dataesr/scientific-paragraphs-categorization huggingface dataset:
CGC web page: 'The Cancer Gene Census (CGC) is an ongoing effort to catalogue those genes which contain mutations that have been causally implicated in cancer'). This interpretation would also explain why LOTUS hardly agrees with the other methods when comparing the top ranked genes. Indeed, for the 20 top predictions of LOTUS (excluding training sets), the intersection with TUSON consists only in two TSGs and one OG, the intersection with DiffMut consist of one TSG and two OGs, and the intersection with 20/20+ consists in four TSGs and one OG. Since LOTUS and MUFFINN are the only methods, among those considered here, that use network information in addition to mutation data, this could explain less overlap between their predictions and those of the other methods, and a lower overlap with CGC for the top ranked genes, since no PPI information was used to establish the CGC database.
Analysis of new driver genes predicted by LOTUS We tested the ability of LOTUS to make new driver gene predictions. We trained LOTUS with the CGCv86 train set, made predictions over the complete COSMIC database (19,320 genes including the training sets). The complete results are given in S1 Table. Complete analysis of the predicted OGs and TSGs rankings is out of the scope of this paper. However, we considered the 22/21 best ranked TSGs and OGs, and made bibliographic search in order to look for independent information that could validate these predictions. For the 22 best ranked predicted OGs, we found abundant literature reporting their implication in various cancers. It is not possible to make a full review for each of these genes, but we group them based on their global functions and focus on some examples.
In this context, we investigate two multitask versions of LOTUS, where we predict driver genes for a given cancer based on the drivers known for this cancer but also on all driver genes known for other cancer types. For a given cancer type, this may improve driver genes prediction by limiting the loss of statistical power compared to the aforementioned single-task approach. For that purpose, we derive a list of 30 cancer diseases from the 20/20 mutation dataset as explained in Methods. This complete list is available in S2 Table. As expected, many cancer types have only few known cancer genes (Fig  4 ). Since we want to evaluate the performance of LOTUS in a cross-validation scheme, we only consider diseases with more than 4 known driver genes in order to be able to run a 2-fold cross-validation scheme.
This leads us to keep 27 cancer types for TSG prediction and 27 for OG prediction. Note however that, for each cancer type, prediction are made while sharing all the driver genes known for the 30 diseases, according to their similarities with these cancer types. The 2-fold cross-validated CE of LOTUS for the 27 considered cancer types is presented in Table  12  (for TSG) and Table  13  (for OG). We compare four variants of LOTUS, as explained in Methods: (1) single-task LOTUS treats each disease in turn independently from the others using only the mutation data related to the considered disease to calculate gene features, and only the driver genes known for this disease are used to train the algorithm; (2) Aggregation LOTUS is also a single-task version of LOTUS, but gene features are calculated using the complete mutation database of gene mutations in all cancers.
Finally, we compare these performances with those of Diff-Mut, as a single-task method using only the mutation data related to the considered disease, as for single-task LOTUS. For most diseases (25/27 for TSG, 27/27 for OG), single-task LOTUS and DiffMut lead to the worst CE, confirming the difficulty to treat each cancer type individually, due to the small number of known driver genes and to the smaller mutation database available for each cancer type type. Interestingly, Aggregation LOTUS often leads to a strong improvement in performance. This shows that different cancer types often share some common mechanisms and driver genes, and therefore, simply using all the available information as in a pan-cancer paradigm improves the performance of driver gene prediction for each disease. However, in many cases, the multitask LOTUS and LOTUS2 algorithms lead to an additional improvement over Aggregation LOTUS, LOTUS2 leading in general to the best results (in 17 types out of 27 for TSG prediction, and in 17 types out of 27 for OG prediction).
Comparing different methods Among LOTUS, TUSON, 20/20+, DiffMut, MUFFINN and MutSigCV, we can distinguish on the one side, the unsupervised methods MutSigCV, MUFFINN and DiffMut that score candidate genes independently of any training set of known drivers, and the supervised methods LOTUS, TUSON and 20/20+ that make predictions based on a training set of known driver genes. In addition, all methods use gene descriptors that are calculated based on a mutation databases, and therefore, changing the mutation database will change the prediction performance. In order to make fair comparison between LOTUS and the other five methods, we performed several experiments in which LOTUS is trained with the training set of the TUSON (respectively 20/20+) paper when compared to TUSON and MUFFINN (respectively 20/20+).
