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We developed an in-silico model of cancer growth to investigate the extent to which metabolic switching occurs in tumour masses. Cancer therapies based on glycoconjugation, the linking of a drug to glucose or another sugar, allow improved selectivity and targeting, thus reducing harmful side effects. This mechanism exploits the over-expression of glucose membrane transporters, a phenotypic alteration in cancer cells included in an array of metabolic alterations known as the Warburg effect. However, the extent to which tumour masses adopt the Warburg phenotype is unclear, potentially limiting the efficacy of therapies based on glycoconjugation. We simulated multiple “what-if” scenarios, each modelling increasing proportions of tumour populations that adopted the Warburg phenotype, and compared the results to the expected growth curves derived from laboratory studies. Our results suggest that the Warburg phenotype is prevalent in tumours, with the population of cancer cells adopting this phenotype significantly outnumbering that of cells that do not.

This paper presents a combination of machine learning techniques for slip detection in grasping, based on temporal features collected by low-cost tactile sensors. A slippage is an event that is subsequent to prior micro-slippages that have occurred at hand-object contact. The method is based on the application of a sequential classification technique (a variant of recurrent neural networks known as long short-term memory networks or LSTMs), whereby time-series pressure readings from tactile sensors are classified as either slip or non-slip events. We also propose a novel method for autonomous labelling, removing the need for humans in the labelling process. Lastly, this paper proposes a new design for an adaptable wearable tactile sensing device that integrates non-expensive sensors. Our proposed method achieved high accuracy in the classification of slip and non-slip events, obtaining over 95% in offline classification and 89% in online classification using a Sawyer robot.

Arabic calligraphy is one of the great art forms of the world. It displays Arabic phrases, commonly taken from the Holy Quran, in beautiful two-dimensional form. The use of two dimensions, and the interweaving of letters and words makes reading a far greater challenge for Artificial Intelligence (AI) than reading standard printed or hand-written Arabic. To approach this challenge, we have constructed a dataset of Arabic calligraphic letters, along with a corresponding corpus of phrases and quotes. The letters dataset contains a total of 3,467 images for 32 various categories of Arabic calligraphic-type letters. The associated text corpus contains 544 unique quoted phrases. These data were collected from various open sources on the web, and include examples from several Arabic calligraphic styles. We have also undertaken both an explorative statistical analysis of this data, and initial machine learning investigations. These analyses suggest that combining knowledge of a limited variety of Arabic calligraphy texts, with a successful machine will be sufficient for the machine reading of forms of Arabic calligraphy.

Traditional research into the arts has almost always been based around the subjective judgment of human critics. The use of data mining tools to understand art has great promise as it is objective and operational. We investigate the distribution of music from around the world: geographical ethnomusicology. We cast the problem as training a machine learning program to predict the geographical origin of pieces of music. This is a technically interesting problem as it has features of both classification and regression, and because of the spherical geometry of the surface of the Earth. Because of these characteristics of the representation of geographical positions, most standard classification/regression methods cannot be directly used. Two applicable methods are K-Nearest Neighbors and Random forest regression, which are robust to the non-standard structure of data. We also investigated improving performance through use of bagging. We collected 1,142 pieces of music from 73 countries/areas, and described them using 2 different sets of standard audio descriptors using MARSYAS. 10-fold cross validation was used in all experiments. The experimental results indicate that Random forest regression produces significantly better results than KNN, and the use of bagging improves the performance of KNN. The best performing algorithm achieved a mean great circle distance error of 3,113 km.

We have developed image analysis methods to analyse the morphology of the budding yeast (Saccharomyces cerevisiae) cell. Experiments were performed on four deletant strains: DYLR371w, DYDR349c, DYLR192c, and DYDR414c. Our results show that our image analysis software provides an efficient way to automatically obtain quantitative morphology features of yeast cells. Our research show significant differences from those previously published for these strains. These differences may be due to different growth conditions or the use of unfixed cells. More research is required to understand the complex relationship between genotype and environment in yeast morphology.

This paper presents a qualitative logic-based method for the steady-state analysis and revision of metabolic networks with inhibition. The approach is able to automatically revise an initial metabolic model -- through the addition and removal of whole reactions or individual substrates, products and inhibitors -- in order to ensure the existence of a steady-state behaviour consistent with a set of experimental observations. We show how this can be done in a nonmonotonic logic programming setting and discuss the challenges that arise when metabolic cycles or mutual inhibitions occur in the underlying network.

his paper describes a new logic-based approach for representing and reasoning about metabolic networks.First it shows how biological pathways can be elegantly represented in a logic programming formalism able to model full chemical reactions with substrates and products in different cell compartments, and which are catalysed by iso-enzymes or enzyme-complexes that are subject to inhibitory feedbacks.Then it shows how a nonmonotonic reasoning system called XHAIL can be used as a practical method for learning and revising such metabolic networks from observational data.Preliminary results are described in which the approach is validated on a state-of-the-art model of Aromatic Amino Acid biosynthesis.

We report on the application of an evolutionary algorithm to a noisy, dynamic optimization problem in chemistry: the maximization of three-photon absorption in molecular iodine. An evolution strategy is used in real-time in a closed loop experiment to search the space of physically realizable phase-modulated femtosecond laser pulses. The probability of three-photon absorption is estimated by measuring UV fluorescence. With the evolutionary search it is possible to enhance the UV fluorescence by a factor of 3.4 compared to the most intense pulse.

The problem of data fabrication, due to heightened consumer concerns about privacy, is on the rise. The unique characteristic of the Internet, anonymity, is a probable contributor to the intention of users to fabricate information. We propose a technological solution to this problem based on the deployment of knowledge discovery in database (KDD) systems to learn discrimination functions that discriminate between correct and fabricated data. These discrimination functions can then be used to form filters that remove falsified data from marketing data. That such discrimination functions are possible is due to the characteristic form falsified data takes. The greatest hurdle to implementing this approach is the availability of data labeled as "falsified" and "correct." However, the proposed technological solution offers potential to marketers and businesses alike

Determining the quantitative structure-activity relationship (QSAR) of a related series of drugs is a central aspect of the drug design process. The machine learning program Golem from the field of inductive logic programming (ILP) applied to QSAR. ILP is the most suitable machine learning technique because it can represent the structural and relational aspects of drugs. A five-step methodology for using machine learning in drug design is presented that consists of identification of the problem, choice of a representation, induction, interpretation of results, and synthesis of new drugs. >

The prediction of protein secondary structure from a primary sequence is one of the most important unsolved problems in molecular biology. This paper shows that the use of a machine learning algorithm (Golem) which allows relational descriptions leads to improved performance. Golem takes, as input, examples and background knowledge described as Prolog facts. It produces, as output, Prolog rules which are a generalisation of the examples. Golem was applied to learning secondary structure prediction rules for alpha domain type proteins (a subset of the Protein Data Bank rich in helical secondary structure and nearly devoid of beta sheet). Golem learned a small set of rules predicting which residues are part of alpha -helices based on their positional relationships and chemical and physical properties. This representations is more easily understood by molecular biologists. Performance of the learned rules was 81% (+/-2%). >