# Bioinformatics Learning Milestones: Template and Examples

By the end of this article you will be able to write a bioinformatics learning milestone in a fixed, checkable format, populate a twelve-month roadmap with those milestones, and verify one of them end to end with real commands on your own computer. You will also have three copied-and-adapted milestone examples at beginner, intermediate, and advanced levels.

Before you start, have this on hand:

- A computer with at least 8 GB of RAM and about 20 GB of free disk space. Linux or macOS is simplest. On Windows, install the Windows Subsystem for Linux.
- Miniforge (a minimal conda installer) so you can create isolated software environments.
- R (version 4.x) with the `BiocManager` package, and Python 3.10 or newer.
- Git, for tracking your work.
- A text editor you like. VS Code works well for both scripts and notebooks.

You do not need a paid [bioinformatics course](/blog/careers/bioinformatics-courses-and-certifications-a-practical-guide) to do any of this. You need artifacts, dates, and proof.

## What a Bioinformatics Learning Milestone Actually Is

A milestone is a dated, verifiable checkpoint in your learning that produces an artifact. An artifact is a file or repository another person can open and judge: a rendered Quarto or Jupyter notebook, a Git repository with a working Snakemake or Nextflow file, a figure with its underlying counts table, or a container that reruns an analysis from raw data.

This definition matters because the most common failure mode when people set out to learn bioinformatics is accumulating course completions without artifacts. A certificate proves you sat through material. A milestone proves you produced something reproducible.

The need for well-defined checkpoints is documented in professional training itself. A review of bioinformatics education in pathology training found that most residents receive little formal exposure to bioinformatics during medical education, and the authors listed ill-defined educational milestones as one of the specific barriers, alongside limited faculty expertise and the apprenticeship model of clinical training [1]. The same review named the floor competencies that any milestone set should cover: data structure, data pipelines, data manipulation, and data regulations [1]. Those four areas are a sensible skeleton for any self-directed plan.

It also helps to separate two uses of the word "milestone." Historical milestones, such as the [Human Genome](/blog/guides/human-genome) Project and the launch of the ENCODE initiative in 2003, mark turning points in the field [2][3]. Learning milestones mark turning points in you. This article is about the second kind, and the historical kind only appears as content you will eventually need to understand.

## The Bioinformatics Learning Milestones Template

![Eight-step flow showing the milestone template fields from skill statement through stretch goal, each with a short example of a strong fill-in.](/article-images/illustrations/bioinformatics-learning-milestones-milestone-template-fields.webp)

Every milestone should contain eight fields. If a milestone cannot fill all eight fields, it is a wish, not a milestone. The bioinformatics learning milestones format below is deliberately terse. Fill it in a plain text file inside your project repository so the plan and the evidence live together.

```text
MILESTONE-00X: <short name>
Skill statement: I can <observable verb> <object> using <tool> to <outcome>.
Prerequisites: MILESTONE-00Y, MILESTONE-00Z
Evidence artifact: <exact file or repository path>
Verification method: <how a reviewer checks it, in one sentence>
Time box: <start date> to <end date>
Pass criteria: <specific, binary checks>
Stretch goal: <optional, the next 20 percent>
```

Each field has a job:

| Field | What it forces you to decide | Weak fill-in vs. strong fill-in |
|--|--|--|
| Skill statement | What you can do, not what you know | "Understand RNA-seq" vs. "I can quantify a paired-end RNA-seq sample with salmon and report the mapping rate" |
| Prerequisites | Dependency order | "None" on a pipeline milestone signals a plan built on sand |
| Evidence artifact | The file a stranger can open | "My notes" vs. "analysis/01_qc_report.html and qc_summary.tsv" |
| Verification method | How someone else checks it | "I feel confident" vs. "Reviewer runs `multiqc .` and sees all four samples summarized" |
| Time box | Real dates, not "someday" | "Q3" vs. "2026-12-01 to 2026-12-21" |
| Pass criteria | Binary, checkable conditions | "Good results" vs. "All samples pass per-base quality above Q20, mapping rate above 70 percent, PCA plot separates the two groups" |
| Stretch goal | Where the real growth is | Optional but written down |

The pass criteria row deserves a hard rule: if you cannot write a check that returns pass or fail, lower the milestone until you can. "Learn Python" fails this test. "Write a Python function that takes a FASTA file and returns the GC content of each record, with a test that runs against a five-sequence file" passes it.