In addition, in all experiments, the gene features calculated for LOTUS and MUFFINN were based on the same mutation databases as those used by the other methods in their respective papers. Therefore, for a fair comparison between LOTUS, MUFFINN and TUSON, we use the mutation database available on the website of the authors along with their training sets of OGs and TSGs provided in  [26]. We evaluate the performance of LOTUS on this dataset by 5-fold cross-validation repeated twice (see Methods). For TUSON, we use the prediction results available in  [26]  and evaluate the CE as the mean number of non-driver genes that are ranked before known driver genes of the TUSON train sets. Finally, we downloaded and re-ran MUFFINN. For a fair comparison between LOTUS and 20/20+, we use the mutation database of 20/20 + and the training sets of OGs and TSGs provided by the authors on their website  [28].
We evaluate the performance of LOTUS as above. However we note that the 20/20+ score itself is obtained by a bootstrap procedure similar to our cross-validation approach  [28]. For a fair comparison between LOTUS and MutSigCV, we use the example mutation database available only for lung squamous cell tumours. Since MutSigCV does not use a train set of driver genes, we trained LOTUS with known OGs and TSGs available in CGCv86 for lung squamous cell tumours. MutSigCV provides a ranked list of genes that does not distinguish TSG and OG. Therefore, the CE is obtained by averaging the numbers of non-driver genes ranked before each driver genes in the train sets used for LOTUS. Finally, for a fair comparison between LOTUS and DiffMut, we use the 20/20+ mutation dataset for both methods. LOTUS is trained with the 20/20+ training sets of OGs and TSGs.
Experimental protocol To assess the performance of a driver gene prediction method on a given gold standard of known driver genes, we score all genes in the COSMIC database and measure how well the known driver genes are ranked. For that purpose, we plot the ROC curve, considering all known drivers as positive examples and all other genes in COSMIC as negative ones, and define the consistency error (CE) as CE ¼ #N � ð1 À AUCÞ; where #N is the number of negative genes, and AUC denotes the area under the ROC curve. In other words, CE measures the mean number of "non-driver" genes that the prediction method ranks higher than known driver genes. Hence, a perfect prediction method should have CE = 0, while a random predictor should have a CE near #N =2. To estimate the performance of a machine learning-based prediction method that estimates a scoring function from a training set of known driver genes, we use k-fold cross-validation for each given gold standard set of known driver genes.
The output of the method is an ordered list of all considered genes as a function of a p-value that estimates how likely this gene is to be a driver gene. TUSON uses gene features that encode frequency mutations and functional impact mutations. The underlying idea is that the proportion of mutation types observed in a given gene can be used to predict the likelihood of this gene to be a cancer driver. After having identified the most predicting parameters for OGs and TSGs based on a train set (called the TUSON train set in the present paper), TUSON uses a statistical model in which a p-value is derived for each gene that characterizes its potential as being an OG or a TSG, then scores all genes in the COSMIC database, to obtain two ranked lists of genes in increasing orders of p-values for OGs and TSGs.
The 20/20+ method encodes genes based on frequency and mutation types, and other biological information. It uses a train set of OGs and TSGs (called the 20/20 train set in the present paper) to train a random forest algorithm. Then, the random forest is used on the COSMIC database and the output of the method is again a list of genes ranked according to their predicted score to be a driver gene  [28]. We did not implement this method, so we decided to evaluate its performance only on its original training set: the 20/20 dataset. Moreover, we applied the same method to compute the CE as for MutSigCV and TUSON, which should actually give an advantage to 20/20+, since it is harder to make predictions in a crossvalidation loop using a smaller set of known driver genes. DiffMut uses a dataset of somatic mutations and a dataset of healthy genomes, but no training sets of known driver genes.
