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R D King

Publications and source records attributed to R D King.

At least 19 recordsLinked to original sources
PubMed1996

Structure-activity relationships derived by machine learning: the use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming.

We present a general approach to forming structure-activity relationships (SARs). This approach is based on representing chemical structure by atoms and their bond connectivities in combination with the inductive logic programming (ILP) algorithm PROGOL. Existing SAR methods describe chemical structure by using attributes which are general properties of an object. It is not possible to map chemical structure directly to attribute-based descriptions, as such descriptions have no internal organization. A more natural and general way to describe chemical structure is to use a relational description, where the internal construction of the description maps that of the object described. Our atom and bond connectivities representation is a relational description. ILP algorithms can form SARs with relational descriptions. We have tested the relational approach by investigating the SARs of 230 aromatic and heteroaromatic nitro compounds. These compounds had been split previously into two subsets, 188 compounds that were amenable to regression and 42 that were not. For the 188 compounds, a SAR was found that was as accurate as the best statistical or neural network-generated SARs. The PROGOL SAR has the advantages that it did not need the use of any indicator variables handcrafted by an expert, and the generated rules were easily comprehensible. For the 42 compounds, PROGOL formed a SAR that was significantly (P < 0.025) more accurate than linear regression, quadratic regression, and back-propagation. This SAR is based on an automatically generated structural alert for mutagenicity.

Algorithms↗
PubMed1994

Application of machine learning to structural molecular biology.

A technique of machine learning, inductive logic programming implemented in the program GOLEM, has been applied to three problems in structural molecular biology. These problems are: the prediction of protein secondary structure; the identification of rules governing the arrangement of beta-sheets strands in the tertiary folding of proteins; and the modelling of a quantitative structure activity relationship (QSAR) of a series of drugs. For secondary structure prediction and the QSAR, GOLEM yielded predictions comparable with contemporary approaches including neural networks. Rules for beta-strand arrangement are derived and it is planned to contrast their accuracy with those obtained by human inspection. In all three studies GOLEM discovered rules that provided insight into the stereochemistry of the system. We conclude machine learning used together with human intervention will provide a powerful tool to discover patterns in biological sequences and structures.

Amino Acid Sequence↗
PubMed1994

Quantitative structure-activity relationships by neural networks and inductive logic programming. I. The inhibition of dihydrofolate reductase by pyrimidines.

Neural networks and inductive logic programming (ILP) have been compared to linear regression for modelling the QSAR of the inhibition of E. coli dihydrofolate reductase (DHFR) by 2,4-diamino-5-(substituted benzyl)pyrimidines, and, in the subsequent paper [Hirst, J.D., King, R.D. and Sternberg, M.J.E. J. Comput.-Aided Mol. Design, 8 (1994) 421], the inhibition of rodent DHFR by 2,4-diamino-6,6-dimethyl-5-phenyl-dihydrotriazines. Cross-validation trials provide a statistically rigorous assessment of the predictive capabilities of the methods, with training and testing data selected randomly and all the methods developed using identical training data. For the ILP analysis, molecules are represented by attributes other than Hansch parameters. Neural networks and ILP perform better than linear regression using the attribute representation, but the difference is not statistically significant. The major benefit from the ILP analysis is the formulation of understandable rules relating the activity of the inhibitors to their chemical structure.

Animals↗
PubMed1994

Quantitative structure-activity relationships by neural networks and inductive logic programming. II. The inhibition of dihydrofolate reductase by triazines.

One of the largest available data sets for developing a quantitative structure-activity relationship (QSAR)--the inhibition of dihydrofolate reductase (DHFR) by 2,4-diamino-6,6-dimethyl-5-phenyl-dihydrotriazine derivatives--has been used for a sixfold cross-validation trial of neural networks, inductive logic programming (ILP) and linear regression. No statistically significant difference was found between the predictive capabilities of the methods. However, the representation of molecules by attributes, which is integral to the ILP approach, provides understandable rules about drug-receptor interactions.

Animals↗
PubMed1994

On the use of machine learning to identify topological rules in the packing of beta-strands.

