User:1onicCoval3nt/sandbox

Sandbox for some additions:

Multinomic Approach:
'''The advent of big data, high throughput sequencing technologies allowed creation of a new field of data analysis. Sequencing genes, transcripts, proteins and mitochondria has thus yielded vast collections of data, demanding new and inventive approaches to be made for their analysis. Deep sequencing and faster platforms such as Illumina, have allowed the field to get more accurate data and lower costs associated with sequencing genome-wide [2]. Combining these data sources and their respective tools to form a biomarker discovery platform is a new approach. Multi-omics relies on combining all data sources to create a unique and complex fingerprint for each disease. Analysis of these signatures can then lead to clinically-relevant biomarker signatures [5]. These multi-omic biomarkers offer usage in cancer diagnostics, prognostics, and individualized treatments while another usage puts biomarkers into applications within drug discovery and development [6].'''

'''These tools are in a constant active state of development and research. Recent advances include a combination of both statistical based and AI biased approaches to identifying mutations in sequencing data [3]. Debate between the efficacy of each approach is present due to disagreements in comparing methods [3]. Each pipeline was also published with inconsistent performance on data sets and were compared to mainstream pipelines in their time [3]. Most pipelines rely on a combination of statistical model approaches such as allele frequency analysis, markov chain model, joint genotype analysis, heuristic threshold. For example, in join genotype analysis adopted by SomaticSniper, FaSD-somatic, SAMtools, JointSNVMix2, Virmid, SNVSniffer, Seurat, and CaVEMan Bayes' rule is used to find the posterior probability of the joint genotypes [3]. Different approaches have also been taken in using machine learning and deep convolutional networks. One example would be the 2019 NeuSomatic caller, which uses deep convolutional networks top identify somatic mutations [4]. Newer statistical approaches also seek to challenge AI. For example, MuClone, released in 2019, uses probabilistic integration of clonal population information to identify somatic mutations in tumor datasets [7].'''

Summary of Edits:
This is an addition of a substantial section to the page on Biomarker discovery. I am adding this as an introduction to a novel approach within the field as the page currently does not have this approach described yet.

7 https://www.nature.com/articles/s42003-019-0291-z#author-information

6 https://www.researchgate.net/publication/6608685_Genomic_approach_to_biomarker_identification_and_its_recent_applications

5 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3007735/

4 https://www.nature.com/articles/s41467-019-09027-x

3 https://www.sciencedirect.com/science/article/pii/S2001037017300946#bb0455

1 https://bmcmedgenomics.biomedcentral.com/articles/10.1186/s12920-019-0508-5

2 https://www.nature.com/articles/nrg2626

Precision Medicine
Precision medicine aims at approaching a causal relation of disease pathology, treatment mechanism of action and clinical outcome (4). Biomarkers used for precision medicine, or personalized medicine, are a part of a relatively new clinical toolset. They are categorized in 3 primary ways according to their clinical applications. They are classified as molecular biomarkers, cellular biomarkers or imaging biomarkers.

All 3 types of biomarkers have a clinical role in narrowing and guiding treatment decisions and follow a sub-categorization of being either predictive or prognostic.

Predictive Biomarkers:
Predictive molecular, cellular, or imaging biomarkers that pass validation can serve as a method of predicting clinical outcomes on both the level of a treatment plan as well as outcome regardless of a specific treatment. This offers a dual approach to both seeing trends in individual cases by treatment and patient outcome regardless of treatment. For example, in metastatic colorectal cancer predictive biomarkers can serve as a way of evaluating and improving patient survival rates and in the individual case by case scenario, they can serve as a way of sparing patients from needless toxicity that arises from cancer treatment plans (1).

Prognostic Biomarkers:
Prognostic biomarkers that meet a burden of proof can serve a role in narrowing down treatment plans. This can lead to diagnosis that are significantly more precise. For example, molecular biomarkers situated at the interface of pathology-specific molecular process architecture and drug mechanism of action promise capturing aspects allowing assessment of an individual treatment response (2).

Research in Precision Medicine:
Biomarkers for precision medicine are a part of a relatively new clinical toolset. In the case of metastatic colorectal cancer (mCRC) only two predictive biomarkers have so far been identified and implemented clinically (1). In this case, the lack of data beyond retrospective studies and successful biomarker-driven approaches was suggested to be principal cause behind a need for novel biomarker studies within the medical field due to the severe attrition that accompanies clinical trials.

The field of biomarker research is also expanding to include a combinatorial approach to identifying biomarkers from multi-omic sources. Combining groups of biomarkers from various omic data allows for the possibility of developing panels that evaluate treatment response based on many biomarkers at a single time. One such area of expanding research in multi-omic biomarkers is mitochondrial DNA sequencing. Mutations in mitochondrial DNA have been shown to correlate to risk, progression, and treatment response of head and neck squamous cell carcinoma (3). In this example, a relatively low cost sequencing pipeline was shown to be able to detect low frequency mutations within tumor-associated cells. This highlights the general snapshot capability of mitochondrial DNA-based biomarkers in capturing heterogeneity.