It compares the mutation profiles on a gene in the mutation dataset with the nucleotide variation profile in the healthy genomes, and computes for every gene a score that allows to rank all genes according to their potential as OG or TSG. MUFFINN uses a dataset of somatic mutations and extracts the number of non-synonymous mutations per gene. Then, it computes a score (either DNmax or DNsum) and propagates these scores on a functional gene network (either HumanNet or STRING). The final scores are used to compute four different rankings for all genes. Among these four possibilities, we systematically used the version that yields the best result for MUFFINN,. Note however that, in practice, the user would not know which version should be preferred.
(iii) The SEDs are resampled, integrated and noise is added to simulate realistic spectra from existing or planned galaxy spectroscopic surveys. To generate a realistic distribution of galaxies with simulated redshift values, SEDs and broadband photometry, we employ the COSMOSSNAP simulation code  (Jouvel et al. 2009). COS-MOSSNAP uses real data from the 30-band COSMOS photometric redshift catalog as a basis  (Ilbert et al. 2008) , thereby ensuring that realistic relationships between galaxy type, color, size and redshift are taken into account. The catalog was originally generated from a combination of observations from astronomical surveys covering the spectral range from the UV (GALEX), through the optical (Subaru) and to near-and far-infrared bands (CFHT, UKIRT, Spitzer). This data set is matched to Hubble ACS imaging data, thus including realistic size-magnitude distributions from high-quality shape measurements originally made for weak lensing applications  (Leauthaud et al.
Median input median residual lines 1 sigma residual lines S/N r = median flux σ 6760 Å 5600 Å. ( 10 ) We work with S/N ∈ {2, 5, 20}, where S/N = 20 is our "clean" case. For the remainder of this paper, we work with a training data set which includes 2000 clean resampled spectra randomly sampled from the original COSMOSSNAP data from a redshift range [0, 1. 7]. These training SEDs are blueshifted to the restframe and cropped to the wavelength range [1250Å, 10500Å]. For testing, we randomly sample galaxies of different types. 2 The LSST r-band filter is not identical to the SDSS one. Therefore, our definition of S/N is not identical to that in  Machado et al. (2013). This does not have any impact in our analysis and conclusions, as they are self-consistent. COSMOSSNAP classifies its spectra in 36 classes, organised in 4 groups: EllS0, SaSc, SdSm and SB.
Denoising autoencoders Firstly, the model was constructed using TensorFlow  (Abadi et al. 2016). TensorFlow is an open source software library for numerical computation using data flow graphs. It was developed by Google and tailored for machine learning. Extensive documentation describing all the functionalities can be found in 3. The denoising autoencoder was trained with the training set described above composed of N = 2 000 samples spanning from 1250 Å to 10500 Å resulting in a vector of dimension m = 4 258. The training samples have followed the same preprocessing as in the Darth Fader scheme for continuum line subtraction, the continuum of the spectra has been removed through wavelet filtering as explained in section 2. 1. 1. The number of visible units is fixed to m = 4 258 in agreement with the size of the input data and the number of hidden units is a free parameter.
Conclusion In this paper, we introduced two new methods of spectroscopic redshift estimation, and benchmarked them on simulated data against a reference method based on line feature estimation and cross-correlation with eigentemplates. Both new methods rely on deriving an efficient representation for the data then used for redshift estimation. The first one uses the MOD dictionary learning technique to obtain a sparse representation for the full galaxy spectra (continuum and lines), which is then used to estimated redshifts from noisy spectra by searching for the lowest sparse approximation error among all tested redshift values. The second method applies denoising autoencoders for non-linear unsupervised feature extraction, learning the features from rest-frame spectra, and deriving the best-fit redshift value by passing the test spectra through the autoencoder and minimizing the reconstruction error on the input signal.