## A Filled-In Example Milestone

Here is a complete bioinformatics learning milestones example, written the way it should appear in your repository.

```text
MILESTONE-004: Quantify one RNA-seq sample
Skill statement: I can download a paired-end human RNA-seq
  sample from SRA, quality-check it, trim adapters, and produce
  a salmon quantification with a transcript-level count table.
Prerequisites: MILESTONE-001 (Unix basics), MILESTONE-002
  (conda environments), MILESTONE-003 ([FASTQ format](/knowledge/bioinformatics/fastq-file-format-phred-scores-qc))
Evidence artifact: rnaseq-mini/ containing environment.yml,
  run_qc.sh, trimmed FASTQ files, salmon_out/quant.sf, and
  multiqc_report.html
Verification method: A reviewer runs `conda env create -f
  environment.yml` then `bash run_qc.sh` in a fresh directory and
  gets a quant.sf with a NumReads column that sums to a plausible
  library size.
Time box: 2026-12-01 to 2026-12-21
Pass criteria:
  1. FastQC summary has no red "fail" modules after trimming.
  2. Salmon reports a mapping rate above 70 percent.
  3. quant.sf contains columns Name, Length, EffectiveLength,
     TPM, NumReads.
  4. Every step is reproducible from the repo without editing.
Stretch goal: Add a second sample and compare TPM distributions.
```

Notice what this format does. It converts a vague intention ("learn RNA-seq") into a contract with a due date, a command a stranger can run, and numeric thresholds you can defend.

## A Twelve-Month Milestone Roadmap

The following roadmap assumes roughly eight hours per week. Adjust the pace, not the order. Each block lists the milestone skill and the artifact that proves it.

**Months 1 and 2, command line and environments.** Milestones cover the Unix shell, file formats (FASTA, FASTQ, SAM, BAM, VCF, GTF), conda environment creation and export, and Git basics. Artifacts: a cheat sheet of twenty shell commands you actually used, an `environment.yml` file, and a Git repository with at least fifteen meaningful commits.

**Months 3 and 4, Python for sequence data.** Milestones cover parsing FASTA and FASTQ without heavyweight frameworks, writing functions with tests, and computing sequence statistics. Artifacts: a tested module with functions for GC content, reverse complement, k-mer counting, and a rendered notebook that applies them to a small genome. A good capstone here is the frequency matrix chaos game representation covered in the worked examples below, because chaos game representation is a classic milestone in graphical bioinformatics that turns sequences into images usable for alignment-free comparison and machine learning feature encoding [4].

**Months 5 and 6, R and statistics.** Milestones cover data frames, tidyverse basics, ggplot2, and one linear model applied to a real table. Artifacts: a notebook that reads a counts table, plots distributions, and fits and interprets one model. R matters because core differential analysis tools are R packages, and because statistics is where most self-taught analysts are weakest.

**Months 7 and 8, a complete small pipeline.** Milestones cover quality control, trimming, alignment or quasi-mapping, quantification, and a first differential expression table. Artifacts: the `rnaseq-mini` repository from the walkthrough below. The NGS-to-downstream-analysis arc you practice here mirrors the structure of published domain pipelines, including recent integrative miRNA frameworks that span NGS data processing through downstream analysis [5].

**Months 9 and 10, a domain project with a question.** Pick one domain and go deep. Genomic options include metagenome-resolved analysis with amplicon or shotgun data, where modern work now routinely produces metagenome-assembled genomes from long-read and short-read data [6]. Transcriptomic options include a second RNA-seq dataset with a published design, replicated end to end. The artifact is a report answering one biological question with a figure, a table, and a methods paragraph you could paste into a manuscript.

**Months 11 and 12, scale, reproducibility, and one advanced method.** Milestones cover workflow managers (Snakemake or Nextflow), containers, and one advanced topic. Advanced options with strong current literature include machine learning for [genomic prediction](/knowledge/bioinformatics/genomic-prediction-in-livestock-a-decision-framework-for-breeders) and classification, where comparative benchmarks of deep learning, [random forest](/blog/data-analysis/what-is-random-forest), gradient boosting, and extreme gradient boosting are now standard practice [7], graph-based machine learning for biomolecular data [8], and structure prediction, where deep learning methods have made modeling feasible even for proteins from poorly characterized lineages [9]. The artifact is a pipeline that reruns your Months 9 and 10 project on a fresh machine from two commands.

This sequence deliberately defers machine learning. A recurring failure pattern in self-directed learners is jumping to neural networks before they can build a counts table. The field's own reviews make the same point implicitly: advanced methods sit on top of mature data pipelines, and their value depends on the quality of the data representation and the train and test splits beneath them [10][11].

