Discriminative Motif Discovery via Simulated Evolution and Random Under-Sampling

<div><p>Conserved motifs in biological sequences are closely related to their structure and functions. Recently, discriminative motif discovery methods have attracted more and more attention. However, little attention has been devoted to the data imbalance problem, which is one of the main reasons affecting the performance of the discriminative models. In this article, a simulated evolution method is applied to solve the multi-class imbalance problem at the stage of data preprocessing, and at the stage of Hidden Markov Models (HMMs) training, a random under-sampling method is introduced for the imbalance between the positive and negative datasets. It is shown that, in the task of discovering targeting motifs of nine subcellular compartments, the motifs found by our method are more conserved than the methods without considering data imbalance problem and recover the most known targeting motifs from Minimotif Miner and InterPro. Meanwhile, we use the found motifs to predict protein subcellular localization and achieve higher prediction precision and recall for the minority classes.</p></div

Number of known motifs recovered by different methods.

<p>The p-values are calculated by generating random motifs.</p

Percentage of conserved instances of the top 20 candidate motifs.

<p>The p-values are calculated by hypergeometric test.</p

Recall of each compartment.

<p>This table shows that our method significantly improves the recall of the minority classes.</p

Top 3 motif candidates.

<p>These motifs are most predictive of localization, which are discovered on hierarchical compartment structure by (A) Disc and (B) DiscMU. The x-axis title of each HMM logo is the rank and compartment of the motif.</p

Total accuracy of predictions.

<p>Motifs are discovered by the five methods on flat and hierarchical (tree) structure respectively.</p

Compartment distribution.

<p>This table shows that the compartment distribution is severely skewed. The first column is the cellular compartment; the second and third columns are the number and percentage of sequences in each cellular compartment of the dataset I, while the fourth and fifth columns are those of the dataset II.</p

Precision of each compartment.

<p>This table shows that our method significantly improves the precision of the minority classes.</p

Data_Sheet_1_Cox Regression Based Modeling of Functional Connectivity and Treatment Outcome for Relapse Prediction and Disease Subtyping in Substance Use Disorder.pdf

Functional magnetic resonance imaging (fMRI) has become one of the most widely used noninvasive neuroimaging technique in research of cognitive neurosciences and of neural mechanisms of neuropsychiatric/neurological diseases. A primary goal of fMRI-based neuroimaging studies is to identify biomarkers for brain-behavior relationship and ultimately perform individualized treatment outcome prognosis. However, the concern of inadequate validation and the nature of small sample sizes are associated with fMRI-based neuroimaging studies, both of which hinder the translation from scientific findings to clinical practice. Therefore, the current paper presents a modeling approach to predict time-dependent prognosis with fMRI-based brain metrics and follow-up data. This prediction modeling is a combination of seed-based functional connectivity and voxel-wise Cox regression analysis with built-in nested cross-validation, which has been demonstrated to be able to provide robust and unbiased model performance estimates. Demonstrated with a cohort of treatment-seeking cocaine users from psychosocial treatment programs with 6-month follow-up, our proposed modeling method is capable of identifying brain regions and related functional circuits that are predictive of certain follow-up behavior, which could provide mechanistic understanding of neuropsychiatric/neurological disease and clearly shows neuromodulation implications and can be used for individualized prognosis and treatment protocol design.</p

Graphical model for IMMMLGP.

<p>Graphical model for IMMMLGP.</p