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- <template>
- <div class="page-box">
- <div class="swiper-box">
- <div class="base">
- <div class="img">
- <img src="../../assets/home_0.png" alt="" />
- </div>
- <div class="text-box">
- <div class="title">What is DeepBIO?</div>
- <div class="content">
- DeepBIO is a user-friendly interpretable deep-learning platform for
- biological sequence functional analysis, such as sequence-level
- functional prediction and base-wise functional annotation. As for
- the sequence-level functional prediction, DeepBIO integrates a total
- of 42 state-of-the-art deep-learning approaches, and enables a
- one-stop-shop web service for researchers to develop a new deep
- learning architecture to answer any biological question. To be
- specific, given any biological sequence data, DeepBIO allows to
- train and evaluate deep-learning models, conduct model comparison
- and optimization, and provide a series of visualization analyses. As
- for the base-wise functional site annotation, DeepBIO offers
- well-pretrained deep-learning architectures for over 20 functional
- site annotation tasks, supports comprehensive interpretations and
- graphical visualizations of identified sites, and validates the
- reliability of the identified sites by conservation motif analysis.
- </div>
- </div>
- </div>
- <div class="base">
- <div class="text-box">
- <div class="title">What is DeepBIO?</div>
- <div class="content">
- DeepBIO is a user-friendly interpretable deep-learning platform for
- biological sequence functional analysis, such as sequence-level
- functional prediction and base-wise functional annotation. As for
- the sequence-level functional prediction, DeepBIO integrates a total
- of 42 state-of-the-art deep-learning approaches, and enables a
- one-stop-shop web service for researchers to develop a new deep
- learning architecture to answer any biological question. To be
- specific, given any biological sequence data, DeepBIO allows to
- train and evaluate deep-learning models, conduct model comparison
- and optimization, and provide a series of visualization analyses. As
- for the base-wise functional site annotation, DeepBIO offers
- well-pretrained deep-learning architectures for over 20 functional
- site annotation tasks, supports comprehensive interpretations and
- graphical visualizations of identified sites, and validates the
- reliability of the identified sites by conservation motif analysis.
- </div>
- </div>
- <div class="img">
- <img src="../../assets/home_1.png" alt="" />
- </div>
- </div>
- <div class="base">
- <div class="img">
- <img src="../../assets/home_2.png" alt="" />
- </div>
- <div class="text-box">
- <div class="title">What is DeepBIO?</div>
- <div class="content">
- DeepBIO is a user-friendly interpretable deep-learning platform for
- biological sequence functional analysis, such as sequence-level
- functional prediction and base-wise functional annotation. As for
- the sequence-level functional prediction, DeepBIO integrates a total
- of 42 state-of-the-art deep-learning approaches, and enables a
- one-stop-shop web service for researchers to develop a new deep
- learning architecture to answer any biological question. To be
- specific, given any biological sequence data, DeepBIO allows to
- train and evaluate deep-learning models, conduct model comparison
- and optimization, and provide a series of visualization analyses. As
- for the base-wise functional site annotation, DeepBIO offers
- well-pretrained deep-learning architectures for over 20 functional
- site annotation tasks, supports comprehensive interpretations and
- graphical visualizations of identified sites, and validates the
- reliability of the identified sites by conservation motif analysis.
- </div>
- </div>
- </div>
- </div>
- </div>
- </template>
- <script>
- export default {
- name: "home",
- components: {},
- data() {
- return {
- // modules: [Pagination],
- swiperOption: {
- slidesPerView: 1,
- spaceBetween: 30,
- direction: "vertical",
- pagination: {
- el: ".swiper-pagination",
- clickable: true,
- },
- },
- };
- },
- watch: {
- tags() {
- console.log(119);
- },
- },
- mounted() {
- console.log(this.$route.query);
- if (this.$route.query.type === "reload") {
- this.$router.replace({
- name: "home",
- });
- window.location.reload();
- }
- },
- methods: {
- onSlideChange() {
- console.log("111");
- },
- scroll() {
- // 获取当前 swiper-wrapper 元素的 left 值,即偏移量
- const wrapperLeft = this.$refs.mySwiper.wrapperEl.style.left;
- // 将 left 值转换为数值
- const left = parseInt(wrapperLeft);
- // 计算当前滚动位置对应的 slide 的索引值
- const currentIndex = -left / this.$refs.mySwiper.slidesGrid[0];
- // 切换到下一个或上一个 slide
- if (this.$refs.mySwiper.progress > 0.5) {
- this.$refs.mySwiper.slideNext();
- } else {
- this.$refs.mySwiper.slidePrev();
- }
- },
- },
- };
- </script>
- <style lang="less" scoped>
- .page-box {
- .swiper-box {
- height: calc(100vh - 67px);
- //overflow: hidden;
- //position: relative;
- .base {
- width: 100%;
- //height: 100%;
- height: calc(80vh - 67px);
- padding: 20px 50px;
- box-sizing: border-box;
- display: flex;
- //flex-direction: row;
- //align-items: center;
- .img {
- box-sizing: border-box;
- display: flex;
- align-items: center;
- justify-content: center;
- width: 35%;
- img {
- width: 100%;
- display: block;
- }
- }
- .text-box {
- box-sizing: border-box;
- flex: 1;
- padding: 40px;
- text-align: left;
- display: flex;
- flex-direction: column;
- justify-content: center;
- .title {
- box-sizing: border-box;
- margin: 20px 0;
- //margin: 0px;
- font-family: Roboto, Helvetica, Arial, sans-serif;
- font-size: 2.125rem;
- line-height: 1.235;
- letter-spacing: 0.00735em;
- font-weight: 700;
- }
- .content {
- box-sizing: border-box;
- width: 100%;
- }
- }
- .text {
- flex: 1;
- display: flex;
- align-items: center;
- }
- }
- .other-box {
- margin-top: 1px;
- }
- }
- }
- </style>
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