8,239 research outputs found
Machine learning-guided directed evolution for protein engineering
Machine learning (ML)-guided directed evolution is a new paradigm for
biological design that enables optimization of complex functions. ML methods
use data to predict how sequence maps to function without requiring a detailed
model of the underlying physics or biological pathways. To demonstrate
ML-guided directed evolution, we introduce the steps required to build ML
sequence-function models and use them to guide engineering, making
recommendations at each stage. This review covers basic concepts relevant to
using ML for protein engineering as well as the current literature and
applications of this new engineering paradigm. ML methods accelerate directed
evolution by learning from information contained in all measured variants and
using that information to select sequences that are likely to be improved. We
then provide two case studies that demonstrate the ML-guided directed evolution
process. We also look to future opportunities where ML will enable discovery of
new protein functions and uncover the relationship between protein sequence and
function.Comment: Made significant revisions to focus on aspects most relevant to
applying machine learning to speed up directed evolutio
On the Investigation of Biological Phenomena through Computational Intelligence
This paper is largely devoted for building a novel approach which is able to explain biological phenomena like splicing promoter gene identification disease and disorder identification and to acquire and exploit biological data This paper also presents an overview on the artificial neural network based computational intelligence technique to infer and analyze biological information from wide spectrum of complex problems Bioinformatics and computational intelligence are new research area which integrates many core subjects such as chemistry biology medical science mathematics computer and information science Since most of the problems in bioinformatics are inherently hard ill defined and possesses overlapping boundaries Neural networks have proved to be effective in solving those problems where conventional com-putation tools failed to provide solution Our experiments demonstrate the endeavor of biological phenomena as an effec-tive description for many intelligent applications Having a computational tool to predict genes and other meaningful in-formation is therefore of great value and can save a lot of expensive and time consuming experiments for biologists This paper will focus on issues related to design methodology comprising neural network to analyze biological information and investigate them for powerful application
An empirical comparison of supervised machine learning techniques in bioinformatics
Research in bioinformatics is driven by the experimental data.
Current biological databases are populated by vast amounts of
experimental data. Machine learning has been widely applied to
bioinformatics and has gained a lot of success in this research
area. At present, with various learning algorithms available in the
literature, researchers are facing difficulties in choosing the best
method that can apply to their data. We performed an empirical
study on 7 individual learning systems and 9 different combined
methods on 4 different biological data sets, and provide some
suggested issues to be considered when answering the following
questions: (i) How does one choose which algorithm is best
suitable for their data set? (ii) Are combined methods better than
a single approach? (iii) How does one compare the effectiveness
of a particular algorithm to the others
Deep Learning for Genomics: A Concise Overview
Advancements in genomic research such as high-throughput sequencing
techniques have driven modern genomic studies into "big data" disciplines. This
data explosion is constantly challenging conventional methods used in genomics.
In parallel with the urgent demand for robust algorithms, deep learning has
succeeded in a variety of fields such as vision, speech, and text processing.
Yet genomics entails unique challenges to deep learning since we are expecting
from deep learning a superhuman intelligence that explores beyond our knowledge
to interpret the genome. A powerful deep learning model should rely on
insightful utilization of task-specific knowledge. In this paper, we briefly
discuss the strengths of different deep learning models from a genomic
perspective so as to fit each particular task with a proper deep architecture,
and remark on practical considerations of developing modern deep learning
architectures for genomics. We also provide a concise review of deep learning
applications in various aspects of genomic research, as well as pointing out
potential opportunities and obstacles for future genomics applications.Comment: Invited chapter for Springer Book: Handbook of Deep Learning
Application
DeepSig: Deep learning improves signal peptide detection in proteins
Motivation:
The identification of signal peptides in protein sequences is an important step toward protein localization and function characterization.
Results:
Here, we present DeepSig, an improved approach for signal peptide detection and cleavage-site prediction based on deep learning methods. Comparative benchmarks performed on an updated independent dataset of proteins show that DeepSig is the current best performing method, scoring better than other available state-of-the-art approaches on both signal peptide detection and precise cleavage-site identification.
Availability and implementation:
DeepSig is available as both standalone program and web server at https://deepsig.biocomp.unibo.it. All datasets used in this study can be obtained from the same website
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