Genetic loci influencing kidney function and chronic kidney disease

Using genome-wide association, we identify common variants at 2p12-p13, 6q26, 17q23 and 19q13 associated with serum creatinine, a marker of kidney function (P = 10 10 to 10 15). Of these, rs10206899 (near NAT8, 2p12-p13) and rs4805834 (near SLC7A9, 19q13) were also associated with chronic kidney disease (P = 5.0 × 10 5 and P = 3.6 × 10 4, respectively). Our findings provide insight into metabolic, solute and drug-transport pathways underlying susceptibility to chronic kidney disease

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 synthesisedand tested. Such constraints canbe viewed as declarative specifications of the structural elements 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 structural chemistry. Thi..

Mutagenesis: ILP experiments in a non-determinate biological domain

This paper describes the use of Inductive Logic Programming as a scientific assistant. In particular, it details the application of the ILP system Progol to discovering structural features that can result in mutagenicity in small molecules. To discover these concepts, Progol only had access to the atomic and bond structure of the molecules. With such a primitive description and no further assistance from chemists, Progol corroborated some existing knowledge and proposed a new structural alert for mutagenicity in compounds. In the process, the experiments act as a case study in which, even with extremely limited background knowledge, an Inductive Logic Programming tool firstly, complements a complex statistical model developed by skilled chemists, and secondly, continues to provide understandable theories when the statistical model fails. The experiments also constitute the first demonstrations of a prototype of the Progol system. Progol allows the construction of hypotheses with boun..

The Predictive Toxicology Evaluation Challenge

Can an AI program contribute to scientific discovery? An area where this gauntlet has been thrown is that of understanding the mechanisms of chemical carcinogenesis. One approach is to obtain Structure-Activity Relationships (SARs) relating molecular structure to cancerous activity. Vital to this are the rodent carcinogenicity tests conducted within the US National Toxicology Program by the National Institute of Environmental Health Sciences (NIEHS). This has resulted in a large database of compounds classified as carcinogens or otherwise. The Predictive-Toxicology Evaluation project of the NIEHS provides the opportunity to compare carcinogenicity predictions on previously untested chemicals. This presents a formidable challenge for programs concerned with knowledge discovery. Desirable features of this problem are: (1) involvement in genuine scientific discovery; (2) availability of a large database with expert-certified classifications; (3) strong competition from methods used by che..

Generating Uniform Incremental Grids on SO(3) Using the Hopf Fibration

Abstract. The problem of generating uniform deterministic samples over the rotation group, SO(3), is fundamental to many fields, such as computational structural biology, robotics, computer graphics, astrophysics. We present the best-known method to date for constructing incremental, deterministic grids on SO(3); it provides the: 1) lowest metric distortion for grid neighbor edges, 2) optimal dispersionreduction with each additional sample, 3) explicit neighborhood structure, and 4) equivolumentic partition of SO(3) by the grid cells. We also demonstrate the use of the sequence on motion planning problems.