The machine learning program GOLEM was applied to discover topological rules in the packing of beta-sheets in alpha/beta-domain proteins. Rules (constraints) were determined for four features of beta-sheet packing: (i) whether a beta-strand is at an edge; (ii) whether two consecutive beta-strands pack parallel or anti-parallel; (iii) whether two beta-strands pack adjacently; and (iv) the winding direction of two consecutive beta-strands. Rules were found with high predictive accuracy and coverage. The errors were generally associated with complications in domain folds, especially in one doubly would domains. Investigation of the rules revealed interesting patterns, some of which were known previously, others that are novel. Novel features include (i) the relationship between pairs of sequential strands is in general one of decreasing size; (ii) more sequential pairs of strands wind in the direction out than in; and (iii) it takes a larger alteration in hydrophobicity to change a strand from winding in the direction out than in. These patterns in the data may be the result of folding pathways in the domains. The rules found are of predictive value and could be used in the combinatorial prediction of protein structure, or as a general test of model structures, e.g. those produced by threading. We conclude that machine learning has a useful role in the analysis of protein structures.

Amino Acid Sequence↗
PubMed1994

Inductive logic programming used to discover topological constraints in protein structures.

This paper describes the application of the Inductive Logic Programming (ILP) program GOLEM to the discovery of constraints in the packing of beta-sheets in alpha/beta proteins. These constraints (rules) have a role in understanding the protein folding problem. Constraints were learnt for four features of beta-sheet packing: the winding direction of two sequential strands, whether two consecutive strands pack parallel or anti-parallel, whether two strands pack adjacently, and whether a beta-strand is at an edge. Investigation of the learnt constraints revealed interesting patterns, some of which were previously known, others that were novel. Novel features include the discovery: that the relationship between pairs of sequential strands is in general one of decreasing size, and that more sequential pairs of strands wind in the direction out than the direction in. We conclude that machine learning has a useful place in molecular biology as a pattern discovery tool.

Animals↗
PubMed1993

Characteristics of Ca(2+)-activated K+ channels isolated from the left ventricle of a patient with idiopathic long QT syndrome.

Early afterdepolarizations (EADs), possibly caused by reduced K+ conductance, have been hypothesized to cause the long QTU interval and ventricular tachyarrhythmias (VT) in patients with the long QT syndrome (LQTS). In a 26-year-old woman with aborted sudden death as a consequence of the idiopathic LQTS, we recorded with a contact electrode left ventricular endocardial EADs that were enhanced by epinephrine and phenylephrine. Because of uncertain efficacy and side effects achieved with beta-adrenoceptor blockade, the patient underwent left-sided cardiac sympathectomy, at which time we obtained left ventricular biopsy tissue. Crude membrane vesicles were prepared from this tissue and single-channel activity was studied after incorporation of the vesicles in an artificial lipid bilayer (phosphatidylserine, phosphatidylethanolamine, 4:5 weight ratio in decane) in the tip of a patch clamp pipette. Bath and pipette contained 100 mmol/L KCI and 25 mmol/L N-2-hydroxyethylpiperazine-N'-2-ethanesulfonic acid (HEPES) at pH 7.4. We recorded K+ conducting channels with a mean slope conductance of 49.9 +/- 4.7 picosiemens (pS) (n = 5). Channel open probability was increased by the addition of 1 to 10 mumol/L Ca2+ to the experimental chamber. Addition of charybdotoxin (1-3 nmol/L), a known specific inhibitor of Ca(2+)-activated K+ channels, blocked channel activity. These results are the first to demonstrate Ca(2+)-activated K+ channels from a patient with idiopathic LQTS. These channels appear to show normal characteristics when studied in an artificial planar lipid bilayer.

Action Potentials↗
PubMed1992

Drug design by machine learning: the use of inductive logic programming to model the structure-activity relationships of trimethoprim analogues binding to dihydrofolate reductase.

The machine learning program GOLEM from the field of inductive logic programming was applied to the drug design problem of modeling structure-activity relationships. The training data for the program were 44 trimethoprim analogues and their observed inhibition of Escherichia coli dihydrofolate reductase. A further 11 compounds were used as unseen test data. GOLEM obtained rules that were statistically more accurate on the training data and also better on the test data than a Hansch linear regression model. Importantly machine learning yields understandable rules that characterized the chemistry of favored inhibitors in terms of polarity, flexibility, and hydrogen-bonding character. These rules agree with the stereochemistry of the interaction observed crystallographically.

Artificial Intelligence↗
PubMed1992

Modelling the structure and function of enzymes by machine learning.

