Ensemble - Science topic

The pressure inside solids can be a very high negative value meaning you are expanding your system. But when you relax the system with NPT you do not get desired pressure but something fluctuating around meaning expand/ shrink. If you have perfect potential (smooth one with a good tail) you can continue NVT or even NVE and pressure remains constant with much less fluctuations. But if your potential needs some conditions for P to remain constant (like a large cutoff high k-space accuracy and so on), I suggest continuing with NPT because it is less expensive than those conditions. If this is the case, it is worth trying a large damping (some thousands of timesteps) which means you are barostat less frequently. Larger damping is less expensive but it may or may not reduce pressure fluctuations.

Not valid for reporting the research.....

Use GROMACS, NAMD, AMBER etc for the same

Regards

Dear university staff!

I inform you that my lecture on electronic medicine on the topic: "The use of automated system-cognitive analysis for the classification of human organ tumors" can be downloaded from the site: https://www.patreon.com/user?u =87599532

Lecture with sound in English. You can download it and listen to it at your convenience.

Sincerely,

Vladimir Ryabtsev, Doctor of Technical Science, Professor Information Technologies.

Dear university staff!

I inform you that my lecture on electronic medicine on the topic: "The use of automated system-cognitive analysis for the classification of human organ tumors" can be downloaded from the site: https://www.patreon.com/user?u =87599532

Lecture with sound in English. You can download it and listen to it at your convenience.

Sincerely,

Vladimir Ryabtsev, Doctor of Technical Science, Professor Information Technologies.

Dear Muhammad Imad ,

Overfitting makes the model relevant to its data set only, and irrelevant to any other data sets. Some of the methods used to prevent overfitting include ensembling, data augmentation, data simplification, and cross-validation.

Regards,

Shafagat

I think the semiisotropic would be the best choice in the case of the membrane, as mentioned in the documentation. If the box size didn't change with the entrance of the nanoparticle, the membrane pressure will be increased.

You found three alleles T (wild) A and C (mutants). While in the previous literature you found only two T and C and their frequencies. If there no information on the mutant A that means new mutation is detected by yourself.

Hi

I wonder if you manage it, and can share the solution?

There are several Possible reasons. However I cannot be certain from the given information. Here are some possible reasons:

1. The NPT imposes a pressure condition, check what was the pressure before cooling and how it changes during. Maybe the box expanding to reach the desired 0 pressure.

2. Check the potential energy for possible phase transitions. 5000 K is super high, I doubt that an EAM potential will support that high temperature accurately.

Zaibun Nisa In Python, follow these steps:

1. Begin with a subset of the training dataset.

2. On the dataset, train a basic model.

3. Make predictions on the entire dataset using the third model.

4. Determine the errors using the expected and actual values.

5. Assign the same weight to all data items.

6. Give more weight to data pieces that were mistakenly forecasted.

Hello Chang-Ting Wang ,

it is, mainly, due to the

"H-bond(s)",

in ('pure & liquid') water.

The lower heat capacity in aqueous electrolyte solutions is due to the lower percentage of the Hydrogen bonding (due to the lower^[1] percentage of the remaining 'pure & liquid' water).

can anyone tell how am i ensemble deep learning models those having different input shape array?

as Dnn using 2D input array shape while CNN and RNN using 3D shape input arrays.

all of the models are already fitted

thank you all for the answers

Usually "ensemble" means "configurational ensemble," i.e., a collection of structures of a single system, with the structures differing only in atomic coordinates. But technically, yes, one can make an ensemble of different systems and dock into them -- if the structures (and pharmacophore features) align well . . .

CRISPR is a technology that can be used to edit genes and, as such, will likely change the world. The essence of CRISPR is simple: it's a way of finding a specific bit of DNA inside a cell. After that, the next step in CRISPR gene editing is usually to alter that piece of DNA.Imagine a future where parents can create bespoke babies, selecting the height and eye color of their unborn children. In fact, imagine that all traits of life forms can be customized to one’s preferences: domestic pet size, plant longevity, and more.

