20 درصد تخفیف بیشتر با کد تخفیف bio20
👍1

Channel
@pishgam_bioinformatics
On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Posts · Posts edited after publishing · Posts recorded as deleted · Citations · Telegram's recommendations · Cite this entry
20,901subscribers
-639 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of 10,000–31,623.
| Telegram ID | -1001083983579 |
|---|---|
| Type | Channel |
| Username | @pishgam_bioinformatics |
| Created | Between 1 September 2016 and 30 September 2017 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 6 August 2026 |
| Last confirmed live | 7 November 2026 |
| Measurements held | 61 |
| Confirmed unchanged | 1 time, most recently 7 November 2026 |
| On Telegram | t.me/pishgam_bioinformatics |
Education — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-FP8, prompt version 1) read this channel’s own recent posts on 10 September 2026 and assigned it the closest of 31 fixed categories, at 99% confidence. This is a model’s judgement about what the channel is likely to be about, not a fact this register measured the way a subscriber count or a view count is measured — it can be revised on a later pass, and it carries no weight anywhere else on this page. How this classification works, and the other topics.
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 7 Nov 2026, 04:48 | 20,901 | -14 |
| 6 Nov 2026, 03:09 | 20,915 | -4 |
| 5 Nov 2026, 00:06 | 20,919 | -4 |
| 2 Nov 2026, 21:59 | 20,923 | -2 |
| 2 Nov 2026, 00:46 | 20,925 | -8 |
| 1 Nov 2026, 00:01 | 20,933 | -3 |
| 31 Oct 2026, 00:16 | 20,936 | -5 |
| 29 Oct 2026, 22:31 | 20,941 | -10 |
| 28 Oct 2026, 20:01 | 20,951 | -6 |
| 27 Oct 2026, 20:06 | 20,957 | -6 |
| 26 Oct 2026, 19:36 | 20,963 | -1 |
| 25 Oct 2026, 17:39 | 20,964 | -13 |
| 24 Oct 2026, 14:04 | 20,977 | -14 |
| 23 Oct 2026, 12:09 | 20,991 | -2 |
| 22 Oct 2026, 12:09 | 20,993 | -5 |
| 20 Oct 2026, 01:08 | 20,998 | -14 |
| 19 Oct 2026, 04:36 | 21,012 | -5 |
| 18 Oct 2026, 06:10 | 21,017 | -1 |
| 17 Oct 2026, 08:11 | 21,018 | -1 |
| 16 Oct 2026, 09:50 | 21,019 | first reading |
72 posts held, back to 26 July 2026 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 69 pages of Telegram’s post history, 20 posts per page.
ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.
ER is defined industry-wide as (forwards + reactions + comments) ÷ views — note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate.
| Window | Rolling 30 days · latest post in window 6 November 2026 |
|---|---|
| Posts held | 72 (26 July 2026 – 6 November 2026) |
| Views total | 21,441 |
| Reactions total | 196 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 7 Nov 2026, 03:03 UTC |
Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.
Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.
Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.
Measured directly from 17 videos with a duration reading, out of the posts we hold for this channel — not this channel’s whole posting history, only the sample this register has actually read. An exact reading to the second, taken from the post itself rather than from Telegram’s own rounded chrome, so it carries no ≈ mark.
255 reactions across 36 posts, in 3 distinct kinds. The most used accounts for 61.2% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 156 | 61.2% | |
| 👍 | 98 | 38.4% | |
| 👎 | 1 | 0.392% |
No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.
Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.
Coverage. Reactions were read on 37 of the 72 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 257 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 72 most recent posts we hold, published 26 July 2026 to 6 November 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
Telegram’s own ‘edited’ marker: 10 of 21 posts held from the last 30 days (47.6%) carried it when this register last read them. The marker says a post was changed after publishing, not what it said before — this register holds the earlier wording only where an edit fell between two of its own reads (see the edit ledger and how it is measured).
