possible to generate and test precise experimental predictions. Other models (such as MURPHY and NOMAD) use neurobiological principles in their architectures, but do not portray any particular organism. Although it is harder to relate the study of these models of the study of real animals, they fulfill an important explanatory role. They make possible insights into how behavior is controlled by neuronal activity that would be unobtainable in real animals using present methods. Thus, even the excesses of neural modeling have provided a useful impetus to what is undoubtedly a most promising approach to integrating data from the various disciplines concerned with behavior and the mind. The problems have been pointed out by many authors (see citations in our introduction), and a phase of more critical evaluation appears to have begun. We hope that our brief survey of models based on widely different theoretical approaches, but all aimed at explaining behavior, will encourage critical comparisons to be made. As in more mature fields, such as thermodynamics, we can expect that more complete models will force an evaluation of theoretical hypotheses against the entire body of available evidence, rather than just a few pertinent test cases. Such evaluation will make possible a much more rigorous exclusion of invalid or inconsistent theoretical ideas. From such studies, a much smaller, but more robust, set of basic principles can be expected to emerge. From the perspective afforded by our own modeling studies, it appears essential that modeling be informed by a general theory of brain function. In this work, the theory of neuronal group selection provides a useful basis for further work by virtue of its consistency with basic evolutionary and physiological principles and the power of the selection paradigm to shape neural networks in behaviorally adaptive directions.

Animals↗

PubMed1991

Development and testing of artificial low-frequency speech codes.

In a new approach to the frequency-lowering of speech, artificial codes were developed for 24 consonants (C) and 15 vowels (V) for two values of lowpass cutoff frequency F (300 and 500 Hz). Each individual phoneme was coded by a unique, nonvarying acoustic signal confined to frequencies less than or equal to F. Stimuli were created through variations in spectral content, amplitude, and duration of tonal complexes or bandpass noise. For example, plosive and fricative sounds were constructed by specifying the duration and relative amplitude of bandpass noise with various center frequencies and bandwidths, while vowels were generated through variations in the spectral shape and duration of a ten-tone harmonic complex. The ability of normal-hearing listeners to identify coded Cs and Vs in fixed-context syllables was compared to their performance on single-token sets of natural speech utterances lowpass filtered to equivalent values of F. For a set of 24 consonants in C-/a/ context, asymptotic performance on coded sounds averaged 90 percent correct for F = 500 Hz and 65 percent for F = 300 Hz, compared to 75 percent and 40 percent for lowpass filtered speech. For a set of 15 vowels in /b/-V-/t/ context, asymptotic performance on coded sounds averaged 85 percent correct for F = 500 Hz and 65 percent for F = 300 Hz, compared to 85 percent and 50 percent for lowpass filtered speech. Identification of coded signals for F = 500 Hz was also examined in CV syllables where C was selected at random from the set of 24 Cs and V was selected at random from the set of 15 Vs. Asymptotic performance of roughly 67 percent correct and 71 percent correct was obtained for C and V identification, respectively. These scores are somewhat lower than those obtained in the fixed-context experiments. Finally, results were obtained concerning the effect of token variability on the identification of lowpass filtered speech. These results indicate a systematic decrease in percent-correct score as the number of tokens representing each phoneme in the identification tests increased from one to nine.

Evaluation Studies as Topic↗