The zebrafish has emerged as a leading model organism for the study of vertebrate biology, because of the remarkable cellular resolution with which the embryo can be studied, the ease of assaying its development and physiology in the laboratory, and its amenability to genetic analysesThe zebrafish is a powerful experimental system for uncovering gene function in vertebrate organisms. Nevertheless, studies in the zebrafish have been limited by the approaches available for eliminating gene function. Here we present simple and efficient methods for inducing, detecting, and recovering mutations at virtually any locus in the zebrafish. Briefly, double-strand DNA breaks are induced at a locus of interest by synthetic nucleases, called TALENs. Subsequent host repair of the DNA lesions leads to the generation of insertion and deletion mutations at the targeted locus.

I agree with Toni Klemm.

Dear Sutanu,

I am new to Gromacs so could you please tell me how I could find different configuration by using gmx trjconv.?

I have tried the command "gmx_mpi trjconv -f nvt.gro -s nvt.gro -o conf.xtc -t0 150 -timestep 40" but it's showing "no output, last frame read at t=0"

It seems that LSTM-like models might not good at learning the long-term trends in time series and thus the predicted time series have low accuracy in long-term trends, even they show good variations (e.g., high correlation coefficients).

Thank you Reza Shokri Gharelo and Jesús María Vielba for your suggestions. I came up with the goseq R package for the GO term analysis, for which I aligned my reads with a new version of the reference genome from the Ensemble Plants.

DEAR Chandrima debnath, please consider these links

Destroy Image Classification by Ensemble of Pre-trained models | by Vardan Agarwal | Towards Data Science

Dear Oliver

Why don't you use the ESGF database as your main source?

If you do not know how to use it for your purpose, I can help you.

Last year I started my research by choosing some SSP scenarios from this website.

Here is my first article:

Best regards

Amir

Brooke Shepard

If you're having an issue finding an exact ID match, you can try this method.

You collect all protein sequences of the strain and use BlastKOALA/GhostKOALA (tool available in the KEGG) to perform Blast. It will provide you with the KEGG's KO IDs. These IDs can also be used for pathway analysis.

Thank you

This paper answers that Lakshminarayanan, B., Pritzel, A., and Blundell, C.Simple and scalable predictive uncertainty estima-tion using deep ensembles.InAdvances in NeuralInformation Processing Systems, volume 30. Curran As-sociates, Inc., 2017, https://proceedings.neurips.cc/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf

As you are now aware, there is quite a bit of "craft" in the practice of molecular dynamics simulations. If simulations could be run for seconds or even milliseconds, there would be no need for much of this craftmanship. As we are constrained to nanoseconds, or at most microseconds, one makes a concerted effort to gently push away from ground truth--the crystal structure---and into the deeper waters of everything floating freely in an NPT simulation. This is solely to avoid running long segments of your simulations in unphysical or unproductive conformations.

This strategy uses initial minimizations to clear bad contacts between solvating waters introduced into the model and hold ions in place because the force fields do not really represent ion coordination. (Van der Waals and electrostatic forces are spherically symmetric. Octahedral coordination is a geometrical accident in the force field, not an intrinsic property of ionic bonding.) Initial dynamics runs are typically NVT with backbone atoms constrained or fixed to allow side chains to begin softly swaying in the breeze. Subsequently, constraints are released and further NPT simulations are run for 1 fs time steps before production runs at 2 ps commence for the duration.

Some simulations also utilize high and low temperatures as another means of control. Low temperatures effectively constrain the atoms in the crystal structure without having to provide constraint information. High temperatures achieve something approaching longer time steps without resorting to higher-order integrations to maintain fidelity. I rarely found fiddling with the temperature to be of much use and, instead, fiddled with constraints. It is a matter of personal taste to a large degree.

You can examine results reported by other groups in similar molecular systems to see what their practices are. You can also look at tutorials for the codes that you are using. Most developers have something like a "best practices" page that explains some of the curious choices for the various parameters involved in the simulations.

Rajesh Kondabala Take a look with this thread: https://www.researchgate.net/post/How_to_maintain_pressure_at_equilibrium_using_NPT-NVT

Check https://www.mathworks.com/help/stats/framework-for-ensemble-learning.html

Check..