Since the pioneering studies from the 1970s  [1] [2] [3] , continuous progress has been made. We refer interested readers to excellent review papers  [4, 5]  and to other articles of this special issue of Molecules. Computer software implementing QM/MM are widespread. Some of them such as Cuby  [6] , PUPIL  [7] , Chemshell  [8] , QMMM  [9]  or LICHEM  [10]  involve interface between QM and MM packages. Other computer codes implement both QM/MM in unique programs without the need for interfaces. This is, non-exhaustively, for instance, the case of AMBER  [11] , NWCHEM  [12] , QCHEM  [13] , Gaussian  [14]  or deMon2k  [15]. deMon2k is a program specialized in Kohn-Sham  [16]  density functional theory (DFT) within the Auxiliary DFT framework  [17]. The latter permits remarkably fast evaluation of energies, potentials and properties.
Methods References Polarizable embedding  [24]  Attosecond electron dynamics  [24, 25]  Ehrenfest non-adiabatic electron-nuclear dynamics -Link atoms this work Continuum solvation model for long range interactions this work Tutorials this work Geometrical restraints this work Metadynamics via interface to plumed library  [26]  [27], this work QM/MM simulations are complex to set up because of the intrinsic technicity of this kind of simulations. A second objective of this article is to provide user-friendly examples to learn how to use deMon2k in practice in QM/MM simulations. In part II, six projects illustrating some of the QM/MM capabilities of the program are presented. For each of them, a tutorial encompassing delineated computational protocols, guidelines to prepare program inputs and explanation on how to extract useful information from output files are deposited on the deMon2k website  [28].
Biasing Born-Oppenheimer Molecular Dynamics (BOMD) Trajectories Because of the intrinsic cost of BOMD simulations it is very often necessary to bias trajectories in order to improve conformational sampling. This is for example the case if one wishes to estimate free energy profiles. One possibility is the use of restraints on distances (or difference of distances), angles or dihedrals that allows the construction of the potential of mean force for studying chemical reactions or conformational rearrangements  [62, 63]. Another approach is the metadynamics (MetaD)  [64, 65]  tool available via the PLUMED 2. x package  [26] , which has been plugged into deMon2k  [27]. For this article our original implementation for gas phase MetaD simulations has been extended to hybrid QM/MM and QM/Onsager MetaD simulations.
Biasing forces applied to atomic nuclei are obtained as derivatives of V(S,t) by application using the chain rule: ∂V(X, t) ∂x k = n i=1 ∂V(X, t) ∂s i ∂s i ∂x k (50) PLUMED 2. x offers a large variety of CVs and the user can also build new CVs as a function of simpler functions to adapt to any new chemical problem. Both on-the-fly biasing and post-processing analyses can be performed with PLUMED 2. x. To perform MetaD simulations with deMon2k one simply includes the keyword METADYNAMICS in the deMon2k input file and provides in a separate file (plumed. inpt) the input information needed for PLUMED 2. x calculations, such as definition of CVs, temperature and others. Detailed information about the PLUMED 2. x code and tutorial can be found in Ref  [26]. An example application will be given in Part II.
How to Prepare a QM/MM Input for deMon2k? In practice, a bottleneck when planning QM/MM simulations with a new program is the preparation of input files in appropriate formats. We explain in this section basic rules to set-up a QM/MM calculation with deMon2k. A program has been created to help users in this critical step. PrepInpdeMon2k enables one to switch from well-established packages (CHARMM  [81] , NAMD  [82] , Amber, Tinker  [83] , Tinker-HP  [84] ) for MM simulations to deMon2k for QM/MM simulations. The program reads topology/parameter files generated by AMBER (prmtop file), CHARMM (psf file) and coordinate file (pdb, AMBER coordinate or restart files, xyz files, CHARMM coordinate files) and generates a deMon2k template input file for a QM/MM calculation. During execution of PrepInpdeMon2k the user can specify QM/MM options and shape the simulated system according to the partition scheme illustrated in Figure  1.
The need for link atoms is detected based on the list of QM atoms specified by the user. The geometry needs to be supplied in the input file itself (deMon. inp) or in a separate geometry file (called deMon. geo) either in Cartesian coordinates or with a z-matrix. For large molecular systems (>5,000 atoms) using the deMon. geo file is recommended. Each line specifying an MM atom should contain, after the position specification, the force field (FF) atom type and possibly the atom connectivity. If the connectivity is not supplied, the program tries to determine it from distances with a criterion based on the covalent radius of the two atoms. To request addition of a link atom, a connection to the corresponding QM atom must be indicated on the line specifying the frontier MM atom. Based on the connectivity and on the selected force field the program builds the list of energy terms entering E bonded (E bond ,E angle , E torsion and E improper ).