A machine learning program, GOLEM, has been applied to two problems: (1) the prediction of protein secondary structure from sequence and (2) modelling a quantitative structure-activity relationship in drug design. GOLEM takes as input observations and combines them with background knowledge of chemistry to yield rules expressed as stereochemical principles for prediction. The secondary structure prediction was explored on the alpha/alpha class of proteins; on an unrelated test set it yielded 81% accuracy. The rules from GOLEM defined patterns of residues forming alpha-helices. The system studied for drug design was the activities of trimethoprim analogues binding to E. coli dihydrofolate reductase. The GOLEM rules were a better model than standard regression approaches. More importantly, these rules described the chemical properties of the enzyme-binding site that were in broad agreement with the crystallographic structure.

Amino Acid Sequence↗
PubMed1992

IPSA-Inductive Protein Structure Analysis.

The Inductive Structure Protein Analysis (IPSA) project presents a new method for investigating protein structure. IPSA includes the creation of a new database which was designed specifically for the analysis of protein structure by statistics and machine learning. The Protein Representation Language (PRL) database includes explicit and symbolic representations of geometrical, topological and chemophysical information about secondary structures and the relationships between secondary structures. The IPSA methodology consists of: the use of PRL information to produce a new database of examples of secondary structures which associate together (examples of possible super-secondary structures); then the use of a variety of clustering techniques to produce a consensus clustering of these examples (super-secondary structures); these super-secondary structures are finally examined to uncover any biological features of significance. We have applied this method to find simple super-secondary structures consisting of pairs of alpha-helices. We found four well-defined super-secondary structures, one formed exclusively by long range interactions, and another in association with an additional element of secondary structure (alpha t alpha-motif). Examinations were carried out using homologous pairs and conformational fits which confirm our clustering.

Cluster Analysis↗
PubMed1992

Protein secondary structure prediction using logic-based machine learning.

Many attempts have been made to solve the problem of predicting protein secondary structure from the primary sequence but the best performance results are still disappointing. In this paper, the use of a machine learning algorithm which allows relational descriptions is shown to lead to improved performance. The Inductive Logic Programming computer program, Golem, was applied to learning secondary structure prediction rules for alpha/alpha domain type proteins. The input to the program consisted of 12 non-homologous proteins (1612 residues) of known structure, together with a background knowledge describing the chemical and physical properties of the residues. Golem learned a small set of rules that predict which residues are part of the alpha-helices--based on their positional relationships and chemical and physical properties. The rules were tested on four independent non-homologous proteins (416 residues) giving an accuracy of 81% (+/- 2%). This is an improvement, on identical data, over the previously reported result of 73% by King and Sternberg (1990, J. Mol. Biol., 216, 441-457) using the machine learning program PROMIS, and of 72% using the standard Garnier-Osguthorpe-Robson method. The best previously reported result in the literature for the alpha/alpha domain type is 76%, achieved using a neural net approach. Machine learning also has the advantage over neural network and statistical methods in producing more understandable results.

Amino Acid Sequence↗
PubMed1992

Surgery for atrioventricular node reentry tachycardia. Results with surgical skeletonization of the atrioventricular node and discrete perinodal cryosurgery.

Surgical treatment options for interruption of atrioventricular node reentrant tachycardia include (1) skeletonization of the atrioventricular node by dissecting it from most of its atrial inputs and (2) discrete cryosurgery of the perinodal tissues by applying a series of sequential cryolesions to the atrial tissues immediately adjacent to the atrioventricular node. Both these techniques attempt to interrupt one of the dual atrioventricular node conduction pathways while preserving the other. This report describes 17 consecutive patients who underwent surgical treatment, 10 patients with skeletonization of the atrioventricular node and seven patients with discrete perinodal cryosurgery. There were 10 female and seven male patients and their ages ranged from 28 to 56 years (mean 38). Two of the 17 patients had Wolff-Parkinson-White syndrome and their accessory pathways were interrupted before the atrioventricular nodal reentrant tachycardia was ablated. All the procedures were performed in a normothermic beating heart while atrioventricular conduction was monitored closely. In the skeletonization technique, the right atrial septum was mobilized and the atrioventricular node exposed anterior to the tendon of the Todaro. The perinodal cryosurgical procedure was also performed through a right atriotomy and a series of sequential 3 mm cryolesions were placed around the borders of the triangle of Koch on the inferior right atrial septum. There were no operative deaths. Two patients who underwent the skeletonization operation had heart block necessitating pacemaker therapy. At postoperative electrophysiologic study, no echoes or atrioventricular nodal reentrant tachycardia were inducible in any of the 17 patients. All patients have remained free of arrhythmia recurrence and have required no antiarrhythmic therapy after a follow-up of 5 to 28 months (mean 14). In conclusion, both atrioventricular node skeletonization and perinodal cryosurgery successfully ablate atrioventricular nodal reentrant tachycardia; however, perinodal cryosurgery appears to be safer in avoiding heart block, is more easily performed, and is our procedure of choice for the management of medically refractory atrioventricular nodal reentrant tachycardia.

