JYnapse helps university researchers, lecturers and labs turn manual, repetitive science into models that predict, classify and automate — in biology, chemistry, pharmacology, medicine, physics, forensics and beyond.
有规律的地方，就有办法。
A synapse is the tiny junction where one neuron fires a signal to the next — the spark that turns isolated cells into a thinking network. It is where the brain encodes patterns and learns.
JYnapse is that spark for your research: the junction between a researcher's domain knowledge and the power of AI. We take the signal in your data — sequences, structures, spectra, scans — and encode it into models that learn, predict and transmit intelligence back into your workflow.
It is no accident that neural networks are named after synapses. Pattern recognition, encoding, learning — that is the DNA of the name, and of the work.
Sequences in a genome. Structures in a molecule. Peaks in a spectrum. Whenever there is a pattern to find — or the same analysis repeated a hundred times — there is a place for AI.
Find the signal in raw data — sequences, spectra, images, tables.
Turn genes, molecules and measurements into features a model can read.
Classify, forecast or score the next case — with the model, not by hand.
Wrap it in a reproducible pipeline the lab can re-run forever.
Eight starting points. The pattern is always the same — repetitive analysis and a prediction waiting to be made. If your field is not here, it almost certainly still fits.
现状： Sequence alignment, gene-expression matrices and variant tables — days of wrangling in spreadsheets.
现状： Screening thousands of compounds and reading spectra by hand, one structure at a time.
现状： Pulling targets from databases and wiring compound–target–pathway networks by hand in Cytoscape.
现状： Patient datasets and scans analysed manually, with predictions locked in a lecturer's head.
现状： Expensive simulations and huge experimental logs, searched parameter by parameter.
现状： Comparing prints, ballistics and trace evidence by eye — slow and hard to reproduce.
现状： Field, soil and climate data scattered across files; disease spotted late by eye.
现状： The test is simple: is there a task your team repeats, and a call they make from experience?
The classic workflow is powerful but painfully manual. Here is the same study with the repetitive steps automated and the guesswork replaced by a model — so the researcher spends time on the science, not the plumbing.
Pull compounds & protein targets from the databases.
PDB · UniProt · TCMSPAuto-construct the compound–target–pathway graph.
STRING · KEGGScore the most promising compound–target pairs.
classifierPredict docking scores; only dock the top candidates.
saves computePublication-ready figures & an interactive dashboard.
Power BI · PlotlyCopy IDs between five websites, hand-build the network in Cytoscape, dock every compound blindly, rebuild the figures each time the data changes. Weeks of work, hard to reproduce, easy to make a slip.
One reproducible pipeline you own and re-run in an afternoon. The model points you at the candidates worth docking, the figures regenerate themselves, and the whole thing is documented for your thesis or paper.
No rip-and-replace. We build on the tools researchers already trust — visual where you want no code, scriptable where you need power, deployable when it is ready to share.
No-code, node-based pipelines your team can read, re-run and hand down to the next student.
Where you need full control: classical ML through to deep learning, with domain libraries built in.
We wire directly into the sources you already cite, so data collection stops being copy-paste.
Turning your objects into features: molecules, sequences and structures a model can learn from.
A model is only useful if the lab can use it — as a simple app, an API, or a one-click tool.
The figures that go into the thesis, the paper and the grant review — clear, correct, reproducible.
Whether it is a final-year project, a postgraduate study or a funded programme — we plug in as the AI partner your group does not have in-house.
A free first conversation. You explain the science and the bottleneck; we find where a model genuinely helps — and say so if it does not.
We prototype fast on your real data — the encoder, the model, the pipeline — and refine it with you against what "good" means for your study.
The working model becomes something the lab can run: a tool, a dashboard, a reproducible pipeline — documented for your thesis or publication.
We train your students and staff to run and extend it, so the capability stays in the group after we are done.
Like a synapse, the strength is not the size of any one cell — it is what it connects. Founder-led and AI-amplified, with the right domain experts wired in for each project.
起始价格帮你判断该敲哪扇门。每个项目都在免费的首次沟通后评估并报价。
Model, pipeline or analysis help for a final-year or postgraduate project — scoped so the student still owns the work.
A repeated analysis turned into a tool the lab runs itself — with documentation good enough to cite.
Hands-on AI and ML for a department, lab group or postgraduate cohort — in English, Malay or Chinese.
No. You bring the science and the question; we bring the model. Everything is explained in the language of your field, and the handover includes enough training for your group to keep running it.
Yes — that is usually the point. Pipelines are documented and reproducible, the methods section writes itself from the notebook, and we are happy to be acknowledged, credited or co-author depending on the contribution.
We agree in writing what data the project touches and where it lives. Sensitive or clinical data can stay inside your institution's environment; we work under NDA and ethics constraints as a matter of course.
We will say so in the first conversation, which is free. A model needs a pattern and enough examples to learn it — where either is missing, we will tell you what to collect first rather than build something that cannot work.
仅提供快速解答。联系表单由真人回复。
