Near-unsupervised computational methods for exploring 'omic' data

Grant number: DP150103512 | Funding period: 2015 - 2019

Completed

Abstract

The project aims to investigate the recently proposed promising machine learning paradigm "Near Unsupervised Learning" by critically analysing and comparing existing methods. The project also aims to develop new algorithms in the broader spectrum of Big Data Analytics and their adaptation to the following three applications: species separation in metagenomic data; development of a model to relate genomic information to cancer drug sensitivity; and, the identification of distinct metabolite distribution patterns in mass spectrometry metabolomic data. The potential outcomes include increased understanding of the usefulness of fertilisers on different plant varieties and newly emerging plant di..

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Related publications (12)

IMPARO: inferring microbial interactions through parameter optimisation.

Rajith Vidanaarachchi, Marnie Shaw, Sen-Lin Tang, Saman Halgamuge

2020-08-19

BACKGROUND: Microbial Interaction Networks (MINs) provide important information for understanding bacterial communities. MINs can ..

A Physarum-Inspired Algorithm for Minimum-Cost Relay Node Placement in Wireless Sensor Networks

Yahui Sun, Daniel Rehfeldt, Marcus Brazil, Doreen Thomas, Saman Halgamuge

2020-04-01

Relay node placement, which aims to connect pre-deployed sensor nodes to base stations, is essential in minimizing the costs of wi..

Estimating neuronal conductance model parameters using dynamic action potential clamp

Y Deerasooriya, G Berecki, D Kaplan, IC Forster, S Halgamuge, S Petrou

2019-09-01

Parameterization of neuronal membrane conductance models relies on data acquired from current clamp (CC) or voltage clamp (VC) rec..

ENVirT: inference of ecological characteristics of viruses from metagenomic data

Duleepa Jayasundara, Damayanthi Herath, Damith Senanayake, Isaam Saeed, Cheng-Yu Yang, Yuan Sun, Bill C Chang, Sen-Lin Tang, Saman K Halgamuge

2019-02-04

Background: Estimating the parameters that describe the ecology of viruses,particularly those that are novel, can be made possible..

Improving MMD-GaN training with repulsive loss function

W Wang, Y Sun, S Halgamuge

2019-01-01

Generative adversarial nets (GANs) are widely used to learn the data sampling process and their performance may heavily depend on ..

A two-tiered unsupervised clustering approach for drug repositioning through heterogeneous data integration

Pathima Nusrath Hameed, Karin Verspoor, Snezana Kusljic, Saman Halgamuge

2018-04-11

Assessment of Gait Characteristics in Total Knee Arthroplasty Patients Using a Hierarchical Partial Least Squares Method

Wei Wang, David C Ackland, Jodie A McClelland, Kate E Webster, Saman Halgamuge

2018-01-01

The node-weighted Steiner tree approach to identify elements of cancer-related signaling pathways

Yahui Sun, Chenkai Ma, Saman Halgamuge

2017-12-28

BACKGROUND: Cancer constitutes a momentous health burden in our society. Critical information on cancer may be hidden in its signa..

Positive-Unlabeled Learning for inferring drug interactions based on heterogeneous attributes

Pathima Nusrath Hameed, Karin Verspoor, Snezana Kusljic, Saman Halgamuge

2017-03-01

Use of adaptive network burst detection methods for multielectrode array data and the generation of artificial spike patterns for method evaluation

GDC Mendis, E Morrisroe, S Petrou, SK Halgamuge

2016-04-01

Mechatronics Fundamentals and Applications Preface

Clarence W de Silva, Farbod Khoshnoud, Maoqing Li, Saman K Halgamuge

2016-01-01

A new peak detection algorithm for MALDI mass spectrometry data based on a modified Asymmetric Pseudo-Voigt model

C Wijetunge, I Saeed, BA BOUGHTON, U Roessner, SK Halgamuge

2015-12-09

Background Mass Spectrometry (MS) is a ubiquitous analytical tool in biological research and is used to measure the mass-to-charge..

Researchers

Saman Halgamuge