However, the user has the possibility to modify afterward the list of MM terms by adding or deleting specific energy terms. It is also possible to modify the charge, the Lennard-Jones parameters or the atomic polarizabilities for specific atoms. This flexibility in the specification of MM energy terms may be extremely useful for non-standard residues necessitating specifically tuned FF. Force field definition is contained in the external FFDS (force field dataset) file. We borrowed the parameter file format from the Tinker program  [83]. FFDS files for AMBERf99  [29] , Amberff02  [36] , OPLS  [23]  and CHARMM22  [81]  are available on the deMon2k website. It is also straightforward to integrate new atom types and FF terms in the FFDS file (an example will be given in part II).
To illustrate the simulation of the absorption spectra in water solution, we have simulated the absorption spectra of the chromophore oxyluciferin (the light emitter in fireflies) in a solution of explicit water molecules (Figure  9 ). The absorption spectrum has been simulated by a gaussian convolution of a series of excitation energies computed at the QM/MM level for a statistical number of snapshots (sampling of water-oxyluciferin conformations) retrieved from a molecular dynamics simulation. First, the system is built with the program tleap from the AMBER suite of programs by placing oxyluciferin in the center of an octahedral water box of 10 Å around the chromophore. The oxyluciferin parameters for the force field used at the classical level were obtained previously  [100]  and the water molecules are treated with the TIP3P model  [95].
Afterwards, the system is equilibrated and minimized at the classical level with AMBER. At this point, we take the system and move to deMon2k for the heating step, by performing a progressive heating from 50 to 300 K. The preparation of the deMon input for the system built in AMBER is done with PrepInpdeMon2k. The water box is reduced to a sphere of 20 Å and the water molecules beyond 15 Å are subjected to harmonic constraints to keep the spherical shape of the atomic system. First, the system is built with the program tleap from the AMBER suite of programs by placing oxyluciferin in the center of an octahedral water box of 10 Å around the chromophore. The oxyluciferin parameters for the force field used at the classical level were obtained previously  [100]  and the water molecules are treated with the TIP3P model  [95].
Non-Adiabatic Chemistry Induced by Ionizing Radiation Method involved: real-time TD-ADFT, Ehrenfest MD. This tutorial illustrates the electron dynamics module implemented in deMon2k  [24]. Two types of simulations are available. The first one is purely RT-TD-ADFT under the frozen-nuclear-position approximation. The second one is mean-field Ehrenfest MD which couples electron dynamics to nuclear dynamics. The former method is well suited to simulate electronic processes taking place on ultrashort time scales, from attoseconds up to a few femtoseconds, before atomic nuclei have time to respond. Both methods are compatible with QM/MM with either non-polarizable or polarizable FF. We consider in this tutorial the collision of a high-kinetic-energy proton with a peptide. Fast protons interact primarily with the electron cloud by Coulomb interactions inducing ionization of the molecule on the attosecond timescale, and eventually fragmentation of the molecule on longer, femtosecond, time scales.
Conclusions In this article we have reviewed the methodologies implemented in deMon2k for conducting QM/MM simulations taking advantage of the auxiliary DFT framework. Available methodologies encompass ground state Born-Oppenheimer MD, non-adiabatic MD simulations within the mean-field Ehrenfest scheme and attosecond electron dynamics simulations. deMon2k undergoes continuous improvements and work is underway in our laboratories to further enhance its QM/MM methods. For example more efficient algorithms for geometry optimizations, MD propagation (e. g. , time-reversible BOMD of Niklasson et al. [118] ) would be desirable. Our groups also work toward the implementation of other force fields like CHARMM36  [119]  or polarizable force field. Interested users or developers can obtain the source code from the deMon2k website (http://www.