Channel
@eduopenbio
On this record: Growth · Engagement · Reactions · Advertising · Posts · Polls · Citations · Cite this entry
2,670subscribers
+0 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of 1,000–3,162.
| Telegram ID | -1002168681658 |
|---|---|
| Type | Channel |
| Username | @eduopenbio |
| Created | Between 1 June 2024 and 30 September 2024— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 7 August 2026 |
| Last confirmed live | 11 August 2026 |
| Measurements held | 2 |
| Confirmed unchanged | 2 times, most recently 11 August 2026 |
| On Telegram | t.me/eduopenbio |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 8 Aug 2026, 00:57 | 2,670 | no change |
| 7 Aug 2026, 01:32 | 2,670 | first reading |
10 posts held, back to 24 October 2025 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 2 pagesof Telegram’s post history, 20 posts per page.
Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 10 posts for this entry, the most recent from 7 June 2026. An engagement rate over an empty window would be a number about nothing.
84 reactions across 9 posts, in 7 distinct kinds. The most used accounts for 45.2% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 38 | 45.2% | |
| 🔥 | 20 | 23.8% | |
| 👍 | 16 | 19.0% | |
| custom 5415644402351627247 | 7 | 8.33% | |
| ✍ | 1 | 1.19% | |
| 😍 | 1 | 1.19% | |
| 🤔 | 1 | 1.19% |
Custom emoji. One row above is a Telegram custom emoji, which the public preview renders as an element carrying only a numeric id — no character, and no image we can reach. The id is printed as-is rather than substituted with a look-alike glyph, because a stand-in would be our invention showing where a measurement should be. The count beside it isTelegram’s.
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 9 of the 10 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 84reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 10 most recent posts we hold, published 24 October 2025 to 7 June 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.
An ad marker, not a judgement about a post. A post is counted here because it carries one of two explicit markings: an erid token, which Russian law has required on paid placements since 2022 and which is issued against a specific advertising contract, or a #реклама / #ad hashtag in the body, which is the channel declaring it itself. The first is documentary; the second is a self-declaration and is weaker. No classifier reads the text and decides — nothing on this site guesses that a post is an advertisement.
This is a floor, and it can only ever be a floor.A channel that runs paid placements without marking them produces no marker for us to count, and an unmarked ad is indistinguishable from an ordinary post on the public surface. The ad load above therefore means “the share of posts that declared themselves”, never “the share of posts that were paid for”. A low figure is not evidence of a channel that runs few ads.
Both figures are medians, and no ratio between them is published. Each is a view reading that actually occurred on a post, picked by percentile_disc rather than averaged, so one viral post cannot move it and no interpolated value is invented between two readings. The sample on one side is under five posts, which is too thin to compare. The two figures are shown side by side with the count behind each, and deliberately not divided into a headline like “ads get x% fewer views” — an arithmetic that is easy to print and, at this sample size, means nothing.
| erid | Posts | First seen | Last seen |
|---|---|---|---|
| 2Vtzqw748FQ | 1 | 25 October 2025 | 25 October 2025 |
| 2Vtzqwdigc6 | 1 | 25 October 2025 | 25 October 2025 |
A token repeated across several posts is one advertising contract placed more than once, which is what the identifier is for. The strings are reproduced exactly as they appeared in the post or in its click-through URL and are not validated against any registry — we record the marker a channel published, and whether it resolves to a real contract is a question for the register that issued it.
Measured over the 10 most recent posts we hold, published 24 October 2025 to 7 June 2026. Views are the latest single reading held for each post, and any reading at or above 1,000 is rounded by Telegram to three significant figures.
