Applying machine learning to catalogue matching in astrophysics

We present the results of applying automated machine learning techniques to the problem of matching different object catalogues in astrophysics. In this study, we take two partially matched catalogues where one of the two catalogues has a large positional uncertainty. The two catalogues we used here were taken from the H I Parkes All Sky Survey (HIPASS) and SuperCOSMOS optical survey. Previous work had matched 44 per cent (1887 objects) of HIPASS to the SuperCOSMOS catalogue. A supervised learning algorithm was then applied to construct a model of the matched portion of our catalogue. Validation of the model shows that we achieved a good classification performance (99.12 per cent correct). Applying this model to the unmatched portion of the catalogue found 1209 new matches. This increases the catalogue size from 1887 matched objects to 3096. The combination of these procedures yields a catalogue that is 72 per cent matched.

Machine learning for matching astronomy catalogues

An emerging issue in the field of astronomy is the integration, management and utilization of databases from around the world to facilitate scientific discovery. In this paper, we investigate application of the machine learning techniques of support vector machines and neural networks to the problem of amalgamating catalogues of galaxies as objects from two disparate data sources: radio and optical. Formulating this as a classification problem presents several challenges, including dealing with a highly unbalanced data set. Unlike the conventional approach to the problem (which is based on a likelihood ratio) machine learning does not require density estimation and is shown here to provide a significant improvement in performance. We also report some experiments that explore the importance of the radio and optical data features for the matching problem.

A machine learning approach to intraday trading on foreign exchange markets

Foreign Exchange trading has emerged in recent times as a significant activity in many countries. As with most forms of trading, the activity is influenced by many random parameters so that the creation of a system that effectively emulates the trading process will be very helpful. In this paper we try to create such a system using Machine learning approach to emulate trader behaviour on the Foreign Exchange market and to find the most profitable trading strategy.

Treatment of non-resectable hepatocellular carcinoma with autologous tumor-pulsed dendritic cells

Background: The response of hepatocellular carcinoma (HCC) to therapy is often disappointing and new modalities of treatment are clearly needed. Active immunotherapy based on the injection of autologous dendritic cells (DC) co-cultured ex vivo with tumor antigens has been used in pilot studies in various malignancies such as melanoma and lymphoma with encouraging results. Methods: In the present paper, the preparation and exposure of patient DC to autologous HCC antigens and re-injection in an attempt to elicit antitumor immune responses are described. Results: Therapy was given to two patients, one with hepatitis C and one with hepatitis B, who had large, multiple HCC and for whom no other therapy was available. No significant side-effects were observed. The clinical course was unchanged in one patient, who died a few months later. The other patient, whose initial prognosis was considered poor, is still alive and well more than 3 years later with evidence of slowing of tumor growth based on organ imaging. Conclusions: It is concluded that HCC may be a malignancy worthy of DC trials and sufficient details in the present paper are given for the protocol to be copied or modified. (C) 2002 Blackwell Publishing Asia Pty Ltd.

Extreme learning machine for predicting HLA-peptide binding

Machine learning techniques have been recognized as powerful tools for learning from data. One of the most popular learning techniques, the Back-Propagation (BP) Artificial Neural Networks, can be used as a computer model to predict peptides binding to the Human Leukocyte Antigens (HLA). The major advantage of computational screening is that it reduces the number of wet-lab experiments that need to be performed, significantly reducing the cost and time. A recently developed method, Extreme Learning Machine (ELM), which has superior properties over BP has been investigated to accomplish such tasks. In our work, we found that the ELM is as good as, if not better than, the BP in term of time complexity, accuracy deviations across experiments, and most importantly - prevention from over-fitting for prediction of peptide binding to HLA.

Reciprocal relationship between methylation status and loss of heterozygosity at the p14 (ARF) locus in Australian and South African hepatocellular carcinomas

Chromosome 9p21, a locus comprising the tumor suppressor genes (TSG) p16(INK4) (a) and p14(ARF) , is a common region of loss of heterozygosity (LOH) in hepatocellular carcinoma (HCC). p14(ARF) shares exon 2 with p16 in a different reading frame. p14 binds to MDM2 resulting in a stabilization of functional p53 . This study examined the roles of p14, p16 and p53 in hepatocarcinogenesis, in 37 Australian and 24 South African patients. LOH at 9p21 and 17p13.1, p14 and p16 mutation analysis, p14 and p16 promoter methylation and p14, p16 and p53 protein expression was examined. LOH at 9p21 was detected more frequently in South African HCC (P = 0.04). Comparable rates of p53 LOH were observed in Australian and South African HCC (10/22, 45%vs 13/22, 59%, respectively). Hypermethylation of the p14 promoter was more prevalent in Australian HCC than in South African HCC (17/37, 46%vs 7/24, 29%, respectively). In Australian HCC the prevalence of p14 methylation increased with age (P = 0.03). p16 promoter methylation was observed in 12/37 (32%) and 6/24 (25%) in Australian and South African HCC, respectively. Loss of p16 protein expression was detected in 14/36 Australian HCC whereas p53 protein expression was detected in 9/36. Significantly, a reciprocal relationship between 9p21 LOH and p14 promoter hypermethylation was observed (P less than or equal to0.05 ). No significant association between p14 and p53 was seen in this study. The reciprocal relationship identified indicates different pathways of tumorigenesis and likely reflects different etiologies of HCC in the two countries. (C) 2002 Blackwell Science Asia Pty Ltd.

On learning context-free and context-sensitive languages

The long short-term memory (LSTM) is not the only neural network which learns a context sensitive language. Second-order sequential cascaded networks (SCNs) are able to induce means from a finite fragment of a context-sensitive language for processing strings outside the training set. The dynamical behavior of the SCN is qualitatively distinct from that observed in LSTM networks. Differences in performance and dynamics are discussed.