Oh wait: check this 1

If you do a classification task you could just use both classification algorithm k-means and CNN to classify then you'll be more confident about your classification (even better if you use more than just two methods)

See some software solutions addressed in these posts:

Widom's insertion method works for hard spheres, too. The Boltzmann factor exp(-beta Delta U) is 1 for a successful insertion and 0 for a failure. The problem is, however, that a rather large number of insertion attempts is required to obtain a meaningful ensemble average for the liquid phase.

For hard spheres it might be better to determine the chemical potential by thermodynamic integration or –if an insertion method must be used – by a multi-step insertion (i.e., insertion of a point particle followed by a gradually increase of its size).

Hi! thank you very much! I will follow your suggestion (indeed it just to see whether some gene have a specific trend in agreement with my RNAseq data and hypothesis)!

Hi Kleran

I think you followed the support (https://support.10xgenomics.com/spatial-gene-expression/software/pipelines/latest/advanced/references) and I see 2 points that could be at the origin of your problem:

- the first one is the genome you selected, is it mm10 genome?

- the second one is that data you downloaded must be compatible with STAR aligner, a point you need to dig in...

all the best

fred

Norman Geist , thanks. I did not know the problem of DFT before. I will have a try :)

It is better to use 10 fold cross-validation mode for calculating results and comparing with other trees.

Most probably if this is vibration data, random forest tree always exhibits superior performance.

Dear Tabinda Masud ,

:ook the link, maybe useful.

Regards,

Shafagat

Yes. You can proceed with that formula as you deal with Precipitation data, which contains only non-negative values. according to axioms of probability P(y≠0|fk)=1-P(y=0|fk).

You can find a worked example in the book titled “Statistical Methods in Hydrology and Hydroclimatology(DOI: 10.1007/978-981-10-8779-0)”

i hope it will be useful for you https://towardsdatascience.com/ensemble-models-5a62d4f4cb0c

This is an answer to the question you asked Dr. Sherien

Summera Khan you can interpolate four points surrounding the gauge point to extract the RCM data

then you can use KGE for comparison between both (RCM data and Observation data)

Hi Giacomo, yes I also ran NVE with MDALGO=0 and SMASS=-3. The results were essentially identical. I have a continued simulation running now. It remain consistent to 3000 steps so far. I'll try a longer time step and lower temp as you suggest.

Jane Sun I cannot find the web-link as given

when googled, I get : https://www.solvergen.com/blog

and at the blog tab, its given : 502 Bad Gateway

nginx/1.14.0 (Ubuntu)

Dear Velmurugan Lingamuthu

This links might be helpful, Have a look:

1. https://www.analyticsvidhya.com/blog/2018/06/comprehensive-guide-for-ensemble-models/

2. https://machinelearningmastery.com/stacking-ensemble-machine-learning-with-python/

3. https://towardsdatascience.com/ensemble-learning-using-scikit-learn-85c4531ff86a

Kind Regards

Qamar Ul Islam

I would also say that you are confusing concepts, in GTEx you will find eQTLs or sQTLs in which the presence of an SNP (rs) is correlated with the expression of a gene (eQTL) or with the expression of the isoforms of a gene (sQTLs). Therefore, if you have the Ensembl ID of the transcripts of a gene and you want to compare it with the GTEx database, you will possibly get the SNP that is correlated with it. I don't know if I made myself clear.

Thanks to 'Juan Weisz' and 'L. I. Plimak' for your quick answers!

I just want to add that the reason for my question was an article ( https://arxiv.org/pdf/1612.00676.pdf ), in which physicists were surveyed about their attitudes concerning the foundations of quantum mechanics. I was shocked to see (in Fig.6) that 29% considered the measurement problem as solved by decoherence, and 17% considered it even as a pseudoproblem. I my opinion, the measurement problem is absolutely important, but still unsolved.

Dear Razi,

that is a good question which puzzled me a lot during the last few years.

There are various approaches on that and it always depends on your expectations ...

1. As decision maker you may want to use various future projections, covering e.g. wet a future, a dry future, or a hot or cold future. There are some papers from Alex Ruane on this ... I have applied it also in my latest publication "To bias correct or not to bias correct ..."

