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 235 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_<i>x</i>Si (<i>a</i>-Li_<i>x</i>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_<i>x</i>Si NWs, we observe the formation of a small amount of <i>c</i>-Si nuclei in the <i>a</i>-Li_<i>x</i>Si matrix below a composition of Li_1.4Si ≈ Li_1.5Si, 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_<i>x</i>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