NDLI: A deep learning network approach to ab initio protein secondary structure prediction

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MRFy: remote homology detection for beta-structural proteins using Markov random fields and stochastic search

RLIMS-P 2.0: a generalizable rule-based information extraction system for literature mining of protein phosphorylation information

Phenotype-dependent coexpression gene clusters: application to normal and premature ageing

Global network alignment in the context of aging

Reachability analysis in probabilistic biological networks

GLProbs: aligning multiple sequences adaptively

Prediction and informative risk factor selection of bone diseases

Capturing uncertainty by modeling local transposon insertion frequencies improves discrimination of essential genes

A deep learning network approach to ab initio protein secondary structure prediction

A hierarchical clustering method of selecting kernel SNP to unify informative SNP and tag SNP

A maximum a posteriori probability and time-varying approach for inferring gene regulatory networks from time course gene microarray data

An algorithm for motif discovery with iteration on lengths of motifs

Discovering binding cores in protein-DNA binding using association rule mining with statistical measures

Gene tree diameter for deep coalescence

Heterogeneous cloud framework for big data genome sequencing

Identification of protein complexes using weighted pagerank-nibble algorithm and core-attachment structure

Identifying affinity classes of inorganic materials binding sequences via a graph-based model

Parallel implementation of MAFFT on CUDA-enabled graphics hardware

Predicting protein function using multiple kernels

Tractable cases of (*,2)-bounded parsimony haplotyping

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A deep learning network approach to ab initio protein secondary structure prediction

Content Provider	ACM Digital Library
Author	Cheng, Jianlin Eickholt, Jesse Spencer, Matt
Copyright Year	2015
Abstract	Ab initio protein secondary structure (SS) predictions are utilized to generate tertiary structure predictions, which are increasingly demanded due to the rapid discovery of proteins. Although recent developments have slightly exceeded previous methods of SS prediction, accuracy has stagnated around 80 percent and many wonder if prediction cannot be advanced beyond this ceiling. Disciplines that have traditionally employed neural networks are experimenting with novel deep learning techniques in attempts to stimulate progress. Since neural networks have historically played an important role in SS prediction, we wanted to determine whether deep learning could contribute to the advancement of this field as well. We developed an SS predictor that makes use of the position-specific scoring matrix generated by PSI-BLAST and deep learning network architectures, which we call DNSS. Graphical processing units and CUDA software optimize the deep network architecture and efficiently train the deep networks. Optimal parameters for the training process were determined, and a workflow comprising three separately trained deep networks was constructed in order to make refined predictions. This deep learning network approach was used to predict SS for a fully independent test dataset of 198 proteins, achieving a Q3 accuracy of 80.7 percent and a Sov accuracy of 74.2 percent.
Starting Page	103
Ending Page	112
Page Count	10
File Format	PDF
ISSN	15455963
DOI	10.1109/TCBB.2014.2343960
Volume Number	12
Issue Number	1
Journal	IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-01-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Deep learning Machine learning Neural nets Protein structure prediction
Content Type	Text
Resource Type	Article
Subject	Genetics Biotechnology Applied Mathematics

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction

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