2. You may also want to determine the "best" performing RCM based on an validation of the historical "baseline" simulations. Then you compare the historical simulations with the statistics of observations or re-analysis data.

The question here is if this gives you enough credibility that the future projections are also the most reliable ones? The underlying scenarios to drive the RCMs are extremely uncertain, which means that even you trust a certain RCM more than another one, this does not urgently lead to more reliable simulations for the future period.

I would therefore prefer the first procedure of considering RCMs which cover different states (cold/wet, hot/wet, cold dry, cold wet, normal) to generate an ensemble, but if you think you can identify the best performing (or if you are only interested in simulations for the past), there is a subsetting algorithm from the group of Samaniego (main author is Stefan Thober).

Raj Kumar Kapooria sir, Thank you very much for the answer. But sir my query is regarding those thermodynamic models and how to interpret them in any system practically?

Thanks and Regards

Subha Sanket Panda

Sorry but I don’t practise those skills bécause I’m just a student in thé second year of my bachelor in Political Science in Université Saint-Louis in Brussels

i would run several equilibration MD simulations, starting with different initial velocities, at very low temperature (5K), with tight temperature control, and with very short time step (0.01 fs), and gradually bringing the system to the state at which the data will be collected for analysis.

please check :

Zero temperature means no motion.

Hi Mohamed,

I had the same question, and I addressed it like this:

I looked for the target gene/transcript in NCBI, found the fasta sequence. This is in most cases the CDS of the gene. Then I doublechecked whether this gene HAS an additional 5'/3' UTR in the transcript. ENSEMBL has also a bacterial platform, which is great. For my target gene, the same sequence was found without additional UTRs. So I assume it just hasn't any (Which might be true for many genes, as it's polycistronic RNA).

But, if you have found a better solution, I'm very happy if you could share it.

RUboost is available in matlab

These are "alternative locations", meaning that in high resolution structure, you may observe different conformations in the electron density. Depending on the programs you are using, you either need to set some parameter to tell the program to ignore all but the most highly occupied conformation, or you need to pre-process the PDB files to remove the secondary locations. Many structure visualisation programs contain options to select, and by extension, selectively delete alternative locations - check the manual for the program you are using

In VMD, you can use the PDB plugin to do so. https://www.ks.uiuc.edu/Research/vmd/plugins/molfile/pdbplugin.html

You can also use PDBtools in Phenix https://www.phenix-online.org/documentation/reference/pdbtools.html

In Rosetta, a simple python script can be used to clean up a pdb file:

tools/protein_tools/scripts/clean_pdb.py   - Prepare PDBs for Rosetta by cleaning and renumbering residues.

in PyMOL, you can use the removal.py script: https://pymolwiki.org/index.php/Removealt or by simply specifying

remove not (alt ' '+'A')

alter all, alt=' '

Dear Debopam Ghosh, interesting question.

I would suggest two approaches, following my own humble experience as a student and as a teacher.

If one wants to teach Quantum Mechanics for Quantum Computing and related fields, a more linear algebra (math) approach can be used.

If one wants to teach Quantum Physics to engineer, physics and chemistry students, a modern physics approach is needed, since students need to understand all experiments before going into the math.

It is just an opinion.

interested

This is interesting topic, mostly concerned with classification problem for Fishes species recognition. Can be performed via MATLAB or Python. I used MATLAB by considering the data at hand as manifolds valued data via parametric modeling framework.

Use Ensembl Biomart.

There are many papers you may consult where Desmond is used for P-L simulations.

Starting with scratch you may initially do it for

Simulation time: 100ns

Recording interval = 20ps and NPT ensemble.

This might take around 4-6GB of your storage. If you are not satisfied with ligand stability you may further extend that simulation as well,=.

New location: https://proteinensemble.org/

I think your intuition is correct.

First of all, in equilibrium the ensembles should be equivalent so whether you have a canonical ensemble (fixed temperature) or a microcanonical ensemble (fixed energy, easier in MD) should not matter to zeroth order (apart from subtle finite size effects that you may worry about much later).

