842 research outputs found
DSL: Discriminative Subgraph Learning via Sparse Self-Representation
The goal in network state prediction (NSP) is to classify the global state
(label) associated with features embedded in a graph. This graph structure
encoding feature relationships is the key distinctive aspect of NSP compared to
classical supervised learning. NSP arises in various applications: gene
expression samples embedded in a protein-protein interaction (PPI) network,
temporal snapshots of infrastructure or sensor networks, and fMRI coherence
network samples from multiple subjects to name a few. Instances from these
domains are typically ``wide'' (more features than samples), and thus, feature
sub-selection is required for robust and generalizable prediction. How to best
employ the network structure in order to learn succinct connected subgraphs
encompassing the most discriminative features becomes a central challenge in
NSP. Prior work employs connected subgraph sampling or graph smoothing within
optimization frameworks, resulting in either large variance of quality or weak
control over the connectivity of selected subgraphs.
In this work we propose an optimization framework for discriminative subgraph
learning (DSL) which simultaneously enforces (i) sparsity, (ii) connectivity
and (iii) high discriminative power of the resulting subgraphs of features. Our
optimization algorithm is a single-step solution for the NSP and the associated
feature selection problem. It is rooted in the rich literature on
maximal-margin optimization, spectral graph methods and sparse subspace
self-representation. DSL simultaneously ensures solution interpretability and
superior predictive power (up to 16% improvement in challenging instances
compared to baselines), with execution times up to an hour for large instances.Comment: 9 page
Structure-Aware Dynamic Scheduler for Parallel Machine Learning
Training large machine learning (ML) models with many variables or parameters
can take a long time if one employs sequential procedures even with stochastic
updates. A natural solution is to turn to distributed computing on a cluster;
however, naive, unstructured parallelization of ML algorithms does not usually
lead to a proportional speedup and can even result in divergence, because
dependencies between model elements can attenuate the computational gains from
parallelization and compromise correctness of inference. Recent efforts toward
this issue have benefited from exploiting the static, a priori block structures
residing in ML algorithms. In this paper, we take this path further by
exploring the dynamic block structures and workloads therein present during ML
program execution, which offers new opportunities for improving convergence,
correctness, and load balancing in distributed ML. We propose and showcase a
general-purpose scheduler, STRADS, for coordinating distributed updates in ML
algorithms, which harnesses the aforementioned opportunities in a systematic
way. We provide theoretical guarantees for our scheduler, and demonstrate its
efficacy versus static block structures on Lasso and Matrix Factorization
Principal Metabolic Flux Mode Analysis
In recent years, much progress has been achieved in the computational analysis of the metabolic networks, as a consequence of the rapid growth of the omics database. However, current literature analysis algorithms still lack good biological interpretability of the analysis results. Moreover, they can not be applied on a whole-genome level.
This thesis assesses the potential of the Principal Metabolic Flux Mode Analysis (PMFA). The PMFA is a novel algorithm that was recently developed, which aims to improve the interpretability of Principal Component Analysis (PCA), through including a stoichiometric regularization to the PCA objective function. The PMFA can determine the flux modes that explain the highest variability in the network and it can also scale-up to a whole-genome level using the sparse version of PMFA. Furthermore, this thesis compares the PMFA to the recent approach Principal Elementary Mode Analysis (PEMA), which also tries to enhance the PCA interpretability. However, this approach is computationally heavy and thus fails to handle the large-scale networks (e.g., whole-genome). In order to further determine the feasibility of the PMFA approach for the analysis of metabolism, a Graph-regularized Matrix Factorization (GMF) was developed analogous to PMFA framework, similarly by adding the network stoichiometric matrix to a graph-structured matrix factorization framework.
The results illustrate the potential of PMFA as a metabolic network analysis for identifying fluxes that explain maximum variation in the network and it can be used to analyze whole-genome level. In addition, the results showed that GMF method performed well in predicting active Elementary Modes (EMs) on simulated data but failed to work on large networks, while PEMA had the lowest performance among all methods. Based on the results, future work can be conducted to improve the GMF approach in terms of genome-scale analysis through including sparsity
Machine Learning and Integrative Analysis of Biomedical Big Data.
Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues
Network-based stratification of tumor mutations.
Many forms of cancer have multiple subtypes with different causes and clinical outcomes. Somatic tumor genome sequences provide a rich new source of data for uncovering these subtypes but have proven difficult to compare, as two tumors rarely share the same mutations. Here we introduce network-based stratification (NBS), a method to integrate somatic tumor genomes with gene networks. This approach allows for stratification of cancer into informative subtypes by clustering together patients with mutations in similar network regions. We demonstrate NBS in ovarian, uterine and lung cancer cohorts from The Cancer Genome Atlas. For each tissue, NBS identifies subtypes that are predictive of clinical outcomes such as patient survival, response to therapy or tumor histology. We identify network regions characteristic of each subtype and show how mutation-derived subtypes can be used to train an mRNA expression signature, which provides similar information in the absence of DNA sequence
Multi-Target Prediction: A Unifying View on Problems and Methods
Multi-target prediction (MTP) is concerned with the simultaneous prediction
of multiple target variables of diverse type. Due to its enormous application
potential, it has developed into an active and rapidly expanding research field
that combines several subfields of machine learning, including multivariate
regression, multi-label classification, multi-task learning, dyadic prediction,
zero-shot learning, network inference, and matrix completion. In this paper, we
present a unifying view on MTP problems and methods. First, we formally discuss
commonalities and differences between existing MTP problems. To this end, we
introduce a general framework that covers the above subfields as special cases.
As a second contribution, we provide a structured overview of MTP methods. This
is accomplished by identifying a number of key properties, which distinguish
such methods and determine their suitability for different types of problems.
Finally, we also discuss a few challenges for future research
SUBIC: A Supervised Bi-Clustering Approach for Precision Medicine
Traditional medicine typically applies one-size-fits-all treatment for the
entire patient population whereas precision medicine develops tailored
treatment schemes for different patient subgroups. The fact that some factors
may be more significant for a specific patient subgroup motivates clinicians
and medical researchers to develop new approaches to subgroup detection and
analysis, which is an effective strategy to personalize treatment. In this
study, we propose a novel patient subgroup detection method, called Supervised
Biclustring (SUBIC) using convex optimization and apply our approach to detect
patient subgroups and prioritize risk factors for hypertension (HTN) in a
vulnerable demographic subgroup (African-American). Our approach not only finds
patient subgroups with guidance of a clinically relevant target variable but
also identifies and prioritizes risk factors by pursuing sparsity of the input
variables and encouraging similarity among the input variables and between the
input and target variable
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