33 research outputs found
Additional file 1: of Utilizing random Forest QSAR models with optimized parameters for target identification and its application to target-fishing server
MySQL codes for bioactivity extraction from ChEMBL database. Variable “molregno” from table “compound_structures” is identification code for ligands while variable “tid” from table “target_dictionary” is identification code for targets. (TXT 1 kb
Additional file 2: of Utilizing random Forest QSAR models with optimized parameters for target identification and its application to target-fishing server
Fitting model scores to the estimated probabilities. It contains mathematical expression used to fit a graph of log-scaled score versus estimated probability to the sigmoid function. (PDF 235Â kb
Predicting and improving the protein sequence alignment quality by support vector regression-0
<p><b>Copyright information:</b></p><p>Taken from "Predicting and improving the protein sequence alignment quality by support vector regression"</p><p>http://www.biomedcentral.com/1471-2105/8/471</p><p>BMC Bioinformatics 2007;8():471-471.</p><p>Published online 3 Dec 2007</p><p>PMCID:PMC2222655.</p><p></p>-profile alignment method. (b) Each alignment is transformed to (n + 1)-dimensional feature vector composed of the alignment scores at n positions and the total alignment score. (c) These feature vectors are used to train SVR model for the target template
Predicting and improving the protein sequence alignment quality by support vector regression-8
<p><b>Copyright information:</b></p><p>Taken from "Predicting and improving the protein sequence alignment quality by support vector regression"</p><p>http://www.biomedcentral.com/1471-2105/8/471</p><p>BMC Bioinformatics 2007;8():471-471.</p><p>Published online 3 Dec 2007</p><p>PMCID:PMC2222655.</p><p></p> 0.9453. Adjacent color bar shows the mapping of relative density. (b) Plot of frequency distribution. (c) Plot of MAE distribution. (d) Plot of NMAE distribution
Distribution of posterior probabilities of outputs of SVM for fold-recognition
<p><b>Copyright information:</b></p><p>Taken from "Predicting and improving the protein sequence alignment quality by support vector regression"</p><p>http://www.biomedcentral.com/1471-2105/8/471</p><p>BMC Bioinformatics 2007;8():471-471.</p><p>Published online 3 Dec 2007</p><p>PMCID:PMC2222655.</p><p></p
Histogram of predicted MaxSub scores of the alignments of the pairs that are not related at the fold level
<p><b>Copyright information:</b></p><p>Taken from "Predicting and improving the protein sequence alignment quality by support vector regression"</p><p>http://www.biomedcentral.com/1471-2105/8/471</p><p>BMC Bioinformatics 2007;8():471-471.</p><p>Published online 3 Dec 2007</p><p>PMCID:PMC2222655.</p><p></p
Predicting and improving the protein sequence alignment quality by support vector regression-7
<p><b>Copyright information:</b></p><p>Taken from "Predicting and improving the protein sequence alignment quality by support vector regression"</p><p>http://www.biomedcentral.com/1471-2105/8/471</p><p>BMC Bioinformatics 2007;8():471-471.</p><p>Published online 3 Dec 2007</p><p>PMCID:PMC2222655.</p><p></p>-profile alignment method. (b) Each alignment is transformed to (n + 1)-dimensional feature vector composed of the alignment scores at n positions and the total alignment score. (c) These feature vectors are used to train SVR model for the target template
Predicting and improving the protein sequence alignment quality by support vector regression-2
<p><b>Copyright information:</b></p><p>Taken from "Predicting and improving the protein sequence alignment quality by support vector regression"</p><p>http://www.biomedcentral.com/1471-2105/8/471</p><p>BMC Bioinformatics 2007;8():471-471.</p><p>Published online 3 Dec 2007</p><p>PMCID:PMC2222655.</p><p></p> (b) superfamily (c) fold level
Predicting and improving the protein sequence alignment quality by support vector regression-1
<p><b>Copyright information:</b></p><p>Taken from "Predicting and improving the protein sequence alignment quality by support vector regression"</p><p>http://www.biomedcentral.com/1471-2105/8/471</p><p>BMC Bioinformatics 2007;8():471-471.</p><p>Published online 3 Dec 2007</p><p>PMCID:PMC2222655.</p><p></p> 0.9453. Adjacent color bar shows the mapping of relative density. (b) Plot of frequency distribution. (c) Plot of MAE distribution. (d) Plot of NMAE distribution
Atomistic Observation of the Lithiation and Delithiation Behaviors of Silicon Nanowires Using Reactive Molecular Dynamics Simulations
For the practical use of silicon
nanowires (Si NWs) as anodes for
Li-ion batteries, understanding their lithiation and delithiation
mechanisms at the atomic level is of critical importance. Here, we
report the mechanisms for the lithiation and delithiation of Si NWs
determined using a large-scale molecular dynamics (MD) simulation
with a reactive force field (ReaxFF). The ReaxFF is developed in this
work using first-principles calculations. Our ReaxFF-MD simulation
shows that an anisotropic volume expansion behavior of Si NWs during
lithiation is dependent on the surface structures of the Si NWs; however,
the volumes of the fully lithiated Si NWs are almost identical irrespective
of the surface structures. During the lithiation process, Li atoms
penetrate into the lattices of the crystalline Si (<i>c</i>-Si) NWs preferentially along the ⟨110⟩ or ⟨112⟩
direction, and then the <i>c</i>-Si changes into amorphous
Li<sub><i>x</i></sub>Si (<i>a</i>-Li<sub><i>x</i></sub>Si) phases due to the simultaneous breaking of Si–Si
bonds as a result of the tensile stresses between Si atoms. Before
the complete amorphization of the Si NWs, we observe the formation
of silicene-like structures in the NWs that are eventually broken
into low-coordinated components, such as dumbbells and isolated atoms.
However, during delithiation of the Li<sub><i>x</i></sub>Si NWs, we observe the formation of a small amount of <i>c</i>-Si nuclei in the <i>a</i>-Li<sub><i>x</i></sub>Si matrix below a composition of Li<sub>1.4</sub>Si ≈ Li<sub>1.5</sub>Si, in which the volume fraction of formed <i>c</i>-Si phases relies on the delithiation rate. We also demonstrate that
the two-phase structure can be thermodynamically more favorable than
the single-phase <i>a</i>-Li<sub><i>x</i></sub>Si. We expect that our comprehensive understanding of the lithiation
and delithiation mechanisms along with the developed ReaxFF for Li–Si
systems will provide helpful guidelines in designing Si anodes to
obtain better performing Li-ion batteries