1268789
Pharmacophore Discovery Using the Inductive Logic Programming System PROGOL
This paper presents a case study of a machine-aided knowledge discovery process within the general area of drug design. Within drug design, the particular problem of pharmacophore discovery is isolated, and the Inductive Logic Programming (ILP) system progol is applied to the problem of identifying potential pharmacophores for ACE inhibition. The case study reported in this paper supports four general lessons for machine learning and knowledge discovery, as well as more specific lessons for pharmacophore discovery, for Inductive Logic Programming, and for ACE inhibition. The general lessons for machine learning and knowledge discovery are as follows.1. An initial rediscovery step is a useful tool when approaching a new application domain.2. General machine learning heuristics may fail to match the details of an application domain, but it may be possible to successfully apply a heuristic-based algorithm in spite of the mismatch.3. A complete search for all plausible hypotheses can provide useful information to a user, although experimentation may be required to choose between competing hypotheses.4. A declarative knowledge representation facilitates the development and debugging of background knowledge in collaboration with a domain expert, as well as the communication of final results.
2075186
An assessment of submissions made to the Predictive Toxicology Evaluation Challenge
Constructing "good" models for chemical carcinogenesis was identified in IJCAI-97 as providing a substantial challenge to "knowledge discovery" programs. Attention was drawn to a comparative exercise which called for predictions on the outcome of 30 rodent carcinogenicity bioassays. This-the Predictive Toxicology Evaluation (or PTE) Challenge - was seen to provide AI programs with an opportunity to participate in an enterprise of scientific merit, and a yardstick for comparison against strong competition. Here we provide an assessment of the machine learning (ML) submissions made. Models submitted are assessed on: (1) their accuracy, in comparison to models developed with expert collaboration; and (2) their explanatory value for toxicology. The principal findings were: (a) using structural information available from a standard modelling package, layman-devised features, and outcomes of established biological tests, results from MLderived models were at least as good as those with expert-derived techniques. This was surprising; (b) the combined use of structural and biological features by ML-derived models was unusual, and suggested new avenues for toxicology modelling. This was also unexpected; and (c) significant effort was required to interpret the output of even the most "symbolic" of ML-derived models. Much of this could have been alleviated with measures for converting the results into a more "toxicology-friendly" form. As it stands, their absence is sufficient to prevent a whole-hearted acceptance of these promising methods by toxicologists. This suggests that ML techniques have been able to respond -not fully, but nevertheless substantially -to the PTE Challenge.
16380223
Learning Chomsky-like Grammars for Biological Sequence Families
This paper presents a new method of measuring performance when positives are rare and investigates whether Chomsky-like grammar representations are useful for learning accurate comprehensible predictors of members of biological sequence families. The positive-only learning framework of the Inductive Logic Programming (ILP) system CProgol is used to generate a grammar for recognising a class of proteins known as human neuropeptide precursors (NPPs). As far as these authors are aware, this is both the first biological grammar learnt using ILP and the first real-world scientific application of the positive-only learning framework of CProgol. Performance is measured using both predictive accuracy and a new cost function, em Relative Advantage (RA). The RA results show that searching for NPPs by using our best NPP predictor as a filter is more than 100 times more efficient than randomly selecting proteins for synthesis and testing them for biological activity. The highest RA was achieved by a model which includes grammar-derived features. This RA is significantly higher than the best RA achieved without the use of the grammar-derived features.
27782030
The Applicability to ILP of Results Concerning the Ordering of Binomial Populations
Results obtained in the 1950's on the ordering of k binomial populations are directly applicable to computing the size of data sample required to identify the best amongst k theories in an ILP search. The basic problem considered is that of selecting the best one of k (k ≥= 2) binomial populations on the basis of the observed frequency of 'success' on n observations. In the statistical formulation of the problem the experimenter specifies that he or she would like to guarantee that the probability of correct selection is at least P* (0 ≤ P* < 1) whenever the true difference between the best and second best population is at least d* (0 < d* ≤ 1). The principal result of interest is that for large values of k, the value of n required to meet any fixed specification (d*, P*) is approximately equal to some constant multiple of ln k. Depending on the k, d* and P* specified, this value of n can be substantially lower than that obtained within the corresponding PAC formulation. Applied to the search conducted by an ILP algorithm, the result guarantees with confidence level P*, that the true accuracy of the theory selected will be no more than d* below that of the best theory. In this paper, we review this basic result and test its predictions empirically. We also comment on the relevance to ILP of more recent work in the field.
16914350
Biochemical Knowledge Discovery Using Inductive Logic Programming
Machine Learning algorithms are being increasingly used for knowledge discovery tasks. Approaches can be broadly divided by distinguishing discovery of procedural from that of declarative knowledge. Client requirements determine which of these is appropriate. This paper discusses an experimental application of machine learning in an area related to drug design. The bottleneck here is in finding appropriate constraints to reduce the large number of candidate molecules to be synthesised and tested. Such constraints can be viewed as declarative specifications of the structuralel ements necessary for high medicinal activity and low toxicity. The first-order representation used within Inductive Logic Programming (ILP) provides an appropriate description language for such constraints. Within this application area knowledge accreditation requires not only a demonstration of predictive accuracy but also, and crucially, a certification of novel insight into the structuralc hemistry. This paper describes an experiment in which the ILP system Progolw as used to obtain structural constraints associated with mutagenicity of molecules. In doing so Progol found a new indicator of mutagenicity within a subset of previously published data. This subset was already known not to be amenable to statistical regression, though its complement was adequately explained by a linear model. According to the combined accuracy/explanation criterion provided in this paper, on both subsets comparative trials show that Progol's structurally-oriented hypotheses are preferable to those of other machine learning algorithms.