Adolescent↗
PubMed1991

Surgical myocardial revascularization for the 1990s.

After two decades, coronary artery surgery remains a reliable mainstay in the treatment of select patients suffering from ischemic heart disease. However, surgical myocardial revascularization has undergone continuous evolution. Several trends have emerged, including increased use of autogenous artery for bypass conduit, extending indications to include patients with poor ventricular function or following recent myocardial infarction and new techniques, such as surgical angioplasty of the left main coronary artery.

Humans↗
PubMed1990

Machine learning approach for the prediction of protein secondary structure.

PROMIS (protein machine induction system), a program for machine learning, was used to generalize rules that characterize the relationship between primary and secondary structure in globular proteins. These rules can be used to predict an unknown secondary structure from a known primary structure. The symbolic induction method used by PROMIS was specifically designed to produce rules that are meaningful in terms of chemical properties of the residues. The rules found were compared with existing knowledge of protein structure: some features of the rules were already recognized (e.g. amphipathic nature of alpha-helices). Other features are not understood, and are under investigation. The rules produced a prediction accuracy for three states (alpha-helix, beta-strand and coil) of 60% for all proteins, 73% for proteins of known alpha domain type, 62% for proteins of known beta domain type and 59% for proteins of known alpha/beta domain type. We conclude that machine learning is a useful tool in the examination of the large databases generated in molecular biology.

Amino Acid Sequence↗
PubMed1988

Surgical division of Wolff-Parkinson-White pathways utilizing the closed-heart technique: a 2-year experience in 47 patients.

Kent bundle interruption for ventricular preexcitation has been successfully accomplished utilizing several different surgical techniques. The external closed-heart technique of Guiraudon combining surgical dissection and cryoablation has been used to interrupt 52 accessory pathways in 47 consecutive patients since May, 1985. The 35 male and 12 female patients ranged in age from 10 to 67 years (mean, 30 years). There were 25 left free wall, 13 right free wall, 13 posterior septal, and 1 anterior septal accessory pathways. Preoperative and intraoperative electrophysiological studies were performed in all patients to induce the arrhythmia and localize all accessory pathways. The operation consisted of dissection of the atrioventricular fat pad. Following this, the delta wave and retrograde accessory pathway conduction disappeared, thereby indicating successful pathway ablation. In 4 patients with right-sided accessory pathways, interruption of the pathway required cryoablation. Cryolesions (made with cryoprobe at -60 degrees C for two minutes) were created in the region of the accessory pathway insertion. All accessory pathways were successfully ablated without any deaths or heart block. Concomitant surgical procedures were performed in 4 patients. Two patients required a second operation the next day for an accessory pathway not found at the initial operation. Three patients had postpericardiotomy syndrome, and 4 had recurrent atrial fibrillation requiring therapy. The remaining patients have had no arrhythmia recurrence and have remained drug free after a follow-up of 1 month to 22 months (mean, 12.5 months). We conclude that the closed-heart technique of accessory pathway ablation is safe and reproducible, obviates the necessity for aortic cross-clamping and cardioplegic arrest, and allows instantaneous monitoring of conduction over the pathway.

Adolescent↗
PubMed1987

Effects of hypothermia on brainstem auditory evoked potentials in humans.

Ten adult patients who underwent open heart surgery under induced hypothermia had brainstem auditory evoked potentials (BAEPs) recorded at 1 degree- to 2 degrees C-steps as body temperature was lowered from 36 degrees C to 20 degrees C to determine temperature-dependent changes. Hypothermia produced increased latencies of BAEP waves I, III, and V; the prolongation was more severe for the later components with the result that interpeak latencies I-III, III-V, and I-V were also prolonged. The temperature-latency relationship was nonlinear and best expressed by exponential curve. The latencies of waves I, III, V and the interpeak latency I-V increased roughly 7% for each 1 degree C drop; they doubled at a temperature around 26 degrees C. The amplitude of the BAEP components had a quasiparabolic relationship to temperature; the amplitude rose with hypothermia to 28 degrees or 27 degrees C, but decreased linearly with further cooling. All BAEP components were present at temperatures above 23 degrees C and absent below 20 degrees C. With rewarming, the changes reversed and BAEPs returned to initial prehypothermia status.

Acoustic Stimulation↗