Helsinki Institute for Information Technology

About: Helsinki Institute for Information Technology is a facility organization based out in Espoo, Finland. It is known for research contribution in the topics: Population & Bayesian network. The organization has 630 authors who have published 1962 publications receiving 63426 citations.

Metabolite identification and molecular fingerprint prediction through machine learning

Abstract: Motivation: Metabolite identification from tandem mass spectra is an important problem in metabolomics, underpinning subsequent metabolic modelling and network analysis. Yet, currently this task requires matching the observed spectrum against a database of reference spectra originating from similar equipment and closely matching operating parameters, a condition that is rarely satisfied in public repositories. Furthermore, the computational support for identification of molecules not present in reference databases is lacking. Recent efforts in assembling large public mass spectral databases such as MassBank have opened the door for the development of a new genre of metabolite identification methods. Results: We introduce a novel framework for prediction of molecular characteristics and identification of metabolites from tandem mass spectra using machine learning with the support vector machine. Our approach is to first predict a large set of molecular properties of the unknown metabolite from salient tandem mass spectral signals, and in the second step to use the predicted properties for matching against large molecule databases, such as PubChem. We demonstrate that several molecular properties can be predicted to high accuracy and that they are useful in de novo metabolite identification, where the reference database does not contain any spectra of the same molecule. Availability: An Matlab/Python package of the FingerID tool is freely available on the web at http://www.sourceforge.net/p/fingerid. Contact: markus.heinonen@cs.helsinki.fi

Applying discrete PCA in data analysis

Abstract: Methods for analysis of principal components in discrete data have existed for some time under various names such as grade of membership modelling, probabilistic latent semantic analysis, and genotype inference with admixture. In this paper we explore a number of extensions to the common theory, and present some application of these methods to some common statistical tasks. We show that these methods can be interpreted as a discrete version of ICA. We develop a hierarchical version yielding components at different levels of detail, and additional techniques for Gibbs sampling. We compare the algorithms on a text prediction task using support vector machines, and to information retrieval.

Machine learning and feature selection for drug response prediction in precision oncology applications

Abstract: In-depth modeling of the complex interplay among multiple omics data measured from cancer cell lines or patient tumors is providing new opportunities toward identification of tailored therapies for individual cancer patients. Supervised machine learning algorithms are increasingly being applied to the omics profiles as they enable integrative analyses among the high-dimensional data sets, as well as personalized predictions of therapy responses using multi-omics panels of response-predictive biomarkers identified through feature selection and cross-validation. However, technical variability and frequent missingness in input “big data” require the application of dedicated data preprocessing pipelines that often lead to some loss of information and compressed view of the biological signal. We describe here the state-of-the-art machine learning methods for anti-cancer drug response modeling and prediction and give our perspective on further opportunities to make better use of high-dimensional multi-omics profiles along with knowledge about cancer pathways targeted by anti-cancer compounds when predicting their phenotypic responses.

Explaining the power-law distribution of human mobility through transportation modality decomposition

Abstract: Human mobility has been empirically observed to exhibit Levy flightcharacteristics and behaviour with power-law distributed jump size. The fundamentalmechanisms behind this behaviour has not yet been fully explained. In thispaper, we propose to explain the Levy walk behaviour observed in humanmobility patterns by decomposing them into different classes according tothe different transportation modes, such as Walk/Run, Bike, Train/Subway orCar/Taxi/Bus. Our analysis is based on two real-life GPS datasets containingapproximately 10 and 20 million GPS samples with transportation mode information.We show that human mobility can be modelled as a mixture of different transportationmodes and that these single movement patterns can be approximated by a lognormaldistribution rather than a power-law distribution. Then, we demonstrate thatthe mixture of the decomposed lognormal flight distributions associated witheach modality is a power-law distribution, providing an explanation to theemergence of Levy Walk patterns that characterize human mobility patterns.