Used Sources:

 * 1: https://ascopubs.org/doi/abs/10.1200/PO.18.00260
 * 2: https://www.biorxiv.org/content/10.1101/573402v1.abstract
 * 3: https://www.sciencedirect.com/science/article/abs/pii/S0304383519306123
 * 4: https://www.biorxiv.org/content/10.1101/573402v1.abstract

Running Sources:

 * 1) https://ascopubs.org/doi/abs/10.1200/PO.18.00260
 * 2) https://www.thieme-connect.com/products/ejournals/html/10.1055/s-0039-3401031 https://www.sciencedirect.com/science/article/pii/S2095809918306647 https://babel.hathitrust.org/cgi/pt?id=mdp.39015049497442&view=1up&seq=37 page 35


 * 1) https://babel.hathitrust.org/cgi/pt?id=uc1.31210012258412&view=1up&seq=46 pg 728 (46 in hathi)
 * 2) https://babel.hathitrust.org/cgi/pt?id=uc1.31210025025220&view=1up&seq=5 (chemistry/ organic chemistry bio markers pg 5

= Peer Review from David Lorell =

== Precision Medicine ' You don't need this title here (this is a new page right?) it's done automatically I believe. Additionally, the "Heading" format should be used as a way of breaking up the content of your page rather than as the title for the whole page. '== This is not a new page, I think you need to read the original page on biomarkers.

Precision medicine aims at approaching a causal relation of disease pathology, treatment mechanism of action and clinical outcome (4). Biomarkers used for precision medicine, or personalized medicine, are a part of a relatively new clinical toolset. They are categorized in 3 primary ways according to their clinical applications. They are classified as molecular biomarkers, cellular biomarkers or imaging biomarkers.

All 3 types of biomarkers have a clinical role in narrowing and guiding treatment decisions and follow a sub-categorization of being either predictive or prognostic.

' You probably want to have an additional citation somewhere near the end of this lead as you make a few new claims regarding the terminology and uses of these biomarkers. '

This introducition was taken from the page on Biomarkers (cellular) where they have a clinical definitions. Therefore the terminology being used / the claims regarding usage is not new and has an established base from a prior Wikipedia article. I will add a reference for clarity.

Predictive Biomarkers:
Predictive molecular, cellular, or imaging biomarkers that pass validation can serve as a method of predicting clinical outcomes on both the level of a treatment plan as well as outcome regardless of a specific treatment. This offers a dual approach to both seeing trends in individual cases by treatment and patient outcome regardless of treatment. For example, in metastatic colorectal cancer predictive biomarkers can serve as a way of evaluating and improving patient survival rates and in the individual case by case scenario, they can serve as a way of sparing patients from needless toxicity that arises from cancer treatment plans (1).

Prognostic Biomarkers:
Prognostic biomarkers that meet a burden of proof can serve a role in narrowing down treatment plans. This can lead to diagnosis that are significantly more precise. For example, molecular biomarkers situated at the interface of pathology-specific molecular process architecture and drug mechanism of action promise capturing aspects allowing assessment of an individual treatment response (2).

'It might be a good idea to separate out your description of kinds of biomarkers from your discussion on the "Research in Precision Medicine." I would suggest placing each in their own headings.'

Research in Precision Medicine:
Biomarkers for precision medicine are a part of a relatively new clinical toolset. In the case of metastatic colorectal cancer (mCRC) only two predictive biomarkers have so far been identified and implemented clinically (1). In this case, the lack of data beyond retrospective studies and successful biomarker-driven approaches was suggested to be principal cause behind a need for novel biomarker studies within the medical field due to the severe attrition that accompanies clinical trials.

The field of biomarker research is also expanding to include a combinatorial approach to identifying biomarkers from multi-omic sources. Combining groups of biomarkers from various omic data allows for the possibility of developing panels that evaluate treatment response based on many biomarkers at a single time. One such area of expanding research in multi-omic biomarkers is mitochondrial DNA sequencing. Mutations in mitochondrial DNA have been shown to correlate to risk, progression, and treatment response of head and neck squamous cell carcinoma (3). In this example, a relatively low cost sequencing pipeline was shown to be able to detect low frequency mutations within tumor-associated cells. This highlights the general snapshot capability of mitochondrial DNA-based biomarkers in capturing heterogeneity.

'You could probably use a few more citations in this research bit. Some new terminology which is not self evident could use linking, citing, or in-text clarification (ideally all three!)'

