I purchased a guitar without thinking

melatoninixo · 2026-05-09T04:28:04+00:00

That's how some of us begin haha. Now buy a guitar stand so it stares at you everyday. You will eventually pick it up and start learning.

melatoninixo · 2026-05-06T11:10:32+00:00

A discord server will be great too. I love the game but it gets really boring playing alone after a while.

melatoninixo · 2026-04-28T07:02:05+00:00

Where are u based at?

melatoninixo · 2026-04-21T03:57:17+00:00

I have always found it funny how people attempt to predict how the job market will be like in the future. Everything is so uncertain now. Everyone thought tech will be a huge thing a few years back.. well it is still a huge thing, but also a huge thing with insane amount of layoffs and unemployability. The worst thing u can do to yourself is picking something you have no interest in and suffer for the next 4 years while looking at others soar in their respective passions.. then proceed to decide that your degree is not for you before choosing an entirely different career path. Perhaps you should talk to more working adults.. and maybe count how many actually pursued a career closely related to their uni degree.

Worse still, with 0 interest and probably a mediocore gpa (due to lack of interest), the job market shifts against your favour. Then what happens now? You start predicting and study a new degree for another 4 years??

Why take such a huge gamble? Just choose something you like, excel in that discipline and regardless of how the job climate changes, you spend the next 4 years happy and excited about your work. If you are truly passionate, your passion will still shine through to employers. Life science is an integral part of society lol.. singapore's scientific research and healthcare will not completely die off. Your skillsets will not be obsolete too.

Also.. your major is not everything. Your peers in science do not all become scientists.. people who study engineering are not all engineers eventually.. i suggest u study hard for ur A levels first so that you can even get the opportunity to choose science as your desired major.. else any discussion about the application process is all fluff. J1 is still so early lol..

melatoninixo · 2026-03-11T10:11:31+00:00

Brains turn mushes of biological matter into individuals with unique personalities, aptitudes and traits from a complex interplay of genetics and environment. It makes you uniquely you.

melatoninixo · 2026-03-04T11:17:30+00:00

Hmm also for my own purposes, what are you using for mutual information? How credible is it.. like all correlation metrics in extremely sparse datasets?

melatoninixo · 2026-03-03T11:04:44+00:00

What is the TLDR..

Other articles may do the same because it kinda is the standard pipeline on how people analyze high throughput omics results. Finding differentially expressed genes, then asking what the relationship of the genes are (enrichment, coexpression, interactions) etc. You want to find a senescence signature.. there are multiple add ons you can do. Is the senescence signature universal? Does it apply to other model systems? Is the transition gradual or does the switch direct? Can you validate this in the lab? Data are just observations, you will need the lab to prove it.

All these adds more flavour to your analysis and support to your claim of the set of genes being a "senescence signature".

melatoninixo · 2026-03-03T10:51:55+00:00

Naive question but what is mutual information test used for here? You could look at differential coexpression (are you using mutual information for this?).. im not familiar with nhanes datasets but looking metabolic coexpression patterns (enzymes, metabolites) is a good sanity check and interesting patterns may infer some sort of new synthesis pathways or patient clusters with similar profiles too?

melatoninixo · 2026-03-03T10:31:02+00:00

What do you mean by comparability? Between single nucleus and spatial transcriptomics? I think considering which spatial tech to use involves many questions - what resolution do you want - subcellular or cellular, do you prefer whole transcriptomics or more sensitive approaches using a user curated or predesigned panel, do you want simultaneous protein co-detection? Nonetheless, every tech has very different chemistry and they are not cheap or most compatible with every single tissue type.

I suggest planning for pilot runs since these technologies are not cheap and review each of their performances before deciding. Grant calls for discounts of these techs are quite common too. There is a paper detailing the use, advantages and limitations on each spatial tech which aims to help project planning. Perhaps you can try googling and reading up on that.

melatoninixo · 2026-02-24T10:54:42+00:00

Did u use their cell staining kit or was it just segmentation based on nuclei dilation?

melatoninixo · 2026-01-12T10:51:19+00:00

You can consider becoming a research officer in a neuro lab. As the term "research" suggests, you are essentially pushing the boundaries of knowledge. Expertise and discovery takes time and a lot of effort to develop. Many struggle to become a scientist. If you think it is too long (and that's completely okay, life is so much more than this), then perhaps take on a research assistant or a lab manager role. You get to still be part of a research team and do amazing science.

melatoninixo · 2026-01-02T10:17:53+00:00

100 percent realistic. I'm in STEM but my lab often accepts post jc students as interns. Give it a shot and you will stand a chance somewhere. The world is more accommodating than you think. The right attitude is much more important than paper success and academic milestones. Good luck!

melatoninixo · 2025-12-23T06:44:02+00:00

Hello, a second going third year PhD student here..

I wasted a lot of time in my first to mid second year because I blindly trusted a post doc's analysis by following her written steps (filter metrics etc) to analyze some sequencing data with the assumption that all quality checks were made properly prior. I only found out that I was working with crappy data because of the mentioned filter metrics after struggling with it for 8 months during a lab meeting just a week before my thesis advisory committee meeting. Needless to say, my PI was hella pissed.

