Update docs

Author: mrueda

docs-site/docs/faq.mdx

@@ -9,7 +9,7 @@ Frequently Asked Questions
 <details>
 <summary>What does `Pheno-Ranker` do?</summary>
 
-`Pheno-Ranker` is an open-source toolkit developed for the semantic similarity analysis of phenotypic and clinical data. It natively supports GA4GH standards, such as [Phenopackets v2](/pxf) and [Beacon v2](/bff), using as input their JSON/YAML data exchange formats. Beyond these specific standards, Pheno-Ranker is designed to be highly versatile, capable of handling any data serialized into `JSON`, `YAML`, and `CSV` (categorical) formats, extending its utility beyond the health data domain. Pheno-Ranker transforms hierarchical data into binary digit strings, enabling efficient similarity matching both within cohorts and between individual patients and reference cohorts. 
+`Pheno-Ranker` is an open-source toolkit developed for the semantic similarity analysis of phenotypic and clinical data. It natively supports GA4GH standards, such as [Phenopackets v2](/pxf) and [Beacon v2](/bff), using as input their JSON/YAML data exchange formats. Configured `JSON`, `YAML`, and categorical `CSV` data can also be compared, including datasets outside the health domain. Pheno-Ranker transforms hierarchical data into binary digit strings, enabling efficient similarity matching both within cohorts and between individual patients and reference cohorts.
 
 </details>
 <details>

docs-site/docs/federated-version-proposal.mdx

@@ -9,7 +9,7 @@ import TabItem from '@theme/TabItem';
 
 # Proposal: Implementing Pheno-Ranker in a Federated Network
 
-In this proposal, we aim to explore the potential application of Pheno-Ranker within two distinct contexts: the Inter-Hospital Network and the Beacon v2 Network.
+This proposal considers two deployment contexts for Pheno-Ranker: an inter-hospital network and a Beacon v2 network.
 
 <figure>
  Federated network diagram
@@ -98,13 +98,13 @@ flowchart LR
 
 Currently, the `/individuals` endpoint from the Beacon v2 API can be queried, and, as long as the user has appropriate access to the record-level data, the response can be parsed and saved as a text file (i.e., `BFF`). `Pheno-Ranker` can then be executed locally through the command-line interface (CLI), Perl module, or legacy Web App UI, which will be supported until the end of 2026. While this approach works, it becomes cumbersome when the goal is to search for similar patients across multiple Beacon instances.
 
-To facilitate `Pheno-Ranker`’s integration into the Beacon v2 API ecosystem, we propose two distinct pathways for query submission to enhance flexibility and security (both requiring `POST` requests):
+Two `POST` query designs could support Pheno-Ranker within a Beacon v2 network:
 
-1. The first method mirrors the approach used in hospital networks, where queries leverage a precomputed vector. This ensures secure and efficient similarity evaluations against an existing database. To support this, a Beacon aggregator would periodically gather ontology terms via the _filtering_terms_ endpoint from each Beacon v2 API, thereby creating a global lookup table.
+1. The first method uses a precomputed vector, as in the inter-hospital model. A Beacon aggregator would periodically collect ontology terms from the _filtering_terms_ endpoint of each Beacon v2 API to create a shared lookup table.
 
-2. Alternatively, centers may submit queries using actual JSON data (either `BFF` or `PXF` objects), which should be anonymized or meet the network’s security standards. This option allows the recipient site to perform similarity analyses either on their precomputed data or on-the-fly using Pheno-Ranker’s CLI or module, offering greater adaptability.
+2. Alternatively, centers may submit `BFF` or `PXF` objects that meet the network's privacy and security requirements. The recipient site can compare them with precomputed data or run Pheno-Ranker on demand.
 
-The response schema can either adhere to the Beacon v2 specification or be adapted to include similarity metrics, enhancing the utility and adaptability of the integration.
+The response schema could follow the Beacon v2 specification or extend it with similarity metrics.
 
 <details>
 <summary>Draft Proposal for the JSON Schema of the `phenoRanker` Query Parameter</summary>

docs-site/docs/qr-code-generator.mdx

@@ -17,17 +17,14 @@ As a proof of concept, we created companion command-line utilities that encode/d
 <details>
 <summary>About the utility of QR codes</summary>
 
-2D barcodes are straightforward and easy to scan, typically using a smartphone camera. They offer the convenience of encoding various data. Below are a few examples:
+QR codes can attach an encoded profile to a report or transfer it between controlled systems. Possible uses include:
 
-* Enrolling in clinical trial or health data program via QR
-* Patient-doctor transfer of  information
+* Enrolling in a clinical trial or health data program
+* Transferring information between a patient and doctor
 * Medical reports with augmented data
-* Clinical trials reports (see example below)
-
-Of course if you plan to use `Pheno-Ranker` with non pheno-clinical data you will need to come up with your own examples :smile:.
+* Clinical trial reports (see example below)
 
 </details>
-Ok, let's convert `Pheno-Ranker` data to QRs.
 
 :::danger[Experimental feature]
 This feature serves as a **proof of concept**. The generated QR code images exclusively encode `Pheno-Ranker` data and do not include any clinical information. To decode these images back into phenotypic-clinical data, a specific `template` is required. In a production environment, implementing an additional security layer, such as AES encryption, is recommended to enhance data safety.

docs-site/docs/vcf.mdx

@@ -9,7 +9,7 @@ import TabItem from '@theme/TabItem';
 
 # Processing VCF files
 
-In this page, we aim to explore the full potential of `Pheno-Ranker`. Our focus will be on processing a [VCF](https://en.wikipedia.org/wiki/Variant_Call_Format) file - a challenging yet intriguing task.
+This page shows how to compare samples from a multisample [VCF](https://en.wikipedia.org/wiki/Variant_Call_Format) file with `Pheno-Ranker`.
 
 <details>
 <summary>A VCF is essentially a specialized form of a TSV</summary>
@@ -36,13 +36,11 @@ The dataset `test_1000G.vcf.gz` is a subset extracted from the 1000 Genomes Proj
 
 </details>
 :::danger[About VCF size and content]
-The idea here is to compare samples (or individuals if you will) by their genomic variations, like if we were comparing a genomic fingerprint. A good example for this would be comparing samples in a **multi-sample** `VCF` from a **gene panel** (or even an Exome) after filtering variations.  This method of course could be complemented by adding the phenotypic information on top of the genomic variations, and then use weights. etc.
+This workflow compares samples by their genomic variants, effectively treating them as genomic fingerprints. It is best suited to a filtered **multisample** `VCF` from a **gene panel** or exome. Phenotypic variables and weights can also be added when the comparison requires both genotype and phenotype information.
 
 :exclamation: We advise against using this method for comparing samples with millions of genomic variations.
 :::
-Let's go!
-
-### Step1: Tranpose the VCF to TSV
+### Step 1: Transpose the VCF to TSV
 
 We are going to be using the included [Python script](https://github.com/CNAG-Biomedical-Informatics/pheno-ranker/blob/main/utils/csv2pheno_ranker/vcf/vcf2pheno-ranker.py)