🧬 10 июня, 19:00 мск — уже скоро Через несколько дней три лида из BostonGene выходят в эфир и честно рассказывают, как выглядит найм в биоинформатике с другой стороны стола. Первый час — живой разговор про red flags, резюме и то, что нанимающий менеджер думает, но обычно не говорит. Второй — мок-собеседования вживую с разбором 3–4 участников. 🔥 Ещё можно попасть на разбор Если хотите, чтобы ваше резюме разобрали п…
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Мы позвали настоящих экспертов-технарей поговорить с вами про собеседование и найм Мы тут подумали — а что если провести открытый семинар по карьере в новом формате? Не просто «послушайте лекцию», а реально разобраться, как устроено собеседование в биоинформатическую компанию — фактурненько, от начала до конца, глазами тех, кто нанимает. Собрали сразу трёх лидов из BostonGene — с разных сторон биоинформатики: Алекс…
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💡 Серия спринтов OpenBio У нас много новеньких на канале (👋) и перед стартом курса «Машинное обучение в биологии и биомедицине» мы решили напомнить о трех спринтах, которые стали для многих первой точкой входа в ML. Если вы проходили их раньше — самое время освежить материалы, а если нет — это отличный шанс быстрее втянуться в курс. 🔥 ML Bootcamp 🌤🌥☁️ Пятидневный интенсив по основам машинного о бучения.Мы говорили …
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Начинаем через 10 минут! Первый вебинар нового формата Open Code — уже на старте. Сегодня вместе с Ильей Воронцовым разбираем batch-эффект: ➡️ Визуализируем данные, ➡️ Применим PCA, ➡️ Оценим вклад batch-эффекта ➡️ Посмотрим, как меняются результаты после коррекции. Если еще не с нами — подключайтесь сейчас! Успеете попасть в прямой эфир и задать вопросы 👉 [ссылка на подключение] Заполняйте анкету что бы получи…
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📲 Мы начинаем новый формат! Open Code — никаких слайдов, только данные и дискуссия, только live-коддинг 🤟 ❕ Тема сегодня — batch-эффект в экспрессионных данных. Он проявляется тогда, когда клетки группируются не по типам, а по тому, из какого образца они пришли. Из-за этого искажаются результаты кластеризации, определения типов клеток и реконструкции траекторий. Но не всякая разница — batch-эффект. Иногда отличия…
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💡 Истории наших выпускников Мы вновь делимся нашей главной гордостью — выпускниками! Нам особенно приятно видеть, что выпускники OpenBio не останавливаются на достигнутом: поступают в магистратуры, защищают дипломные работы, развиваются в биоинформатике и смежных областях. Для нас это подтверждение тогочто наш курс стал тем самым кирпичиком в фундаменте знаний, на который можно уверенно опираться дальше. 🔺Валерия Л…
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❔Open Code: разберемся в данных вместе 28 октября в 19:00 — приглашаем на открытый вебинар с лайв-кодингом в прямом эфире. На этот раз мы посмотрим на batch-эффект в экспрессионных данных и разберем, как его увидеть и скорректировать с помощью классических методов машинного обучения. Вместе: 🔺Визуализируем данные и посмотрим, как проявляется батч-эффект; 🔺Применим PCA для уменьшения размерности; 🔺Используем лин…
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Анонс возможности попасть на первый модуль основной программы курса "Задачи классического ML для биологии и биомедицины". 📲Чтобы уверенно ориентироваться на любой карте, нужно научиться прокладывать маршрут самостоятельно: выбирать правильную дорогу, обходить препятствия и добираться до цели. Именно для этого мы запускаем отдельный доступ к первому модулю нашей основной программы «Классические методы ML» - 17 часов …
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Поздравляем тех, кто дошел с нами до финала спринта ❤️ Наш экспресс-маршрут по миру классического машинного обучения окончен. В течение пять дней мы знакомили вас с логикой регрессий, мощью SVM, интуитивностью деревьев и скоростью Наивного Байеса. Надеемся, что нам удалось показать вам "карту местности": где находятся ключевые «города» и «реки» в области классических методов ML. ↗️ Спасибо тем, кто не просто читал…
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Перед вами три графика: confusion matrix, ROC-кривая и распределение вероятностей.Попробуйте по ним понять, насколько уверенно модель делает предсказания и где остается зона неопределенности.
The shares total 182%, above 100: this poll accepts more than one answer per voter. No per-option vote count is published, so the number of voters who chose each option is not derivable and is not shown.
Showing the 10 most recent of 10 posts we hold for @eduopenbio. 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.
The poll we hold for this entry, as Telegram rendered it when we read the post. A poll’s figures keep moving after that, so each one is dated.
Перед вами три графика: confusion matrix, ROC-кривая и распределение вероятностей.Попробуйте по ним понять, насколько уверенно модель делает предсказания и где остается зона неопределенности.
The shares total 182%, above 100: this poll accepts more than one answer per voter. No per-option vote count is published, so the number of voters who chose each option is not derivable and is not shown.
Percentages only — there are no per-option vote counts here, because Telegram publishes none.The public post preview gives each option’s share and a single voter total, and nothing else. Multiplying one by the other would produce a per-option tally that looks measured and is not: the shares are rounded to whole numbers before we ever see them. We print what was published and leave the column that does not exist empty.
The shares need not add up to 100.Rounding alone puts many polls at 99 or 101. A poll that allows more than one answer per voter runs well past 100 by design, and several here do. The bars are drawn against a fixed 100% track at each option’s own percentage rather than normalised to the total, so a poll that exceeds it shows that it does instead of being quietly rescaled.
Read from the 10 most recent posts we hold, published 24 October 2025 to 7 June 2026. Telegram labels each poll by kind — an anonymous poll, a quiz, a closed set of final results — and that label is reproduced rather than paraphrased.
Citation-graph rank — 612,954 of 1,160,990entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.
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.
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.
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 8 August 2026 — this entry's latest reading, not the date you are reading this.
“Машинное обучение в биологии и биомедицине | OpenBio.Edu” (@eduopenbio), 2,670 subscribers as measured 8 August 2026. Telegram Register, tgregister.com/channel/eduopenbio.
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.