20 درصد تخفیف بیشتر با کد تخفیف bio20
👍1
🧬 متاآنالیز را برای پایاننامه و مقالهات اصولی یاد بگیر! اگر برای انجام متاآنالیز در پروژه پژوهشی، پایاننامه یا مقاله خود به دنبال آموزشهای کاربردی و تخصصی هستید، مجموعه آموزشهای متاآنالیز پیشگامان بیوانفورماتیک را ببینید. 🎓 در این مجموعه ۶ آموزش تخصصی ارائه شده است: 🔹 آموزش متاآنالیز پلیمورفیسمهای ژنتیکی با R 🔹 آموزش متاآنالیز جهشهای ژنتیکی با RevMan و CMA 🔹 آموزش متاآنالیز بیومارکرها با R 🔹 آموزش متاآن…
❤2
🧬 ۵ اشتباه رایج در تحلیل دادههای بیوانفورماتیکی که ممکن است نتیجه پژوهش شما را تغییر دهند! گاهی مشکل یک پروژه تحقیقاتی، کمبود داده یا ابزارهای پیشرفته نیست؛ بلکه یک اشتباه کوچک در مراحل تحلیل میتواند کل نتیجه را تحت تأثیر قرار دهد. 🔬 ۱. نادیده گرفتن کنترل کیفیت دادهها (Quality Control) شروع تحلیل بدون بررسی کیفیت دادهها ممکن است باعث ورود اطلاعات نامعتبر به مراحل بعدی شود. 📊 ۲. انتخاب نادرست روش نرمالسازی (N…
❤4👍4
20 درصد تخفیف بیشتر آموزش های سایت با کد bio20 (علاوه بر تخفیف فعلی سایت ) به مدت محدود فعال شد .👇🏻👇🏻 https://pishgam-bio.ir/edu
❤5
🧬 از صفر تا مقاله با دادهی عمومی؛ نقشهی راه ۶ قدمی برای شروع یک پروژه پژوهشی برای انجام یک پروژه بیوانفورماتیکی و نوشتن مقاله، همیشه لازم نیست از ابتدا آزمایشهای پرهزینه انجام بدی. گاهی میتونی با استفاده از دادههای عمومی موجود، یک سؤال پژوهشی مناسب طراحی کنی، دادهها رو تحلیل کنی و نتایجت رو در مسیر آمادهسازی یک مقاله علمی پیش ببری. 🔬📊 اما این مسیر فقط به پیدا کردن یک دیتاست و رسم چند نمودار محدود نمیشه؛ هر …
❤7
🧬 نقشهی راه یادگیری بیوانفورماتیک از صفر میخوای بیوانفورماتیک یاد بگیری ولی نمیدونی از کجا شروع کنی؟ خیلیها وسط راه گم میشن چون ترتیب یادگیری رو نمیدونن. این مسیر ۶ قدمی رو دنبال کن 👇 1️⃣ زیستشناسی مولکولی پایه مفاهیمی مثل DNA، RNA، ژن، رونویسی، ترجمه و بیان ژن. بدون این پایه، خروجی نرمافزارها برات معنی نداره و نمیتونی نتیجه رو تفسیر کنی. 2️⃣ آشنایی با پایگاههای داده یاد بگیر داده رو کجا پیدا کنی: NCBI ب…
❤10
🧬 آموزش ساخت شبکه میانکنشی mRNA، lncRNA و miRNA در مطالعات بیوانفورماتیک و سرطان، بررسی ارتباطات میان mRNA، lncRNA و miRNA میتواند به شناسایی روابط تنظیمی و مولکولهای کلیدی در یک بیماری کمک کند. 📊 در این آموزش با فرآیند ساخت شبکه میانکنشی RNAها آشنا میشوید؛ مهارتی کاربردی برای انجام: 🔹 پروژههای بیوانفورماتیک 🔹 پایاننامه و مقاله 🔹 مطالعات سرطان و بیماریها 🔹 تحلیل RNAها و روابط تنظیمی 🔹 شناسایی مولکولهای کل…
❤6
#فیلم_آموزشی_به_زبان_فارسی 🧬 فیلم آموزش پایگاه داده TarBase برای بررسی میانکنش ژنها و میکروRNAها 🔹 توضیح: پایگاه داده TarBase منبعی تخصصی برای شناسایی میانکنش بین میکروRNAها (miRNA) و ژنهای هدف آنهاست. در این پایگاه میتوانید میانکنشهای تأییدشده با روشهای تجربی را بررسی کرده و از شواهد علمی آنها برای تحلیل شبکههای تنظیم بیان ژن استفاده کنید. ✅ مزیتها: • پوشش گونههای مختلف، از جمله انسان • ارائه میانکن…
❤7
🧬 برای شروع بیوانفورماتیک از کجا شروع کنیم؟ اگر دانشجوی ژنتیک، زیستشناسی یا علوم پزشکی هستی و میخوای وارد دنیای بیوانفورماتیک بشی، این مسیر میتونه نقطه شروع خوبی باشه 👇 🔹 قدم اول: یادگیری مقدماتی زبان برنامهنویسی R 🔹 قدم دوم: آشنایی با پایگاههای داده زیستی مثل GEO و TCGA 🔹 قدم سوم: یادگیری تحلیل دادههای ژنی 🔹 قدم چهارم: تفسیر نتایج و استفاده از اونها در پژوهش و پایاننامه 📌 لازم نیست از همون اول همه ابزارها…
❤13👍2
🎁 هدیه ویژه اعضای کانال پیشگامان بیوانفورماتیک (20+20) علاوه بر ۲۰٪ تخفیف فعلی سایت، اعضای کانال میتونن با کد اختصاصی زیر، ۲۰٪ تخفیف اضافه دریافت کنن. 🔑 کد تخفیف اختصاصی: bio20 ⏳ به مدت محدود 🧬 از آموزشهای تخصصی بیوانفورماتیک و تحلیل داده تا متاآنالیز، تکنیکهای آزمایشگاهی و ابزارهای پژوهشی؛ آموزش موردنیازت رو انتخاب کن و با تخفیف ویژه یادگیری رو شروع کن. 🔗 مشاهده آموزشها و ثبتنام: https://pishgam-bio.ir/ed…
❤6
