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PubMed1989

Molecular recognition and information gain.

Molecular recognition is considered as an information transduction process, and information theory is applied to analyze the efficiency of the information transduction during molecular recognition. It is shown that a certain amount of information is gained in the recognition process, and it can be expressed in terms of thermodynamic quantities in the binding process of molecules. The information gain, which can be interpreted as the amount of information extracted from the sequence or structure by a molecule, represents the intrinsic ability of a molecule to recognize specific sequence or structure out of large ambient ensemble based on physical interactions. In contrast with thermodynamic quantities themselves, the information gain is a normalized quantity, and thus serves as a good measure of specificity in the molecular recognition. The information gain, which can be evaluated experimentally, enables us to identify the specific interactions and compare the specificity among different molecules quantitatively. In contrast with macroscopic recognition, specificity of molecular recognition may be severely restricted by the thermodynamic nature of molecular interactions. The thermodynamic meaning of the information gain and its biological implications to molecular recognition are discussed.

Amino Acid Sequence↗
PubMed2003

Multivariate analysis and classification of the chemical quality of 7-aminocephalosporanic acid using near-infrared reflectance spectroscopy.

The capability of near-infrared (NIR) spectroscopy in comparison to conventional chemical testing to control the chemical quality of a pharmaceutical intermediate has been investigated. Multivariate projection methods including principal component analysis, partial least-squares discriminant analysis and soft independent modeling of class analogy have been evaluated. 7-Aminocephalosporanic acid has been chosen as an example providing a large variation of quality due to its relative chemical instability. Three sets of production lots have been selected to study the extent of quality information extractable from NIR spectra. The first set of 91 lots covers a very broad range of chemical quality assessed by 8 parameters with a partially extended characterization by physical properties. The general congruence of spectral, chemical, and physical information has been investigated. The second set of 110 lots covers a very narrow range of chemical quality assessed by 11 parameters. With extended quality information, the intrinsic selectivity within the spectral data structure has been studied. The third set of 228 lots characterized by 8 parameters is a selection out of more than 1000 lots over a production period of two years. The ruggedness of the multivariate approach has been confirmed by a cross validation of the classification test.

Algorithms↗
PubMed2001

A review of the evidence for overlap among unexplained clinical conditions.

PURPOSE: Unexplained clinical conditions share features, including symptoms (fatigue, pain), disability out of proportion to physical examination findings, inconsistent demonstration of laboratory abnormalities, and an association with "stress" and psychosocial factors. This literature review examines the nature and extent of the overlap among these unexplained clinical conditions and the limitations of previous research. DATA SOURCES: English-language articles were identified by a search of the MEDLINE database from 1966 to January 2001 by using individual syndromes and their hallmark symptoms as search terms. STUDY SELECTION: Studies that assessed patients with at least one unexplained clinical condition and that included information on symptoms, overlap with other unexplained clinical conditions, or physiologic markers. Conditions examined were the chronic fatigue syndrome, fibromyalgia, the irritable bowel syndrome, multiple chemical sensitivity, temporomandibular disorder, tension headache, interstitial cystitis, and the postconcussion syndrome. DATA EXTRACTION: Information on authorship, patient and control groups, eligibility criteria, case definitions, study methods, and major findings. DATA SYNTHESIS: Many similarities were apparent in case definition and symptoms, and the proportion of patients with one unexplained clinical condition meeting criteria for a second unexplained condition was striking. Tender points on physical examination and decreased pain threshold and tolerance were the most frequent and consistent objective findings. A major shortcoming of all proposed explanatory models is their inability to account for the occurrence of unexplained clinical conditions in many affected patients. CONCLUSIONS: Overlap between unexplained clinical conditions is substantial. Most studies are limited by methodologic problems, such as case definition and the selection and recruitment of case-patients and controls.

Brain Concussion↗
PubMed2006

Paleodiet characterisation of an Etrurian population of Pontecagnano (Italy) by Isotope Ratio Mass Spectrometry (IRMS) and Atomic Absorption Spectrometry (AAS)(#).

Human bones recovered from the archaeological site of Pontecagnano (Salerno, Italy) have been studied to reconstruct the diet of an Etrurian population. Two different areas were investigated, named Library and Sant' Antonio, with a total of 44 tombs containing human skeletal remains, ranging in age from the 8th to the 3rd century B.C. This time span was confirmed by 14C dating obtained using Accelerator Mass Spectrometry (AMS) on one bone sample from each site. Atomic Absorption Spectrometry (AAS) was used to extract information about the concentration of Sr, Zn, Ca elements in the bone inorganic fraction, whilst stable isotope ratio measurements (IRMS) were carried out on bone collagen to obtain the delta13C and delta15N. A reliable technique has been used to extract and separate the inorganic and organic fractions of the bone remains. Both IRMS and AAS results suggest a mixed diet including C3 plant food and herbivore animals, consistent with archaeological indications.

