e-journal

Intelligent Consensus Modeling for Proline Cis-Trans Isomerization Prediction

---

Proline cis-trans isomerization (CTI) plays a key role in the rate-determining steps of protein folding. Accurate prediction of proline CTI is of great importance for the understanding of protein folding, splicing, cell signaling, and transmembrane active transport in both the human body and animals. Our goal is to develop a state-of-the-art proline CTI predictor based on a biophysically motivated intelligent consensus modeling through the use of sequence information only (i.e., position specific scores generated by PSI-BLAST). The current computational proline CTI predictors reach about 70-73 percent Q2 accuracies and about 0.40 Matthew correlation coefficient (Mcc) through the use of sequence-based evolutionary information as well as predicted protein secondary structure information. However, our approach that utilizes a novel decision tree-based consensus model with a powerful randomizedmetalearning technique has achieved 86.58 percent Q2 accuracy and 0.74 Mcc, on the same proline CTI data set, which is a better
result than those of any existing computational proline CTI predictors reported in the literature.
Index Terms—Proline cis-trans isomerization, machine-learning, intelligent systems, ensemble methods

---

Ketersediaan

Tidak ada salinan data

Informasi Detail

Judul Seri

IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS

No. Panggil

-

Penerbit

New York : IEEE., 2014

Deskripsi Fisik

IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, VOL. 11, NO. 1, JANUARY/FEBRUARY 2014

Bahasa

English

ISBN/ISSN

1545-5963

Klasifikasi

-

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

VOL. 11, NO. 1, JANUARY/FEBRUARY 2014

Subjek

TEKNIK

Info Detail Spesifik

-

Pernyataan Tanggungjawab

Yuli/Agus

Versi lain/terkait

Lampiran Berkas

Komentar

---

Anda harus masuk sebelum memberikan komentar