## Worked Example: Verifying a Pipeline Milestone Step by Step

This walkthrough completes MILESTONE-004 from the filled example above. Every command is standard. Example output numbers are illustrative, since your values depend on your data.

### Step 1: Create and export the environment

```bash
conda create -n rnaseq -c conda-forge -c bioconda \
  fastqc fastp salmon multiqc -y
conda activate rnaseq
conda env export > environment.yml
```

Parameters that matter:

- `-n rnaseq` names the environment. Always use one environment per project.
- `-c conda-forge -c bioconda` sets channel priority. Bioconda packages live on top of conda-forge, and this order avoids most solver conflicts.
- `conda env export` writes the exact package versions to a file, which is what makes the milestone reproducible.

The expected output of the create step ends with:

```text
# To activate this environment, use
#     $ conda activate rnaseq
```

### Step 2: Quality check the raw reads

```bash
mkdir -p data qc
fastqc data/sample_R1.fastq.gz data/sample_R2.fastq.gz -o qc/
```

FastQC writes one HTML report per file. Open `qc/sample_R1_fastqc.html` and read the Basic Statistics and Per base sequence quality modules first. A green pass on the per-base quality plot before trimming is uncommon for real data, which is exactly why the next step exists.

### Step 3: Trim adapters and low-quality ends

```bash
fastp -i data/sample_R1.fastq.gz -I data/sample_R2.fastq.gz \
      -o qc/trimmed_R1.fastq.gz -O qc/trimmed_R2.fastq.gz \
      --html qc/fastp.html --json qc/fastp.json \
      --qualified_quality_phred 20 --length_required 50
```

Parameters that matter:

- `-i` and `-I` take read 1 and read 2. Pairing is preserved because fastp processes both files together.
- `-qualified_quality_phred 20` keeps bases at Phred quality 20 or higher, which corresponds to a 1 percent chance of a miscall per base.
- `-length_required 50` discards reads shorter than 50 bases after trimming, which prevents tiny fragments from inflating downstream mapping noise.
- `-html` and `-json` save the report so your evidence artifact includes it.

The terminal output ends with a summary block like this:

```text
Read1 before filtering:
    total reads: 1000000
    total bases: 90000000
Read1 after filtering:
    total reads: 968542
    total bases: 83412904
Q20 bases: 81127403(97.2571%)
```

Check that total reads after filtering is not catastrophically lower than before. A drop above roughly 20 percent on good-quality data usually means wrong adapter settings, not bad reads.

### Step 4: Build the index and quantify

```bash
salmon index -t transcripts.fa -i salmon_index -k 31
salmon quant -i salmon_index -l A \
  -1 qc/trimmed_R1.fastq.gz -2 qc/trimmed_R2.fastq.gz \
  -p 8 -o salmon_out
```

Parameters that matter:

- `-t` is the target transcriptome FASTA. Download the same reference version your lab or the published study used and record the accession.
- `-k 31` is the k-mer size. The default of 31 works for standard 75 to 150 base reads. Shorter reads need smaller k.
- `-l A` tells salmon to infer the library type automatically, which handles stranded and unstranded libraries without guessing.
- `-p 8` uses eight threads. Match this to your machine.
- `-validateMappings` is needed on older salmon versions to enable selective alignment. Newer versions use selective alignment by default, so check your version with `salmon, version`.

The log prints lines like:

```text
Mapping rate 82.314%
```

That single number is a pass criterion in the milestone. Below 70 percent on a well-annotated organism, look for the wrong reference, wrong library type, or leftover adapter sequence.

### Step 5: Aggregate QC and commit

```bash
multiqc qc/ -o qc/
git add environment.yml run_qc.sh qc/ salmon_out/quant.sf
git commit -m "MILESTONE-004: quantify one RNA-seq sample"
```

MultiQC collapses every FastQC and fastp report into one `multiqc_report.html`. This file is the evidence artifact a reviewer opens first.