15982740
Carcinogenesis Predictions Using ILP
Obtaining accurate structural alerts for the causes of chemical cancers is a problem of great scientific and humanitarian value. This paper follows up on earlier research that demonstrated the use of Inductive Logic Programming (ILP) for predictions for the related problem of mutagenic activity amongst nitroaromatic molecules. Here we are concerned with predicting carcinogenic activity in rodent bioassays using data from the U.S. National Toxicology Program conducted by the National Institute of Environmental Health Sciences. The 330 chemicals used here are significantly more diverse than the previous study, and form the basis for obtaining Structure-Activity Relationships (SARs) relating molecular structure to cancerous activity in rodents. We describe the use of the ILP system Progol to obtain SARs from this data. The rules obtained from Progol are comparable in accuracy to those from expert chemists, and more accurate than most state-of-the-art toxicity prediction methods. The rules can also be interpreted to give clues about the biological and chemical mechanisms of carcinogenesis, and make use of those learnt by Progol for mutagenesis. Finally, we present details of, and predictions for, an ongoing international blind trial aimed specifically at comparing prediction methods. This trial provides ILP algorithms an opportunity to participate at the leading-edge of scientific discovery.
9234553
Lower Bounds on the Deterministic and Quantum Communication Complexities of Hamming-Distance Problems
Alice and Bob want to know if two strings of length n are almost equal. That is, do they differ on at mosta bits? Let 0≤a≤n–1. We show that any deterministic protocol, as well as any error-free quantum protocol (C* version), for this problem requires at least n–2 bits of communication. We show the same bounds for the problem of determining if two strings differ in exactlya bits. We also prove a lower bound of n/2–1 for error-free Q* quantum protocols. Our results are obtained by employing basic tools from combinatorics and calculus to lower-bound the ranks of the appropriate matrices.
16314241
End-to-end packet-scheduling in wireless ad-hoc networks
Packet-scheduling is a particular challenge in wireless networks due to interference from nearby transmissions. A distance-2 interference model serves as a useful abstraction here, and we study packet routing and scheduling under this model. The main focus of our work is the development of fully-distributed (decentralized) protocols. We present polylogarithmic/constant factor approximation algorithms for various families of disk graphs (which capture the geometric nature of wireless-signal propagation), as well as near-optimal approximation algorithms for general graphs. The packet-scheduling work by L eighton, Maggs and Rao (Combinatorica, 1994) and a basic distributed coloring procedure, originally due to Luby (J. Computer and System Sciences, 1993), underlie many of our algorithms. Experimental work of Finocchi, Panconesi, and Silvestri (SODA 2002) showed that a natural modification of Luby's algorithm leads to improved performance, and a rigorous explanation of this was left as an open question; we prove that the modified algorithm is provably better in the worst-case. Finally, using simulations, we study the impact of the routing strategy and the choice of parameters on the performance of our distributed algorithm for unit disk graphs.
38425944
Scheduling on Unrelated Machines Under Tree-Like Precedence Constraints
We present polylogarithmic approximations for the R|prec|Cmax and R|prec|∑jwjCj problems, when the precedence constraints are “treelike” – i.e., when the undirected graph underlying the precedences is a forest. We also obtain improved bounds for the weighted completion time and flow time for the case of chains with restricted assignment – this generalizes the job shop problem to these objective functions. We use the same lower bound of “congestion+dilation”, as in other job shop scheduling approaches. The first step in our algorithm for the R|prec|Cmax problem with treelike precedences involves using the algorithm of Lenstra, Shmoys and Tardos to obtain a processor assignment with the congestion + dilation value within a constant factor of the optimal. We then show how to generalize the random delays technique of Leighton, Maggs and Rao to the case of trees. For the weighted completion time, we show a certain type of reduction to the makespan problem, which dovetails well with the lower bound we employ for the makespan problem. For the special case of chains, we show a dependent rounding technique which leads to improved bounds on the weighted completion time and new bicriteria bounds for the flow time.
5089320
Better approximation guarantees for job-shop scheduling
Job-shop scheduling is a classical NP-hard problem. Shmoys, Stein, and Wein presented the first polynomial-time approximation algorithm for this problem that has a good (polylogarithmic) approximation guarantee. We improve the approximation guarantee of their work and present further improvements for some important NP-hard special cases of this problem (e.g., in the preemptive case where machines can suspend work on operations and later resume). We also present NC algorithms with improved approximation guarantees for some NP-hard special cases.
18673248
Mixed Pfair/ERfair scheduling of asynchronous periodic tasks
Pfair scheduling was proposed by Baruah, Cohen, Plaxton, and Varvel as a non-work-conserving way of optimally and efficiently scheduling periodic tasks on a multiprocessor. In this paper, we introduce a work-conserving variant of Pfair scheduling called "early-release" fair (ERfair) scheduling. We also present a new scheduling algorithm called PD2 and show that it is optimal for scheduling any mix of early-release and non-early-release asynchronous, periodic tasks. In contrast, almost all prior work on Pfair scheduling has been limited to synchronous systems. PD2 is an optimization of an earlier deadline-based algorithm of Baruah, Gehrke, and Plaxton called PD; PD2 uses a simpler tie-breaking scheme than PD to dis ambiguate equal deadlines. We present a series of counterexamples that suggest that, in general, the PD2 tie-breaking mechanism cannot be simplified. In contrast to this, we show that no tie-breaking information is needed on two-processor systems.