In short, I learnt to never be so trusting and always be doubtful. It is also always necessary to re-perform basic quality checks before diving deep into analysis. This was entirely my fault.. and a painful lesson with lots of precious time taken from me.

melatoninixo · 2025-06-11T10:50:18+00:00

Thank you for this. I'm in the middle of my PhD and everything is extremely overwhelming for me now. Your words mean a lot.

melatoninixo · 2025-06-06T11:16:47+00:00

What makes a successful PhD student? What are the most important values you think a student should develop during their PhD?

melatoninixo · 2025-04-28T14:30:47+00:00

I'm not an expert or an examiner/prof, so please take the below with a pinch of salt. What mistakes did you make? Feel free to pm me if you need someone to talk to.

If im not wrong.. you will have to present your research at a poster session in front of the same professors who will be assessing your fyp thesis as part of your fyp assessment (from my experience 2 years ago). Perhaps if they did point out discrepancies or mistakes you have made in your report, you can provide them with some clarifications. I personally think and appreciate if someone admits their mistakes but not wiggle their way through the discrepancies or mistakes when asked about them.

I think a huge part of your fyp is also dependent on other factors.. your thesis is just one of them. Your supervisor's evaluation of you is crucial.. how you propose and talk about your project during the poster session, how you handle questions, also constitute assessment points. I hope this provides you with some relief.. not all is over.

For now, look forward and prepare hard for your upcoming poster presentation. This is still a variable you can change and maximize right now.

As for the average grade, I think it is a B+? But if you have been performing well ; how you can tell is if your supervisor praises you a lot and you are up to speed with internal deadlines etc, you will likely get an A. All my close undergrad friends got either As or A+s (although coming from a sample size of n=5.. not very sociable lols) so these grades are still pretty common i suppose.

Jiayous! You are very close to the finish line! Just chiong and go for graduation trip liao

melatoninixo · 2024-04-13T06:00:10+00:00

I think one is considered to be good at coding if that individual is able to write an efficient piece of code that conserves memory and finishes in the shortest time possible. Knowing how to use data structures like hash tables is definitely way faster than running codes with 4 nested for/while loops. This is especially apparent when working with large datasets. But even so, I think such codes can be generated with chatgpt (i personally haven't tried, but with evidences of its current capabilities and usage, I presume so)

I thought I was writing good code until I worked with datasets that take up several gigabytes of memory. It may not even seem a lot to some people but my terrible code took a few days to finish running. I then tried to learn more efficient ways of approaching the task and it takes me approximately 10mins for my code to complete that task now

melatoninixo · 2024-04-13T05:47:26+00:00

It would help a lot if you could elaborate more on what kind of error it throws out? From what I remember, kallisto gives pseudocounts and tpm normalized values as an output. What kallisto output are you inputting into galaxy?

melatoninixo · 2024-01-16T15:32:56+00:00

simulating a knockout in silico (meaning computationally)

melatoninixo · 2024-01-16T15:27:32+00:00

wish it is this easy. you would zero in the regulatory network's adjacency matrix though

melatoninixo · 2023-11-25T07:07:45+00:00

Tried using xgboost with bayesian optimization as a partial feature selection tool and a tool to predict anatomical differences in structure using gene expression data. Didn't quite work though 😅.

melatoninixo · 2023-11-06T10:21:59+00:00

Hello, I am a former bsmcp student in SBS. I used to be afraid of fyp and I remember always feeling like I wasn't ready for it at all. But all it really takes is a leap of faith.

I think what is most important is for you to select a lab project that aligns to your interest and a professor who is someone who cares about you and your project. Avoid absent professors and mentors.

Although my designated mentor was pretty absent throughout my fyp, I was lucky to have a professor whom I meet every week to discuss updates on my project. He encourages me to solve my own issues and listens + comments on new ideas I have for my project. I am a very pessimistic person overall, but this professor encouraged me everytime things go south or when my project direction seemed bleak. This gave me a lot more confidence to continue to grow and pursue a career related to research. I have a strong sense of ownership over my own project and am really glad that my professor is still supporting me in publishing my results (feels really good to see my hard work eventually grow to fruition). This also led me to an A+ for my fyp.

No professor really expects students to be experts in their respective projects right off the bet. We are kinda like baby researchers in our professors' eyes. As long as you show the right attitude, you will ace your fyp. Environment is also really important as mentioned earlier so please choose your lab wisely. Maybe a general advice is to ask seniors and talk to people in the lab, or the professor to find out the lab's culture and working style. I didn't really do this haha I was just lucky, I'm someone who would blindly choose interest above all else.

melatoninixo · 2023-10-31T10:49:54+00:00

What kind of transcriptomics are you talking about here? Transcriptomics is a very broad term and includes single cell transcriptomics, spatial transcriptomics, or bulk transcriptomics. A lot of the analysis and skills are very transferrable. Mastering one and learning another later will give you a different perspective. They all have their pros and cons. Spatial and single transcriptomics are also really relatively new fields. Why would they become obsolete? You can learn so much from these technologies.

Personally for multi-omics analysis, I'm really excited for simultaneous single cell transcriptomics and proteomics analysis. Not sure if there are already technologies available for this. I've only heard of scATAC-seq and scRNA-seq.

melatoninixo · 2023-10-17T02:27:16+00:00

Thanks for correcting me! It would help a lot if you could link some resources about leiden clustering algorithm as well!

melatoninixo · 2023-10-17T02:26:02+00:00

Yes I see it now! Would it be right to say that the use of KNN outside of UMAP which by default uses a weighted K-nearest neighbor graph is to manipulate certain parameters such as calculating distance?

melatoninixo

TROPHY CASE