### Step 6: Differential expression in R

Save the counts table and a metadata table, then run:

```r
library(DESeq2)

cts <- read.delim("counts.tsv", row.names = 1)
coldata <- data.frame(
  condition = factor(c("control", "control", "treated", "treated"),
                     levels = c("control", "treated"))
)
rownames(coldata) <- colnames(cts)

dds <- DESeqDataSetFromMatrix(countData = cts,
                              colData = coldata,
                              design = ~ condition)
dds <- DESeq(dds)
res <- results(dds)
head(res[order(res$padj), ])
```

Expected output looks like:

```text
              baseMean  log2FoldChange      lfcSE      stat        pvalue         padj
GENE0001      412.37        2.411          0.512      4.71     2.5e-06       0.0031
GENE0233     1890.05       -1.208          0.298     -4.05     5.1e-05       0.0298
```

Columns you must be able to explain out loud are `log2FoldChange`, `pvalue`, and `padj`, the adjusted p-value that controls the false discovery rate across thousands of simultaneous tests.

### Step 7: Prove it reruns

Close the terminal, delete the generated outputs, and run the whole thing from the repository in one pass. If any step required you to remember something not written in a script or the README, the milestone is not done. Reproducibility is not decoration. Working groups across drug discovery and data science now list transparent, reproducible data processing as a first-class requirement for any analysis that others will build on [10], and multi-center collaborations identify mismatched data standards and governance as the practical friction points that reproducible local practices help prevent [12].

### Common errors and their exact fixes

- `zsh: command not found: fastqc`. You did not activate the environment. Run `conda activate rnaseq` and retry.
- `CondaError: UnsatisfiableError` during creation. You mixed channels or pinned conflicting versions. Delete the partial environment with `conda env remove -n rnaseq`, then create it again with only the channels shown above. If it still fails, use `mamba`, which gives clearer solver messages.
- `salmon: error while loading shared libraries` on a manually downloaded binary. Your system libraries do not match the prebuilt binary. Install salmon through conda instead, which bundles compatible libraries.
- `there is no package called 'DESeq2'` in R. Install it with `BiocManager::install("DESeq2")`, not `install.packages`, because it is a Bioconductor package.
- `Error in DESeqDataSetFromMatrix: all genes have equal values for all samples`. Your count matrix is transposed, with genes as columns and samples as rows. Transpose it with `t(cts)` or fix the export from the previous step.

### Key options and formulas at a glance

| Tool | Command | Key parameter | What it controls |
|--|--|--|--|
| conda | `conda create -n name -c bioconda pkgs` | `-c` channel order | Where packages resolve from, and therefore whether the solve succeeds |
| FastQC | `fastqc *.fastq.gz -o qc/` | output module set | Which QC checks appear in the HTML report |
| fastp | `fastp -i R1 -I R2 -o t1 -O t2` | `-qualified_quality_phred` | Per-base quality cutoff for sliding-window trimming |
| fastp | same | `-length_required` | Minimum read length kept after trimming |
| salmon | `salmon index -t ref.fa -i idx` | `-k` | k-mer size for the index, default 31 |
| salmon | `salmon quant -i idx -l A` | `-l A` | Automatic library type inference |
| multiqc | `multiqc qc/` | search directory | Which tool reports get aggregated |
| DESeq2 | `results(dds)` | contrast | Which factor levels define the comparison |

## Three Milestone Examples You Can Copy

### Beginner: a sequence encoder with tests

Write a Python function that converts a DNA sequence into its frequency chaos game representation. Chaos game representation maps a sequence onto a two-dimensional square by iteratively moving a point halfway toward the corner labeled by each base, and the frequency variant, called FCGR, turns sequences of any length into equal-sized images or matrices. That property makes FCGR useful for alignment-free sequence comparison and as feature encoding for machine learning models [4].

```python
import numpy as np

def fcgr(sequence, k=2):
    corners = {"A": (0, 0), "C": (0, 1), "G": (1, 1), "T": (1, 0)}
    n = 2 ** k
    matrix = np.zeros((n, n), dtype=int)
    x, y = 0.5, 0.5
    for base in sequence.upper():
        if base not in corners:
            continue
        cx, cy = corners[base]
        x = (x + cx) / 2
        y = (y + cy) / 2
        col = min(int(x * n), n - 1)
        row = min(int(y * n), n - 1)
        matrix[row, col] += 1
    return matrix

assert fcgr("ACGT", k=2).sum() == 4
assert fcgr("AAAA", k=2)[0, 0] == 4
```

Pass criteria: the function handles lowercase input, skips ambiguous bases, and passes both assertions. Stretch goal: plot the matrix with matplotlib and compare FCGR images of two related and two unrelated sequences.