🧬 ۶ سایت رایگان که تحلیل ژنهات رو سادهتر میکنه خیلی از دانشجوها ماهها وقت میذارن برای کاری که با یک ابزار رایگان در چند دقیقه انجام میشه 🔹 GEO2R: پیدا کردن ژنهای تغییرکرده از دادههای عمومی 🔹 GEPIA2: مقایسهی بیان ژن در نمونهی سرطانی و سالم 🔹 UALCAN: بیان ژن و ارتباطش با بقای بیمار 🔹 Enrichr: تحلیل عملکردی و غنیسازی مسیرها 🔹 Primer-BLAST: طراحی پرایمر و بررسی اختصاصی بودن 🔹 NCBI Gene: اطلاعات پایهی هر ژن …
❤7
🧬 لیست ژنهای تغییرکرده رو درآوردی، مرحلهی بعدی چیه؟ شناسایی ژنهای متفاوت فقط قدم اوله. سؤال اصلی اینه که این ژنها در چه فرایندها و مسیرهای زیستی نقش دارن. با ابزار DAVID میتونی: 🔹 تحلیل عملکردی ژنها رو انجام بدی 🔹 غنیسازی مسیرهای زیستی (Enrichment) رو بررسی کنی 🔹 نتایج پژوهشت رو بهتر تفسیر کنی و در بخش بحث پایاننامه یا مقاله استفاده کنی 🎓 آموزش تخصصی DAVID از پیشگامان بیوانفورماتیک 📌 مناسب دانشجویان و پژوه…
❤5
Showing the 12 most recent of 72 posts we hold for @pishgam_bioinformatics. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
@pishgam_bioinformatics edited 1 post after it first published — the same permalink now carries different wording than the one this register originally read, caught because our own crawl held a copy of the earlier text.
An edit is not deception. Typo fixes, price updates and corrections look exactly like this too — this register can tell you the wording changed and when, not why. How this is measured.
We recorded 3 posts on @pishgam_bioinformatics as deleted — posts this register held that were missing from the channel’s public page on two separate reads, taken from different network addresses at least an hour apart.
A deletion is not wrongdoing. This register can see that a post it held is no longer on the channel’s public page, and when — not why. Expired ad placements, reposts, rotating links and corrections all look the same. This is a count of recorded detections, not an exact total: it misses deletions we could not see, because only a post we held and later re-read inside the page can be seen to go, and it may include a small number of wrong records. How this is measured.
Named by 1 registered channel — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it.
Named by
Channels on the register whose posts name this channel's handle.
Names
Channels on the register whose handles appear in this channel's posts.
A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.
Telegram’s own answer, not this register’s. When this register asks Telegram’s API what is similar to this channel, this is the list it returns, in the exact order Telegram returns it — never re-sorted by subscribers or by anything else this register measures. The relationship, and the order, are Telegram’s; we record them and date them, and make no claim of our own about which of these channels actually resemble this one.
Read from Telegram’s recommendation API, most recently 24 September 2026. Telegram holds a list like this for a small and growing share of the register — how this is measured, and why most channel pages show nothing here.
The reverse of the list above, and a different kind of signal. This does not require this channel to have ever been asked about directly — each row below is a channel we DID ask Telegram about, whose Telegram-generated list happened to include this one. A channel can appear here with an empty list above it, because being named by someone else’s query is independent of having been queried itself.
This channel appears in 6 seed channels' Telegram-generated recommendation lists in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 7 November 2026 — this entry's latest reading, not the date you are reading this.
“آموزش بیوانفورماتیک کاربردی و آنالیز داده” (@pishgam_bioinformatics), 20,901 subscribers as measured 7 November 2026. Telegram Register, tgregister.com/channel/pishgam_bioinformatics.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.