7,200 research outputs found
Metric learning pairwise kernel for graph inference
Much recent work in bioinformatics has focused on the inference of various
types of biological networks, representing gene regulation, metabolic
processes, protein-protein interactions, etc. A common setting involves
inferring network edges in a supervised fashion from a set of high-confidence
edges, possibly characterized by multiple, heterogeneous data sets (protein
sequence, gene expression, etc.). Here, we distinguish between two modes of
inference in this setting: direct inference based upon similarities between
nodes joined by an edge, and indirect inference based upon similarities between
one pair of nodes and another pair of nodes. We propose a supervised approach
for the direct case by translating it into a distance metric learning problem.
A relaxation of the resulting convex optimization problem leads to the support
vector machine (SVM) algorithm with a particular kernel for pairs, which we
call the metric learning pairwise kernel (MLPK). We demonstrate, using several
real biological networks, that this direct approach often improves upon the
state-of-the-art SVM for indirect inference with the tensor product pairwise
kernel
Identification of functionally related enzymes by learning-to-rank methods
Enzyme sequences and structures are routinely used in the biological sciences
as queries to search for functionally related enzymes in online databases. To
this end, one usually departs from some notion of similarity, comparing two
enzymes by looking for correspondences in their sequences, structures or
surfaces. For a given query, the search operation results in a ranking of the
enzymes in the database, from very similar to dissimilar enzymes, while
information about the biological function of annotated database enzymes is
ignored.
In this work we show that rankings of that kind can be substantially improved
by applying kernel-based learning algorithms. This approach enables the
detection of statistical dependencies between similarities of the active cleft
and the biological function of annotated enzymes. This is in contrast to
search-based approaches, which do not take annotated training data into
account. Similarity measures based on the active cleft are known to outperform
sequence-based or structure-based measures under certain conditions. We
consider the Enzyme Commission (EC) classification hierarchy for obtaining
annotated enzymes during the training phase. The results of a set of sizeable
experiments indicate a consistent and significant improvement for a set of
similarity measures that exploit information about small cavities in the
surface of enzymes
On the optimal contact potential of proteins
We analytically derive the lower bound of the total conformational energy of
a protein structure by assuming that the total conformational energy is well
approximated by the sum of sequence-dependent pairwise contact energies. The
condition for the native structure achieving the lower bound leads to the
contact energy matrix that is a scalar multiple of the native contact matrix,
i.e., the so-called Go potential. We also derive spectral relations between
contact matrix and energy matrix, and approximations related to one-dimensional
protein structures. Implications for protein structure prediction are
discussed.Comment: 5 pages, text onl
Algebraic shortcuts for leave-one-out cross-validation in supervised network inference
Supervised machine learning techniques have traditionally been very successful at reconstructing biological networks, such as protein-ligand interaction, protein-protein interaction and gene regulatory networks. Many supervised techniques for network prediction use linear models on a possibly nonlinear pairwise feature representation of edges. Recently, much emphasis has been placed on the correct evaluation of such supervised models. It is vital to distinguish between using a model to either predict new interactions in a given network or to predict interactions for a new vertex not present in the original network. This distinction matters because (i) the performance might dramatically differ between the prediction settings and (ii) tuning the model hyperparameters to obtain the best possible model depends on the setting of interest. Specific cross-validation schemes need to be used to assess the performance in such different prediction settings. In this work we discuss a state-of-the-art kernel-based network inference technique called two-step kernel ridge regression. We show that this regression model can be trained efficiently, with a time complexity scaling with the number of vertices rather than the number of edges. Furthermore, this framework leads to a series of cross-validation shortcuts that allow one to rapidly estimate the model performance for any relevant network prediction setting. This allows computational biologists to fully assess the capabilities of their models
Spectral Sequence Motif Discovery
Sequence discovery tools play a central role in several fields of
computational biology. In the framework of Transcription Factor binding
studies, motif finding algorithms of increasingly high performance are required
to process the big datasets produced by new high-throughput sequencing
technologies. Most existing algorithms are computationally demanding and often
cannot support the large size of new experimental data. We present a new motif
discovery algorithm that is built on a recent machine learning technique,
referred to as Method of Moments. Based on spectral decompositions, this method
is robust under model misspecification and is not prone to locally optimal
solutions. We obtain an algorithm that is extremely fast and designed for the
analysis of big sequencing data. In a few minutes, we can process datasets of
hundreds of thousand sequences and extract motif profiles that match those
computed by various state-of-the-art algorithms.Comment: 20 pages, 3 figures, 1 tabl
ProLanGO: Protein Function Prediction Using Neural~Machine Translation Based on a Recurrent Neural Network
With the development of next generation sequencing techniques, it is fast and
cheap to determine protein sequences but relatively slow and expensive to
extract useful information from protein sequences because of limitations of
traditional biological experimental techniques. Protein function prediction has
been a long standing challenge to fill the gap between the huge amount of
protein sequences and the known function. In this paper, we propose a novel
method to convert the protein function problem into a language translation
problem by the new proposed protein sequence language "ProLan" to the protein
function language "GOLan", and build a neural machine translation model based
on recurrent neural networks to translate "ProLan" language to "GOLan"
language. We blindly tested our method by attending the latest third Critical
Assessment of Function Annotation (CAFA 3) in 2016, and also evaluate the
performance of our methods on selected proteins whose function was released
after CAFA competition. The good performance on the training and testing
datasets demonstrates that our new proposed method is a promising direction for
protein function prediction. In summary, we first time propose a method which
converts the protein function prediction problem to a language translation
problem and applies a neural machine translation model for protein function
prediction.Comment: 13 pages, 5 figure
- …