Used Sources:

 * 1: https://ascopubs.org/doi/abs/10.1200/PO.18.00260
 * 2: https://www.biorxiv.org/content/10.1101/573402v1.abstract
 * 3: https://www.sciencedirect.com/science/article/abs/pii/S0304383519306123
 * 4: https://www.biorxiv.org/content/10.1101/573402v1.abstract

Running Sources:

 * 1) https://ascopubs.org/doi/abs/10.1200/PO.18.00260
 * 2) https://www.thieme-connect.com/products/ejournals/html/10.1055/s-0039-3401031 https://www.sciencedirect.com/science/article/pii/S2095809918306647 https://babel.hathitrust.org/cgi/pt?id=mdp.39015049497442&view=1up&seq=37 page 35


 * 1) https://babel.hathitrust.org/cgi/pt?id=uc1.31210012258412&view=1up&seq=46 pg 728 (46 in hathi)
 * 2) https://babel.hathitrust.org/cgi/pt?id=uc1.31210025025220&view=1up&seq=5 (chemistry/ organic chemistry bio markers pg 5

Peer Review from James Hayman
It may be useful in linking to Multinomial logistic regression, if I am right in thinking this is where the name of the approach comes from. I would be interested in knowing a little more generally about the algorithmic approaches of these programs in discovering biomarkers. It may also be useful to define "posterior probability of the joint genotypes" an expand on that sort of thing a tad more. A brief discussion of how these techniques can be used in relation to epigentic DNA packing and implications, such as exploring transposons, viral evolutionary history, and CRISPR Cas analogues- that sort of thing- would nicely complete the article. What is done well is describing the relevant players in the field, although linking them to descriptors or classifying them by traits may clarify the article a bit.

Peer Review from William Wendt
Sorry that this is after the deadline - I was wrongly under the impression that peer review had been cancelled with the change in deadlines. I wanted to contribute something anyways. I added my comments in italics.

Precision Medicine
Precision medicine aims at approaching a causal relation of disease pathology, treatment mechanism of action and clinical outcome (4). Biomarkers used for precision medicine, or personalized medicine, are a part of a relatively new clinical toolset. They are categorized in 3 primary ways according to their clinical applications. They are classified as molecular biomarkers, cellular biomarkers or imaging biomarkers.

All 3 types of biomarkers have a clinical role in narrowing and guiding treatment decisions and follow a sub-categorization of being either predictive or prognostic.

The colons aren't necessary.

Predictive Biomarkers:
Predictive molecular, cellular, or imaging biomarkers that pass validation can serve as a method of predicting clinical outcomes on both the level of a treatment plan as well as outcome regardless of a specific treatment. This offers a dual approach to both seeing trends in individual cases by treatment and patient outcome regardless of treatment. For example, in metastatic colorectal cancer predictive biomarkers can serve as a way of evaluating and improving patient survival rates and in the individual case by case scenario, they can serve as a way of sparing patients from needless toxicity that arises from cancer treatment plans (1).

Prognostic Biomarkers:
Prognostic biomarkers that meet a burden of proof can serve a role in narrowing down treatment plans. This can lead to diagnosis that are significantly more precise. For example, molecular biomarkers situated at the interface of pathology-specific molecular process architecture and drug mechanism of action promise capturing aspects allowing assessment of an individual treatment response (2).

Research in Precision Medicine:
Biomarkers for precision medicine are a part of a relatively new clinical toolset (Duplicated choice of words?). In the case of metastatic colorectal cancer (mCRC) (<- I think this acronym is only used here, so I don't see why it's necessary to include.) only two predictive biomarkers have so far been identified and implemented clinically (1). In this case, the lack of data beyond retrospective studies and successful biomarker-driven approaches was suggested to be principal cause behind a need for novel biomarker studies within the medical field due to the severe attrition that accompanies clinical trials.

The field of biomarker research (shorten to just biomarker research) is also expanding to include a combinatorial approach to identifying biomarkers from multi-omic sources. Combining groups of biomarkers from various omic data allows for the possibility of developing panels that evaluate treatment response based on many biomarkers at a single time. One such area of expanding research in multi-omic biomarkers is mitochondrial DNA sequencing. Mutations in mitochondrial DNA have been shown to correlate to risk, progression, and treatment response of head and neck squamous cell carcinoma (3). In this example, a relatively low cost sequencing pipeline was shown to be able to detect low frequency mutations within tumor-associated cells. This highlights the general snapshot capability of mitochondrial DNA-based biomarkers in capturing heterogeneity.

Used Sources:

 * 1: https://ascopubs.org/doi/abs/10.1200/PO.18.00260
 * 2: https://www.biorxiv.org/content/10.1101/573402v1.abstract
 * 3: https://www.sciencedirect.com/science/article/abs/pii/S0304383519306123
 * 4: https://www.biorxiv.org/content/10.1101/573402v1.abstract

Running Sources:

 * 1) https://ascopubs.org/doi/abs/10.1200/PO.18.00260
 * 2) https://www.thieme-connect.com/products/ejournals/html/10.1055/s-0039-3401031 https://www.sciencedirect.com/science/article/pii/S2095809918306647 https://babel.hathitrust.org/cgi/pt?id=mdp.39015049497442&view=1up&seq=37 page 35


 * 1) https://babel.hathitrust.org/cgi/pt?id=uc1.31210012258412&view=1up&seq=46 pg 728 (46 in hathi)
 * 2) https://babel.hathitrust.org/cgi/pt?id=uc1.31210025025220&view=1up&seq=5 (chemistry/ organic chemistry bio markers pg 5