### Intermediate: an end-to-end bacterial resistance analysis

Reproduce the shape, not the exact data, of a published antimicrobial resistance study. The reference design worked from 662 Streptococcus pneumoniae genomes, ran a genome-wide association study on trimethoprim resistance, validated candidate mutations in the folA and sulA loci functionally, and then trained machine learning models to predict minimum inhibitory concentration, reaching 86.3 percent average accuracy within one twofold dilution of the observed MIC [13]. Your version: download twenty genomes of one species, call variants, run a simple association of variants against a phenotype you obtain from metadata, and write up which loci come out. The artifact is a notebook plus a short report. The learning target is the in silico analysis loop, and the study you are modeling shows explicitly why wet-lab or literature validation of candidates belongs in the plan [13].

### Advanced: integrate a machine learning layer onto your pipeline

Take your Months 9 and 10 project and add a supervised model with an honest evaluation. The bar you are copying comes from studies that combine pathway-aware feature construction with classifiers, in the pattern set by integrated miRNA oncology frameworks that chain NGS processing, benchmarked tools such as miRDeep2 and DIANA-miRPath, and then supervised models including support vector machines, convolutional neural networks, and recurrent neural networks [5]. Read before you build. Field-level reviews of AI in biology catalog the landmark systems, from DeepBind for binding prediction to AlphaFold for structure, and consistently flag data quality, model interpretability, and computational cost as the constraints that decide whether a model means anything [11]. In drug discovery, graph machine learning earned industry adoption only after producing results such as repurposed drugs advancing into in vivo studies, which is a useful calibration for how much evidence an advanced model must generate [8]. Your pass criteria: a train and test split decided before model fitting, a baseline model (logistic regression) that the fancy model must beat, uncertainty or cross-validation on the headline metric, and a paragraph stating what the model cannot tell you.

## How to Verify and Score a Milestone

![Flowchart of the verification path: artifact opens, commands rerun from a clean directory, and the learner explains tool and parameter choices.](/article-images/illustrations/bioinformatics-learning-milestones-pipeline-verification-path.webp)

Verification has three layers. Score all three on every milestone.

**Layer 1, does the artifact exist and open.** The reviewer (future you counts) opens the notebook, report, or repository within thirty seconds and sees the claimed content. No broken image links, no missing files.

**Layer 2, does the artifact rerun.** From a clean directory, the documented commands produce the outputs. For conda-based projects, this check is `conda env create -f environment.yml` followed by the pipeline entry script. Export the environment file after any package change, because a stale `environment.yml` is the most common silent failure in student repositories.

**Layer 3, can you explain it.** Answer three questions out loud, recorded or written: why this tool, why these parameters, and what would change your conclusion if a parameter moved. This layer separates operators from analysts, and it is the layer most often skipped.

A simple scoring rubric:

| Layer | 0 points | 1 point | 2 points |
|--|--|--|--|
| Artifact exists | Missing or broken | Exists, hard to interpret | Opens in 30 seconds, self-explanatory |
| Reruns | Does not run | Runs after manual fixes | Two commands from a clean clone |
| Explained | Cannot answer | Answers with hand-waving | Answers with parameter-level specifics |

A milestone passes at five of six points. Anything less rolls into the next time box with a written note about what failed. When your project touches shared or human data, add a fourth check: consent basis, data standards, and governance match the requirements of the source dataset, because those are the documented barriers that stall real multi-center data work [12].

## Common Mistakes and Limitations

**Collecting courses instead of artifacts.** Enrolling in another bioinformatics course feels like progress and often is not. If you have completed three courses but have no repository a stranger can run, redirect that time to MILESTONE-004 above.

**Doing everything in the conda base environment.** Installing into `base` eventually produces dependency conflicts you cannot undo cleanly. One project, one environment, always exported.

**Skipping Git until "the project is real."** The first moment you need version control is the moment you broke something and cannot say what changed. Initialize the repository on day one of every project.

**Copying tutorial code without breaking it on purpose.** Change an input, corrupt a file, run with a wrong flag, and read the error. The error-reading habit is the actual skill. A learner who has deliberately triggered `CondaError: UnsatisfiableError` and fixed it will not freeze the first time it happens on a cluster.

**Treating a passing command as a validated result.** Salmon reporting a mapping rate does not mean the biology is right. Every result needs a sanity check against something independent, such as housekeeping genes in RNA-seq or known resistance loci in the AMR example [13].

**Choosing datasets too big for your hardware.** A single 30x human genome BAM is tens of gigabytes, and iterative assembly or metatranscriptomic workflows scale far beyond casual laptops [6]. Start with bacteria, yeast, or published subsets.