Diet↗
PubMed2004

Detection of pairwise residue proximity by covariation analysis for 3D-structure prediction of G-protein-coupled receptors.

G-protein-coupled receptor (GPCR) is one of the most important targets for medicines. Homology modeling based on the crystal structure of bovine rhodopsin is currently the most frequently used method for GPCR targeted drug design. Information about residue-residue contacts and the structural specificity in the subfamily is essential for constructing more precise 3D structures, to distinguish the structural differences between the template and targets. In this study, we adopted the covariation analysis to extract information about residue-residue interactions from the amino acid sequence. In the opsin family, a large number of adjacent covarying residue pairs were detected. The detected residue pairs have a strong tendency to gather in some regions important for the structure and function. These results suggest that the covariation analysis is practically utilized to detect adjacent residue pairs and also to apply for predicting functional sites. Analyses of other GPCR subfamilies, olfactory receptor and chemokine receptor families, demonstrated that some adjacent covarying residue pairs were common. Thus, the covariation analysis has possibilities in the substantial improvement of the 3D-structure modeling of GPCRs and in the detection of functional sites such as the ligand-binding sites.

Animals↗
PubMed2005

High-recall protein entity recognition using a dictionary.

SUMMARY: Protein name extraction is an important step in mining biological literature. We describe two new methods for this task: semiCRFs and dictionary HMMs. SemiCRFs are a recently-proposed extension to conditional random fields (CRFs) that enables more effective use of dictionary information as features. Dictionary HMMs are a technique in which a dictionary is converted to a large HMM that recognizes phrases from the dictionary, as well as variations of these phrases. Standard training methods for HMMs can be used to learn which variants should be recognized. We compared the performance of our new approaches with that of Maximum Entropy (MaxEnt) and normal CRFs on three datasets, and improvement was obtained for all four methods over the best published results for two of the datasets. CRFs and semiCRFs achieved the highest overall performance according to the widely-used F-measure, while the dictionary HMMs performed the best at finding entities that actually appear in the dictionary-the measure of most interest in our intended application. AVAILABILITY: Dictionary HMMs were implemented in Java. Algorithms are available through an information extraction package MINORTHIRD on http://minorthird.sourceforge.net

Algorithms↗
PubMed2002

BRENDA, enzyme data and metabolic information.

BRENDA is a comprehensive relational database on functional and molecular information of enzymes, based on primary literature. The database contains information extracted and evaluated from approximately 46 000 references, holding data of at least 40 000 different enzymes from more than 6900 different organisms, classified in approximately 3900 EC numbers. BRENDA is an important tool for biochemical and medical research covering information on properties of all classified enzymes, including data on the occurrence, catalyzed reaction, kinetics, substrates/products, inhibitors, cofactors, activators, structure and stability. All data are connected to literature references which in turn are linked to PubMed. The data and information provide a fundamental tool for research of enzyme mechanisms, metabolic pathways, the evolution of metabolism and, furthermore, for medicinal diagnostics and pharmaceutical research. The database is a resource for data of enzymes, classified according to the EC system of the IUBMB Enzyme Nomenclature Committee, and the entries are cross-referenced to other databases, i.e. organism classification, protein sequence, protein structure and literature references. BRENDA provides an academic web access at http://www.brenda.uni-koeln.de.

Animals↗
PubMed2002

The use of visual search for knowledge gathering in image decision support.

This paper presents a new method of knowledge gathering for decision support in image understanding based on information extracted from the dynamics of saccadic eye movements. The framework involves the construction of a generic image feature extraction library, from which the feature extractors that are most relevant to the visual assessment by domain experts are determined automatically through factor analysis. The dynamics of the visual search are analyzed by using the Markov model for providing training information to novices on how and where to look for image features. The validity of the framework has been evaluated in a clinical scenario whereby the pulmonary vascular distribution on Computed Tomography images was assessed by experienced radiologists as a potential indicator of heart failure. The performance of the system has been demonstrated by training four novices to follow the visual assessment behavior of two experienced observers. In all cases, the accuracy of the students improved from near random decision making (33%) to accuracies ranging from 50% to 68%.

Algorithms↗
PubMed2006

Breast masses: computer-aided diagnosis with serial mammograms.

