-
Article
Open AccessScalable variable selection for two-view learning tasks with projection operators
In this paper we propose a novel variable selection method for two-view settings, or for vector-valued supervised learning problems. Our framework is able to handle extremely large scale selection tasks, where...
-
Article
Open AccessProtein function prediction through multi-view multi-label latent tensor reconstruction
In last two decades, the use of high-throughput sequencing technologies has accelerated the pace of discovery of proteins. However, due to the time and resource limitations of rigorous experimental functional ...
-
Article
Open AccessLearning symmetry-aware atom map** in chemical reactions through deep graph matching
Accurate atom map**, which establishes correspondences between atoms in reactants and products, is a crucial step in analyzing chemical reactions. In this paper, we present a novel end-to-end approach that f...
-
Article
Open AccessJoint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data
Structural annotation of small molecules in biological samples remains a key bottleneck in untargeted metabolomics, despite rapid progress in predictive methods and tools during the past decade. Liquid chromat...
-
Article
Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra
Metabolomics using nontargeted tandem mass spectrometry can detect thousands of molecules in a biological sample. However, structural molecule annotation is limited to structures present in libraries or databa...
-
Article
Open AccessSubstrate specificity of 2-deoxy-D-ribose 5-phosphate aldolase (DERA) assessed by different protein engineering and machine learning methods
In this work, deoxyribose-5-phosphate aldolase (Ec DERA, EC 4.1.2.4) from Escherichia coli was chosen as the protein engineering target for improving the substrate preference towards smaller, non-phosphorylated a...
-
Article
Open AccessLeveraging multi-way interactions for systematic prediction of pre-clinical drug combination effects
We present comboFM, a machine learning framework for predicting the responses of drug combinations in pre-clinical studies, such as those based on cell lines or patient-derived cells. comboFM models the cell c...
-
Article
SIRIUS 4: a rapid tool for turning tandem mass spectra into metabolite structure information
Mass spectrometry is a predominant experimental technique in metabolomics and related fields, but metabolite structural elucidation remains highly challenging. We report SIRIUS 4 (https://bio.informatik.uni-jena....
-
Protocol
Analysis of Fluxomic Experiments with Principal Metabolic Flux Mode Analysis
In the analysis of metabolism, two distinct and complementary approaches are frequently used: Principal component analysis (PCA) and stoichiometric flux analysis. PCA is able to capture the main modes of varia...
-
Article
Multi-view kernel completion
In this paper, we introduce the first method that (1) can complete kernel matrices with completely missing rows and columns as opposed to individual missing kernel values, with help of information from other i...
-
Article
Open AccessCritical Assessment of Small Molecule Identification 2016: automated methods
The fourth round of the Critical Assessment of Small Molecule Identification (CASMI) Contest (www.casmi-contest.org) was held in 2016, with two new cat...
-
Article
Open AccessSelected proceedings of Machine Learning in Systems Biology: MLSB 2016
-
Article
Open AccessProfiling persistent tubercule bacilli from patient sputa during therapy predicts early drug efficacy
New treatment options are needed to maintain and improve therapy for tuberculosis, which caused the death of 1.5 million people in 2013 despite potential for an 86 % treatment success rate. A greater understan...
-
Chapter and Conference Paper
Soft Kernel Target Alignment for Two-Stage Multiple Kernel Learning
The two-stage multiple kernel learning (MKL) algorithms gained the popularity due to their simplicity and modularity. In this paper, we focus on two recently proposed two-stage MKL algorithms: ALIGNF and TSMKL...
-
Article
Multilabel classification through random graph ensembles
We present new methods for multilabel classification, relying on ensemble learning on a collection of random output graphs imposed on the multilabel, and a kernel-based structured output learner as the base cl...
-
Chapter and Conference Paper
Canonical Correlation Methods for Exploring Microbe-Environment Interactions in Deep Subsurface
In this study, we apply non-linear kernelized canonical correlation analysis (KCCA) as well as primal-dual sparse canonical correlation analysis (SCCA) to the discovery of correlations between sulphate reducin...
-
Chapter and Conference Paper
Reconstructing Gapless Ancestral Metabolic Networks
We present a method for inferring the structure of ancestral metabolic networks directly from the networks of observed species and their phylogenetic tree. In particular, we aim to minimize the number of mutat...
-
Chapter and Conference Paper
Multi-task Drug Bioactivity Classification with Graph Labeling Ensembles
We present a new method for drug bioactivity classification based on learning an ensemble of multi-task classifiers. As the base classifiers of the ensemble we use Max-Margin Conditional Random Field (MMCRF) m...
-
Chapter and Conference Paper
Structured Output Prediction of Novel Enzyme Function with Reaction Kernels
Enzyme function prediction is an important problem in post-genomic bioinformatics, needed for reconstruction of metabolic networks of organisms. Currently there are two general methods for solving the problem:...
-
Chapter and Conference Paper
Structured Output Prediction of Anti-cancer Drug Activity
We present a structured output prediction approach for classifying potential anti-cancer drugs. Our QSAR model takes as input a description of a molecule and predicts the activity against a set of cancer cell ...