**Confusing milestones with calendars.** If a time box expires and the artifact is incomplete, the correct response is to shrink the milestone, not to extend the deadline indefinitely. Shrinking preserves the habit of finishing.

**Limitations of this plan.** The twelve-month roadmap is a structure, not a guarantee. Pace depends on your background, your available hours, and how often you get stuck productively versus destructively. The published frameworks referenced here describe the state of methods and training at a field level, and none of them certify your individual competence. Only your artifacts do.

## Frequently Asked Questions

### What is a bioinformatics learning milestone?

A bioinformatics learning milestone is a dated, verifiable checkpoint that produces an artifact, such as a runnable notebook or repository, and states binary pass criteria. It differs from a course completion because the evidence is something a reviewer can open and rerun, not a certificate.

### How long does it take to learn bioinformatics?

With roughly eight hours per week, a realistic path to running and explaining a complete small analysis pipeline is about twelve months, following the roadmap above. Reaching independent research-level competence in one domain takes longer and depends on your starting background.

### Should I learn R or Python first for bioinformatics?

Either works, and the roadmap above suggests Python first for sequence manipulation and R second for statistics and differential expression. Both are eventually required, because core genomics tooling exists in each.

### Can I learn bioinformatics without a biology degree?

Yes, but budget extra time for the biology that your chosen domain assumes. Structure your milestones so each pipeline project forces you to learn the underlying biology, which is more durable than studying it separately first.

### What is a good first bioinformatics project?

One that fits on your laptop and produces a count table and a plot, such as quantifying a single small RNA-seq sample with FastQC, fastp, and salmon as in the walkthrough above. Completeness and reproducibility matter more than biological novelty at this stage.

### How do I know when a milestone is complete?

The milestone is complete when its three verification layers pass: the artifact opens, the documented commands rerun it from a clean directory, and you can explain the tool and parameter choices out loud. Score it with the rubric and require five of six points.

### Are structured bioinformatics courses worth taking?

They are worth taking when the course requires you to produce graded, runnable artifacts, and they are low value when they only deliver videos. Use a course as scaffolding for specific milestones rather than as the plan itself.

### Which tools should I master first?

Master the shell, conda, Git, FastQC, fastp or an equivalent trimmer, one aligner or quasi-mapper such as salmon, MultiQC, and DESeq2 in R. This stack covers the most common genomics workflow shapes and every later tool attaches to it.

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## Sources

1. [Bioinformatics Education in Pathology Training: Current Scope and Future Direction.](https://pubmed.ncbi.nlm.nih.gov/28469393/)
2. [The historical evolution, technological transformation, and future vision of personalized medicine: a narrative review.](https://pubmed.ncbi.nlm.nih.gov/42575570/)
3. [From tradition to innovation: conventional and deep learning frameworks in genome annotation.](https://pubmed.ncbi.nlm.nih.gov/38581418/)
4. [Chaos game representation and its applications in bioinformatics.](https://pubmed.ncbi.nlm.nih.gov/34900136/)
5. [MicroRNA bioinformatics in precision oncology: an integrated pipeline from NGS to AI-based target discovery.](https://pubmed.ncbi.nlm.nih.gov/41168533/)
6. [Thermophiles in the genomic Era (2015-2025): a review on biodiversity, metagenome-assembled genomes, and future directions.](https://pubmed.ncbi.nlm.nih.gov/41532487/)
7. [Genomic prediction of pig growth traits based on machine learning.](https://pubmed.ncbi.nlm.nih.gov/37872114/)
8. [Utilizing graph machine learning within drug discovery and development.](https://pubmed.ncbi.nlm.nih.gov/34013350/)
9. [A new age in structural S-layer biology: Experimental and in silico milestones.](https://pubmed.ncbi.nlm.nih.gov/40345586/)
10. [A data science roadmap for open science organizations engaged in early-stage drug discovery.](https://pubmed.ncbi.nlm.nih.gov/38965235/)
11. [AI and Machine Learning in Biology: From Genes to Proteins.](https://pubmed.ncbi.nlm.nih.gov/41154856/)
12. [A roadmap for navigating child health research data sharing across Canada and beyond - building on UCAN CAN-DU.](https://pubmed.ncbi.nlm.nih.gov/40898230/)
13. [Holistic understanding of trimethoprim resistance in Streptococcus pneumoniae using an integrative approach of genome-wide association study, resistance reconstruction, and machine learning.](https://pubmed.ncbi.nlm.nih.gov/39120145/)