PURPOSE: To retrospectively evaluate effects of computer-aided diagnosis (CAD) involving an interval change classifier (which uses interval change information extracted from prior and current mammograms and estimates a malignancy rating) on radiologists' accuracy in characterizing masses on two-view serial mammograms as malignant or benign. MATERIALS AND METHODS: The data collection protocol had institutional review board approval. Patient informed consent was waived for this HIPAA-compliant retrospective study. Ninety temporal pairs of two-view serial mammograms (depicting 47 malignant and 43 benign biopsy-proved masses) were obtained from 68 patient files and were digitized. Biopsy was the reference standard. Eight Mammography Quality Standards Act of 1992-accredited radiologists and two breast imaging fellows assessed digitized two-view temporal pairs (in preselected regions of interest only) by estimating likelihood of malignancy and Breast Imaging Reporting and Data System (BI-RADS) category without and with CAD. Observers' rating data were analyzed with Dorfman-Berbaum-Metz (DBM) multireader multicase method. Statistical significance of differences was estimated with the DBM method and Student two-tailed paired t test. RESULTS: Average area under the receiver operating characteristic curve for likelihood of malignancy across the 10 observers was 0.83 (range, 0.74-0.88) without CAD and improved to 0.87 (range, 0.80-0.92) with CAD (P < .05). The average partial area index above a sensitivity of 0.90 for likelihood of malignancy was 0.35 (range, 0.13-0.54) without CAD and 0.49 (range, 0.18-0.73) with CAD--a nonsignificant improvement (P = .11). For BI-RADS assessment, it was estimated that with CAD, six radiologists would correctly recommend additional biopsies for malignant masses (range, 4.3%-10.6%) and five would correctly recommend reduction of biopsy (ie, fewer biopsies) for benign masses (range, 2.3%-9.3%). However, five radiologists would incorrectly recommend additional biopsy for benign masses (range, 2.3%-14.0%), and one would incorrectly recommend reduction of biopsy (4.3%). CONCLUSION: CAD involving interval change analysis of preselected regions of interest can significantly improve radiologists' accuracy in classifying masses on digitized screen-film mammograms as malignant or benign.

Adult↗
PubMed1995

Genetic testing in the diagnosis and management of multiple endocrine neoplasia type II.

PURPOSE: To review significant advances in the early diagnosis and treatment of medullary thyroid carcinoma in patients with the multiple endocrine neoplasia II (MEN II) syndromes, advances made possible by the application of recently discovered genetic information. DATA SOURCES: Recently published English-language literature on linkage analysis and DNA analysis in the MEN II syndromes. STUDY SELECTION: Articles on familial and sporadic forms of medullary thyroid carcinoma; pentagastrin-calcitonin determination; and genetic testing. DATA EXTRACTION: Information from recent studies on 1) the usefulness and limitations of genetic testing, especially DNA and linkage analysis, in the early diagnosis of the familial form of thyroid carcinoma and 2) the correlation between the results of genetic testing and the results of biochemical screening. DATA SYNTHESIS: Medullary thyroid carcinoma accounts for most of the morbidity and mortality among patients with the familial medullary thyroid carcinoma syndromes. Multiple endocrine neoplasia IIa and IIb and familial medullary thyroid carcinoma are inherited conditions with autosomal dominance and incomplete penetrance. Traditionally, diagnosis and screening for these conditions have been done using pentagastrin stimulation tests and plasma calcitonin determinations. Recent genetic mapping, however, has assigned the genes responsible for these tumors to the pericentromeric region of chromosome 10. Available data suggest that mutations in exon 10, 11, or 16 of the RET proto-oncogene are responsible for MEN IIa and IIb and familial non-MEN medullary thyroid carcinoma. Thus, genetic testing can identify affected members of a kindred and will probably lead to early thyroidectomy and possible cure for gene carriers. CONCLUSIONS: Early studies confirm the usefulness of DNA analysis in the diagnosis and treatment of patients with familial forms of medullary thyroid carcinoma. We review changes in the diagnosis and treatment of these patients and offer a strategy for operative intervention based on results of genetic testing.

Algorithms↗
PubMed1998

BUNDLE: a program for building the transmembrane domains of G-protein-coupled receptors.

The only information available at present about the structural features of G-protein-coupled receptors (GPCRs) comes from low resolution electron density maps of rhodopsin obtained from electron microscopy studies on 2D crystals. Despite their low resolution, maps can be used to extract information about transmembrane helix relative positions and their tilt. This information, together with a reliable algorithm to assess the residues involved in each of the membrane spanning regions, can be used to construct a 3D model of the transmembrane domains of rhodopsin at atomic resolution. In the present work, we describe an automated procedure applicable to generate such a model and, in general, to construct a 3D model of any given GPCR with the only assumption that it adopts the same helix arrangement as in rhodopsin. The present approach avoids uncertainties associated with other procedures available for constructing models of GPCRs based on a template, since sequence identity among GPCRs of different families in most of the cases is not significant. The steps involved in the construction of the model are: (i) locate the centers of the helices according to the low-resolution electron density map; (ii) compute the tilt of each helix based on the elliptical shape observed by each helix in the map; (iii) define a local coordinate system for each of the helices; (iv) bring them together in an antiparallel orientation; (v) rotate each helix through the helical axis in such a way that its hydrophobic moment points in the same direction of the bisector formed between three consecutive helices in the bundle; (vi) rotate each helix through an axis perpendicular to the helical one to assign a proper tilt; and (vii) translate each helix to its center deduced from the projection map.

Amino Acid Sequence↗
PubMed1996

Small-scale navigation in the honeybee: active acquisition of visual information about the goal

In a series of behavioural studies we found that bees use depth information extracted from self-induced image motion in several visual tasks involving pin-pointing the goal. Some of the results are reviewed here in an attempt to emphasise the active nature of this performance. They show that bees acquire depth information during free flight by employing two different strategies. One is to adapt flight behaviour, upon arrival at the food source, to the requirements of the task, a performance that is based on a learning process. The other is based on a stereotyped, innate flight pattern performed upon departure from the food source. The latter has probably evolved specifically for the acquisition of depth information.

Journal Article↗
PubMedDate not supplied

On the use of EEG features towards person identification via neural networks.

Person identification based on spectral information extracted from the EEG is addressed in this work a problem that has not yet been seen in a signal processing framework. Spectral features are extracted non-parametrically from real EEG data recorded from healthy individuals. Neural network classification is applied on these features using a Learning Vector Quantizer in an attempt to experimentally investigate the connection between a person's EEG and genetically specific information. The proposed method, compared with previously proposed methods, has yielded encouraging correct classification scores in the range of 80% to 100% (case-dependent). These results are in agreement with previous research showing evidence that the EEG carries genetic information.

Adult↗
PubMed2005

GenSo-FDSS: a neural-fuzzy decision support system for pediatric ALL cancer subtype identification using gene expression data.

OBJECTIVE: Acute lymphoblastic leukemia (ALL) is the most common malignancy of childhood, representing nearly one third of all pediatric cancers. Currently, the treatment of pediatric ALL is centered on tailoring the intensity of the therapy applied to a patient's risk of relapse, which is linked to the type of leukemia the patient has. Hence, accurate and correct diagnosis of the various leukemia subtypes becomes an important first step in the treatment process. Recently, gene expression profiling using DNA microarrays has been shown to be a viable and accurate diagnostic tool to identify the known prognostically important ALL subtypes. Thus, there is currently a huge interest in developing autonomous classification systems for cancer diagnosis using gene expression data. This is to achieve an unbiased analysis of the data and also partly to handle the large amount of genetic information extracted from the DNA microarrays. METHODOLOGY: Generally, existing medical decision support systems (DSS) for cancer classification and diagnosis are based on traditional statistical methods such as Bayesian decision theory and machine learning models such as neural networks (NN) and support vector machine (SVM). Though high accuracies have been reported for these systems, they fall short on certain critical areas. These included (a) being able to present the extracted knowledge and explain the computed solutions to the users; (b) having a logical deduction process that is similar and intuitive to the human reasoning process; and (c) flexible enough to incorporate new knowledge without running the risk of eroding old but valid information. On the other hand, a neural fuzzy system, which is synthesized to emulate the human ability to learn and reason in the presence of imprecise and incomplete information, has the ability to overcome the above-mentioned shortcomings. However, existing neural fuzzy systems have their own limitations when used in the design and implementation of DSS. Hence, this paper proposed the use of a novel neural fuzzy system: the generic self-organising fuzzy neural network (GenSoFNN) with truth-value restriction (TVR) fuzzy inference, as a fuzzy DSS (denoted as GenSo-FDSS) for the classification of ALL subtypes using gene expression data. RESULTS AND CONCLUSION: The performance of the GenSo-FDSS system is encouraging when benchmarked against those of NN, SVM and the K-nearest neighbor (K-NN) classifier. On average, a classification rate of above 90% has been achieved using the GenSo-FDSS system.

Algorithms↗
PubMed2005

Energy loss spectroscopic profiling across linear interfaces: the example of amorphous carbon superlattices.

Energy loss spectroscopic profiling is a way to acquire, in parallel, spectroscopic information across a linear feature of interest, using a Gatan imaging filter (GIF) fitted to a transmission electron microscope (TEM). This technique is capable of translating the high spatial resolution of a bright field image into a sampling of the spectral information with similar resolution. Here we evaluate the contributions of chromatic aberration and the various acquisition parameters to the spatial sampling resolution of the spectral information, and show that this can reach 0.5 nm, in a system not ordinarily capable of forming electron probes smaller than 2 nm. We use this high spatial sampling resolution to study the plasmon energy variation across amorphous carbon superlattices, in order to extract information about their structure and electronic properties. By modelling the interaction of the relativistic incident electrons with a dielectric layer sandwiched between outer layers, we show that, due to the screening of the interfaces and at increased collection angles, the plasmon energy in the sandwiched layer can still be identified for layer thicknesses down to 5 A. This allows us to measure the change in the well bandgap as a function of well width and to interpret it in terms of the changes in the sp2 -fractions due to the deposition method, as measured from the carbon K-edges, and in terms of quantum confinement of the well wavefunction by the adjacent barriers.

Journal Article↗
PubMed2004

Predicting protein functions with message passing algorithms.

MOTIVATION: In the last few years, a growing interest in biology has been shifting toward the problem of optimal information extraction from the huge amount of data generated via large-scale and high-throughput techniques. One of the most relevant issues has recently emerged that of correctly and reliably predicting the functions of a given protein with that of functions exploiting information coming from the whole network of proteins physically interacting with the functionally undetermined one. In the present work, we will refer to an 'observed' protein as the one present in the protein-protein interaction networks published in the literature. METHODS: The method proposed in this paper is based on a message passing algorithm known as Belief Propagation, which accepts the network of protein's physical interactions and a catalog of known protein's functions as input, and returns the probabilities for each unclassified protein of having one chosen function. The implementation of the algorithm allows for fast online analysis, and can easily be generalized into more complex graph topologies taking into account hypergraphs, i.e. complexes of more than two interacting proteins. RESULTS: Benchmarks of our method are the two Saccharomyces cerevisiae protein-protein interaction networks and the Database of Interacting Proteins. The validity of our approach is successfully tested against other available techniques. CONTACT: leone@isiosf.isi.it SUPPLEMENTARY INFORMATION: http://isiosf.isi.it/~pagnani

Algorithms↗
PubMedDate not supplied

Central auditory system.

A biological view of the central auditory system seems to be replacing the older Galilean or physics-based view as the guide for research in the late 20th century. This emerging view is primarily Darwinian in origin and recognizes that the entire auditory system is an adaptation for extracting information from natural sounds and their sources, usually for the guidance of immediate behavioral action. The fact that natural sounds themselves are almost always transients, too brief in duration to have tonal quality, suggests that the ear is far more of a Fast Fourier Transformer (and less of a Fourier analyzer) than is usually explicitly stated. Since the brief sounds constituting most of the acoustical environment contain critical information for guiding behavioral activity, several further hypotheses regarding the cell-level and tissue-level contributions of the central auditory system also follow. For example, the new view suggests that the central auditory system's role is likely to include the extraction of the behaviorally most important features, aspects, or dimensions of the sources of sounds in addition to, or possibly instead of, the physical dimensions of the sounds themselves. Because this new view has already enjoyed some measure of success and has also suggested a number of new and unusual directions for future research, it may become the dominant theory for research in the 21st century.

Animals↗
PubMed1997

New approach to studies on ECG dynamics: extraction and analyses of QRS complex irregularity time series.

How to extract information intensively from ECGs for the diagnosis of cardiovascular diseases and assessment of heart function is a topical subject. Using a method based on the wavelet transform to calculate the irregularity of the QRS complex, which may relate to inotropy, the QRS complex irregularity time series is successfully extracted from original ECG signals. This provides a new approach to studies of ECG dynamics. With the help of non-linear dynamics theory, the QRS complex irregularity time series of eight subjects from the MIT/BIH arrhythmia database are studied qualitatively and quantitatively, and the characteristics of ECG dynamics are analysed extensively. The power spectrum, phase portrait, correlation dimension, largest Lyapunov exponent, time-dependent divergence exponent and complexity measure all verify the fact that ECG dynamics are dominated by an underlying 5-6-dimensional non-linear chaotic system, whose complexity measure is about 0.7. The QRS complex irregularity time series contains abundant information about all parts of the heart and the regulation of the autonomic nervous system, and so further analyses are of great potential theoretical and clinical significance to patho-physiology studies and ambulatory monitoring.

Cardiovascular Diseases↗