Then the average is, as you say, just an average over snapshots of the simulation. You need to be careful about the 'rolling window' because ideally you do not want to average over correlated snapshots so you should wait between snapshots until correlations have decayed. Which is a bit of a chicken and egg problem as you want to measure the correlations exactly to figure out how long they persist. So this probably needs some iterations to figure out good parameters. Or, alternatively, you do not reuse simulations at all and start from fresh, random initial conditions for every t=0.

I hope you have found your answer.

If not, the link may be your want.

Anyway, the question of the code should be asked on other web sites.

https://www.programcreek.com/python/example/110776/rdkit.Chem.AllChem.EmbedMultipleConfs

I think the different databases are problems because then there are often different references for the gene.

Hello Amobichukwu Amanambu

I invite you to watch the video for a better understanding:

It can help you to understand the different related concepts and find the related website to download your desire models and extract them.

I dig myself into CNN explainability in the past months. Most of the projects are suitable only one purpose or damn slow per image. I found an implementation of GradCAM, GradCAM++ and ScoreCAM from a Japanese developer on Github. It is awesome, no dependencies and very easy to integrate into your Python stack. And these methods can be run on a larger scale on many images in a reasonable amount of time:

https://github.com/tabayashi0117/Score-CAM/blob/master/gradcamutils.py

Other notes:

- SHAP APIs are not straightforward to use and that method is damn slow. Not very useful to do quick iterations of your work.

- Backprop-based techniques got criticism for being not really working.

- LIME got similar criticism.

- For saliency maps, I did not find them useful at all.

Any other methods are very much WIP, no public software or you have to implement yourself. I found the above mentioned CAM-based methods, easy to integrate, more or less working and they are relative quick per image.

It's no secret that quantum fluctuations admit a statistical interpretation: They can be described by a probability distribution.

Gunjan Katyal

If two genes are overlapping as you describe, then the reading frames of them will be different as they can be on same or opposite strand (maybe). And since the overlapping is in opposite direction, 5' would be described for the gene you would be targeting or characterizing.

I can't say about the promoter, since promoters are strange entities and they need not to be present in vicinity of the gene. But I don't think overlapping genes would share a promoter.

However, you can go through these two articles. Maybe you will not get exact answer, but it would give you some hints.

Meng-Ru H, Kuo-Wang T, Wen-chang L. A unified framework of overlapping genes: Towards the origination and endogenic regulation. Genomics 2012;100(4):231-239. doi.org/10.1016/j.ygeno.2012.06.011

Rosikiewicz W, Suzuki Y, Makalowska I. OverGeneDB: a database of 5' end protein coding overlapping genes in human and mouse genomes. Nucleic Acids Res. 2018;46(D1):D186-D193. doi:10.1093/nar/gkx948

CNN is mainly used to classify and it needs large compute ability. so I suggest you use a machine learning model to finish your project. such as SVM、Random Forest、XGBoost.

This is precisely the differential magnetic susceptibilty: dM/dH. It depends on the frequency of the magnetic field and on temperature.

Of course, it is related to the coercivity: just think in the energy landscape where the anisotropy is the barrier that prevents the magnetization switching of each nanoparticle.

The full width at half maximum (FWHM) is related to the monodisperse character of your ensemble of nanoparticles (shape, purity of phases, etc). For instance, if you are planning to use the nanoparticles in Magnetic Particle Imaging (MPI), the FWHM is an indication of the potential spatial resolution in MPI experiments: the narrower the better.

Please click Recommend if you like this answer and therefore it can help others.

As with all machine learning predictors, you can choose any threshold depending on your goals. If you want to maximize recall then stick to 0.5 but if you are interested in higher precision, choose a higher threshold. If you are interested in prioritizing the most likely candidates for disease-causing variants, you can simply use the predictions as a ranking. Keep in mind the results are only predictions, you may find these guidelines helpful:

If you are using machine learning tools such as WEKA, SAS, MATLAB package, etc you need to select the appropriate techniques. However, if you are writing your own codes you need to implement training and learning of the algorithm using the concepts of parallelism. Then combined the results using an appropriate method to obtain the final results that meet your needs. If further assistance is needed please let me know.

I suggest some useful links: